Ke Ma, Jiaqi Tang, Bin Guo, Xueting Han, Ruonan Xu, Qingfeng He, Ziheng Wang, Xu Wang, Qifeng Chen, Zhiwen Yu, Yunhao Liu. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

This paper presents E-Cube, a framework designed to enhance the efficiency of mobile video streaming systems. Our core insight is that content correlations among frames, which are critical for video streaming but challenging to extract in dynamic scenes, are often already available as intermediate outputs from other mobile computing subsystems. By adopting a cross-subsystem design, E-Cube repurposes these intermediate results obtained from event cameras to reduce computation, energy consumption, and bandwidth usage of existing video streaming systems, while simultaneously improving video quality. We implement E-Cube on a drone-embedded chip through software-hardware co-design, and plugged E-Cube into three prevalent drone video streaming systems for industrial inspection. Evaluations in one of the world's largest oil fields and on public datasets demonstrate E-Cube can save over 35% network bandwidth overhead and reduce drone video streaming energy consumption by over 12% for H.265 and AV1, and 47% for advanced H.266, all while achieving improved video quality.

As drone-based applications proliferate, paramount contactless sensing of airborne drones from the ground becomes indispensable. This work demonstrates concentrating on propeller rotational speed will substantially improve drone sensing performance and proposes an event-camera-based solution, EventPro. EventPro features two components: Count Every Rotation achieves accurate, real-time propeller speed estimation by mitigating ultra-high sensitivity of event cameras to environmental noise. Every Rotation Counts leverages these speeds to infer both internal and external drone dynamics. Extensive evaluations in real-world drone delivery scenarios show that EventPro achieves a sensing latency of 3 ms and a rotational speed estimation error of merely 0.23%. Additionally, EventPro infers drone flight commands with 96.5% precision and improves drone tracking accuracy by over 22% when combined with other sensing modalities. Demo: https://eventpro25.github.io/EventPro/.

This paper addresses the challenges of achieving optimal quality of information (QoI) in non-dedicated vehicular mobile crowdsensing (NVMCS) system, where vehicles not originally designed for sensing are leveraged to collect real-time data as they traverse urban environments. These challenges are exacerbated by the interrelated issues of sensing coverage, sensing reliability, and the inherently dynamic nature of participating vehicles. To tackle these challenges, we propose QUIDS, a QUality-informed Incentive-driven multi-agent Dispatching System, which ensures high sensing coverage and sensing reliability under budget constraints in NVMCS systems. QUIDS improves QoI by introducing a novel metric, Aggregated Sensing Quality (ASQ), designed to quantitatively capture the concept of QoI by integrating both sensing coverage and sensing reliability. Moreover, we develop a Mutually Assisted Belief-aware Vehicle Dispatching algorithm that estimates sensing reliability and allocates monetary incentives under uncertain vehicle conditions, thereby further improving ASQ. Evaluation using real-world data collected from a deployed NVMCS system in a metropolitan area demonstrates the effectiveness of QUIDS. The ASQ metric shows a 38% improvement over non-dispatching scenarios and a 10% enhancement over state-of-the-art methods. Additionally, QUIDS reduces reconstruction map errors by 39–74% across various reconstruction algorithms, validating its efficacy in improving QoI within NVMCS systems. Addressing the often-overlooked issue of sensing reliability in existing studies, the QUIDS system leverages non-dedicated vehicles and incorporates a quality-informed incentive-driven dispatching system to jointly optimize sensing coverage and sensing reliability. This enables low-cost, high-quality, and scalable urban environmental monitoring without the need for dedicated sensing infrastructure, and makes the system applicable to diverse smart-city scenarios such as traffic monitoring and environmental sensing.

For precise, efficient, and safe drone landings, ground platforms should real-time, accurately locate descending drones and guide them to designated spots. While mmWave sensing combined with cameras improves localization accuracy, lower sampling frequency of traditional frame cameras compared to mmWave radar creates bottlenecks in system throughput. In this work, we upgrade traditional frame camera with event camera, a novel sensor that harmonizes in sampling frequency with mmWave radar within ground platform setup, and introduce mmE-Loc, a high-precision, low-latency ground localization system designed for precise drone landings. To fully exploit thetemporal consistencyandspatial complementaritybetween these two modalities, we propose two innovative modules:(i)the Consistency-instructed Collaborative Tracking module, which further leverages the drone's physical knowledge of periodic micro-motions and structure for accurate measurements extraction, and(ii)the Graph-informed Adaptive Joint Optimization module, which integrates drone motion information for efficient sensor fusion and drone localization. Extensive experiments (30+ hours) demonstrate that mmE-Loc attains 0.083$m$localization accuracy and 5.12$ms$end-to-end latency, outperforming four state-of-the-art methods by over 48% and 62%, respectively.

A heterogeneous micro aerial vehicles (MAV) swarm consists of resource-intensive but expensive advanced MAVs (AMAVs) and resource-limited but cost-effective basic MAVs (BMAVs), offering opportunities in diverse fields. Accurate and real-time localization is crucial for MAV swarms, but current practices lack a low-cost, high-precision, and real-time solution, especially for lightweight BMAVs. We find an opportunity to accomplish the task by transforming AMAVs into mobile localization infrastructures for BMAVs. However, translating this insight into a practical system is challenging due to issues in estimating locations with diverse and unknown localization errors of BMAVs, and allocating resources of AMAVs considering interconnected influential factors. This work introduces TransformLoc, a new framework that transforms AMAVs into mobile localization infrastructures, specifically designed for low-cost and resource-constrained BMAVs. We design an error-aware joint location estimation model to perform intermittent joint estimation for BMAVs and introduce a similarity-instructed adaptive grouping-scheduling strategy to allocate resources of AMAVs dynamically. TransformLoc achieves a collaborative, adaptive, and cost-effective localization system suitable for large-scale heterogeneous MAV swarms. We implement and validate TransformLoc on industrial drones. Results show it outperforms all baselines by up to 68% in localization performance, improving navigation success rates by 60%. Extensive robustness and ablation experiments further highlight superiority of its design.

Today's mobile applications oftentimes need to detect user-defined complex patterns (e.g., the mysterious “phantom traffic jam”) over data streams to support decision making. It is achieved by continuously creating candidate instances that have partially matched a pattern, and meanwhile aggregating common instances (across patterns) for efficiency enhancement. Existing aggregation approaches are taken in a straightforward or intuitive manner, incurring an exponential solution space and thus having to be executed offline. This paper explores how to significantly accelerate aggregation so as to make pattern detection online executable, even suited to the emerging serverless runtime that involves complicated state synchronizations among distributed cloud functions. By comprehensively investigating a wide variety of mobile data streams, we note the existence of a latent hierarchical cluster structure among complex patterns (in terms of their instance similarities), which can be utilized to quickly aggregate common instances without going through the exponential solution space. To extract the latent information, we devise a content-aware structural entropy minimization algorithm to properly determine intra-cluster patterns, together with a lightweight differential compensation mechanism to maintain those inter-cluster “residual” relations among patterns. Evaluations on real-world vehicle and sensor network data streams illustrate that the resulting approach, dubbed PatternInsight, saves the aggregation time by 10× to 50× and reduces the instance size by 40%.

AI is making mobile applications increasingly cooler, but also introduces serious privacy risks due to the extensive user data collection. Federated learning (FL), as a privacy-preserving machine learning paradigm, enables mobile devices to collaboratively learn a shared prediction model while keeping all training data on devices. However, a key obstacle towards practical cross-device FL training is the huge energy consumption, especially for lightweight mobile devices. Prior literature mostly optimizes the convergence speed and network communication cost. In this work, we first perform the experimental analysis of improving FL performance through low-precision training with energy-friendly Digital Signal Processor (DSP) on mobile devices. Then, we demonstrate that directly integrating the state-of-the-art INT8 (8-bit integer) training algorithm and classic FL protocols will significantly degrade the model accuracy. Finally, we propose a novel FL protocol, namelyQ-FedUpdate, incorporates two critical techniques: error-compensated aggregation and pipelined batch quantization. The former can ensure the tiny model updates be accumulated and take effects, and the latter can improve the DSP cache hit rate to reduce the context switching. Extensive experiments show that,Q-FedUpdatecan effectively reduce the on-device energy consumption by 21×, and accelerate the FL convergence by 6.1× with only 2% accuracy loss.

Bluetooth Low Energy (BLE) is a prevalent technology in various applications due to its low power consumption and wide device compatibility. Despite its numerous advantages, the encryption methods of BLE often expose devices to potential attacks. To fortify security, we investigate the application of Physical-layer Key Generation (PKG), a promising technology that enables devices to generate a shared secret key from their shared physical environment. Although extensively investigated, PKG is generally discussed in the context of Wi-Fi, and existing solutions for BLE demonstrate significantly lower performance. To bridge this gap, we propose a distinctive approach that capitalizes on the inherent characteristics of BLE to facilitate efficient PKG. We utilize the constant tone extension within BLE protocols to extract comprehensive physical layer information and introduce an innovative method that employs Legendre polynomial quantization for PKG. This method facilitates the exchange of secret keys with a high key matching rate and a high key generation rate. The efficacy of our approach is validated through extensive experiments on a software-defined radio platform, underscoring its potential to enhance security in the rapidly expanding field of BLE applications. A pilot study on commercial off-the-shelf BLE devices further validates the system's practicality, revealing important trade-offs between performance and hardware constraints in real-world deployments.

Emerging heavy-load mobile apps like UHD video and AR/VR access diverse high-throughput hardware devices, e.g., video codecs and cameras. However, today's mobile emulators exhibit poor performance when emulating these devices. We pinpoint the major reason to be the discrepancy between the guest's (system-on-chip) and host's (PC or cloud server) memory architectures for these devices, which makes the shared virtual memory (SVM) architecture of mobile emulators highly inefficient. To address this, we introduce vSoC, the first virtual mobile SoC featuring aunifiedSVM framework that enables efficient and secure data sharing among virtual devices, as well as anintelligentprefetch engine that effectively eliminates the vast majority of coherence maintenance overhead. While vSoC addresses runtime performance issues, app developers face a severe debugging challenge due to the inability of traditional tools to capture complete system states. Therefore, we devise an adaptive VM (virtual machine) snapshot-based approach that dynamically selects the optimal resource loading strategy to make vSoC debug-friendly. Compared to state-of-the-art emulators, vSoC brings 1.8–9.0$\times$frame rates, 35%-62% lower motion-tophoton latency, and 6.5–14.4$\times$bug reproduction rates for heavyload apps. It is applicable to a variety of scenarios like end-user high-performance emulation and cloud/web-based rendering.

Gas source localization is pivotal for the rapid mitigation of gas leakage disasters, where mobile robots emerge as a promising solution. However, existing methods predominantly schedule robots’ movements based on reactive stimuli or simplified gas plume models. These approaches typically excel in idealized, simulated environments but fall short in real-world gas environments characterized by their patchy distribution. In this work, we introduce SniffySquad , a multi-robot olfaction-based system designed to address the inherent patchiness in gas source localization. SniffySquad incorporates a patchiness-aware active sensing approach that enhances the quality of data collection and estimation. Moreover, it features an innovative collaborative role adaptation strategy to boost the efficiency of source-seeking endeavors. Extensive evaluations demonstrate that our system achieves an increase in the success rate by \(20\%+\) and an improvement in path efficiency by \(30\%+\) , outperforming state-of-the-art gas source localization solutions.

Despite the growing integration of deep models into mobile terminals, the accuracy of these models declines significantly due to various deployment interferences. Test-time adaptation (TTA) has emerged to improve the performance of deep models by adapting them to unlabeled target data online. Yet, the significant memory cost, particularly in resource-constrained terminals, impedes the effective deployment of most backward-propagation-based TTA methods. To tackle memory constraints, we introduce Surgeon, a method that substantially reduces memory cost while preserving comparable accuracy improvements during fully test-time adaptation (FTTA) without relying on specific network architectures or modifications to the original training procedure. Specifically, we propose a novel dynamic activation sparsity strategy that directly prunes activations at layer-specific dynamic ratios during adaptation, allowing for flexible control of learning ability and memory cost in a data-sensitive manner. Among this, two metrics, Gradient Importance and Layer Activation Memory, are considered to determine the layer-wise pruning ratios, reflecting accuracy contribution and memory efficiency, respectively. Experimentally, our method surpasses the baselines by not only reducing memory usage but also achieving superior accuracy, delivering SOTA performance across diverse datasets, architectures, and tasks.

Existing inter-domain routing simulators often suffer from low simulation accuracy, slow processing speeds, and high memory consumption, hindering their ability to perform large-scale Internet simulations. In this paper, we present InternetSim, a multi-threaded simulator for Internet-scale inter-domain routing. InternetSim enables incremental computation to deal with policy changes by some ASes, thus significantly accelerating multi-iteration context-continuous routing simulations. The memory efficiency is also improved by designing compact data structures for routing tables and out-of-memory errors are prevented by offloading the data to disk when necessary. Our simulator can complete an iteration of Internet-scale simulation within 13 hours (21× speedup compared to C-BGP) and can complete an ''incremental computation'' iteration within 15 seconds. The memory requirement is only 45% of C-BGP (without offloading) and the simulator can work well for Internet-scale simulations on a server with only 64GB if offloading is always enabled.

The swarm of Unmanned Aerial Vehicles (UAVs) has garnered considerable attention, particularly in scenarios with critical situations or limited communication infrastructure. In such cases, Mission UAVs (MUs) often be deployed in clusters to provide communication services. However, the high mobility of MUs always leads to frequent changes in swarm network topology, posing challenges for the network performance. To tackle this issue, we deploy additional Relay UAVs (RUs) with a two-stage adaptive learning-based approach. In the first stage, we employ a Delaunay triangulation-based algorithm to optimize RUs’ position and construct the initial topology. In the second stage, we implement a centralized learning and decentralized execution (CTDE) reinforcement learning framework to ensure continuous network connectivity and optimize performance throughout the task cycle. To further enhance cooperation among RUs, we introduce a sequential update technique coupled with an entropy regularization term during the policy network updates. Finally, extensive simulation results demonstrate the effectiveness of our proposed algorithms.

Large language models (LLMs) have emerged as a transformative advancement in artificial intelligence (AI). To fully leverage their potential, Docker containers, serving as a lightweight, portable, and isolated framework, facilitate the seamless deployment of LLM-based applications. However, the deployment of containerized AI microservices faces challenges such as heavy network loads, delayed image loading, and redundancy. In this paper, we introduce MetaPipe, an innovative incremental deployment approach for containerized AI microservices in edge cloud environments. MetaPipe aims to optimize startup times through a dynamic workflow that incorporates proactive layer pre-fetching and reinforcement layer re-scheduling. The proactive pre-fetching reduces service deployment time through layer caching prediction and pre-scheduling before requests arrive, while the reinforcement re-scheduling addresses inaccuracies by dynamically adjusting layer scheduling strategies after requests arrive. Extensive experiments on realworld datasets show that MetaPipe significantly outperforms traditional methods, achieving 83.58% reduction in initialization startup time and 85.58% reduction in cold startup time. These results highlight its effectiveness in enhancing the performance of AI microservices deployment within edge cloud environments.

Neural enhancement through super-resolution (SR) deep neural networks (DNNs) opens up new possibilities for ultra-high-definition (UHD) live streaming. Yet, the heavy SR DNN inference overhead leads to severe deployment challenges. To reduce the overhead, existing systems propose to apply DNN-based SR only on carefully selected anchor frames while upscaling non-anchor frames via the lightweight reusing-based SR approach. However, frame-level scheduling is coarse-grained and fails to deliver optimal efficiency. In this work, we propose Palantír, the first neural-enhanced UHD live streaming system with fine-grained patch-level scheduling.

Parallelizing passive Radio Frequency Identification (RFID) reading is an arguably crucial, yet unsolved challenge in modern IoT applications. Existing approaches remain limited to time-division operations and fail to read multiple tags simultaneously. In this paper, we introduce QuinID, the first frequency-division multiple access (FDMA) RFID system to achieve fully parallel reading. We innovatively exploit the frequency selectivity of the tag antenna rather than a conventional digital FDMA, bypassing the power and circuitry constraint of RFID tags. Specifically, we delicately design the frequency-selective antenna based on surface acoustic wave (SAW) components to achieve extreme narrow-band response, so that QuinID tags (i.e., QuinTags) operate exclusively within their designated frequency bands. By carefully designing the matching network and canceling various interference, a customized QuinReader communicates simultaneously with multiple QuinTags across distinct bands. QuinID maintains high compatibility with commercial RFID systems and presents a tag cost of less than 10 cents. We implement a 5-band QuinID system and evaluate its performance under various settings. The results demonstrate a fivefold increase in read rate, reaching up to 5000 reads per second.

For precise, efficient, and safe drone landings, ground platforms should real-time, accurately locate descending drones and guide them to designated spots. While mmWave sensing combined with cameras improves localization accuracy, lower sampling frequency of traditional frame cameras compared to mmWave radar creates bottlenecks in system throughput. In this work, we replace traditional frame camera with event camera, a novel sensor that harmonizes in sampling frequency with mmWave radar within ground platform setup, and introduce mmE-Loc, a high-precision, low-latency ground localization system designed for drone landings. To fully leverage the temporal consistency and spatial complementarity between these modalities, we propose two innovative modules, consistency-instructed collaborative tracking and graph-informed adaptive joint optimization, for accurate drone measurement extraction and efficient sensor fusion. Real-world experiments in landing scenarios from a drone delivery company demonstrate that mmE-Loc outperforms SOTA methods in both accuracy and latency.

The flexibility and portability characteristics have made containers a popular serverless environment for large model training in recent years. Unfortunately, these advantages render the network support for containerized large model training extremely challenging, due to the high dynamics of containers, the complex interplay between underlay and overlay networks, and the stringent requirements on failure detection and localization. Existing data center network debugging tools, which rely on comprehensive or opportunistic monitoring, are either inefficient or inaccurate in this setting.

Abstract Navigation is a fundamental component of modern information application systems, ranging from military, transportations, and logistic, to explorations. Traditional navigations are based on an absolute coordination system that provides a precise map of the physical world, the locations of the moving objects, and the optimized navigation routes. In recent years, many new emerging applications have presented new demands for navigation, e.g., underwater/underground navigations where no GPS or other localizations are available, an un-explored area with no maps, and task-oriented navigations without specific routes. The advances in IoT and AI enable us to design new navigation paradigms, embodied navigation that allows the moving object to interact with the physical world to obtain the local map, localize the objects, and optimize the navigation routes accordingly. We make a systematic and comprehensive review of research in embodied navigation, encompassing key aspects on perceptions, navigation and efficiency optimization. Beyond advancements in these areas, we also examine the emerging tasks enabled by embodied navigation which require flexible mobility in diverse and evolving environments. Moreover, we identify the challenges associated with deploying embodied navigation systems in the real world and extend them to substantial areas. We aim for this article to provide valuable insights into this rapidly developing field, fostering future research to close existing gaps and advance the development of general-purpose autonomous systems grounded in embodied navigation.

This study unveils a cutting-edge camera-based system for the automated monitoring of hand hygiene practices within healthcare settings. Utilizing advanced computer vision and machine learning technologies, our system employs three strategically placed synchronized cameras around a wash basin. These cameras capture the handwashing process from multiple perspectives, allowing for detailed analysis of hand movements including finger and wrist dynamics. The extracted skeletal coordinate data are processed by a Gesture Category Model (GCM), which automatically identifies handwashing gestures. The model is rigorously trained on a dataset comprising video recordings from 55 healthcare professionals, focusing on the World Health Organizations seven-step hand-washing protocol. Furthermore, we introduce a Counting Algorithm to quantify the frequency and duration of each gesture, coupled with a Quality Assessment Model (QAM) that evaluates compliance with hand hygiene standards. The systems precision and its strong correlation with expert annotations highlight its potential to significantly enhance hand hygiene compliance and reduce healthcare-associated infections.

Flexible manufacturing is one of the core goals of Industry 4.0 and brings new challenges to current industrial control systems. Our detailed field study on auto glass industry revealed that existing production lines are laborious to reconfigure, difficult to upscale, and costly to upgrade during production switching. Such inflexibility arises from the tight coupling of devices, controllers, and control tasks. In this work, we propose a new architecture for industrial control systems named Control-as-a-Service (CaaS). CaaS transfers and distributes control tasks from dedicated controllers into network switches. By combining control and transmission functions in switches, CaaS virtualizes the whole industrial network to one Programmable Logic Controller (PLC). We propose a set of techniques that realize end-to-end determinism for in-network industrial control and a joint task and traffic scheduling algorithm. We evaluate the performance of CaaS on testbeds based on real-world networked control systems. The results show that the idea of CaaS is feasible and effective, and CaaS achieves absolute packet delivery, 42-45% lower latency, and three orders of magnitude lower jitter. We believe CaaS is a meaningful step towards the distribution, virtualization, and servitization of industrial control.

Split learning (SL) aims to protect a client's data by splitting up a neural network among the client and the server. Previous efforts have shown that a semi-honest server can conduct a model inversion attack. However, those attacks require the knowledge of the client network structure, and the performance deteriorates dramatically as the client network gets deeper ($\geq 2$layers). In this work, we explore the attack in a more general and challenging situation where the client model is unknown and more complex. We unveil the inherent privacy leakage through a series of intermediate server models during SL, and propose a new attack on SL:Pseudo-ClientATtack (PCAT). To the best of our knowledge, this is the first attack for a semi-honest server to steal clients' functionality, reconstruct private inputs and labels without any knowledge about the clients' network structure. Moreover, the attack is transparent to clients. Extensive experiments demonstrate that our attack outperforms previous works in scenarios involving more complex models and learning tasks, even in non-i.i.d. settings and confronted with conventional defensive measures. We further explore novel defense mechanisms to mitigate PCAT and improve our attack to counteract the potential defenses.

On low-power, low-cost Internet of Things (IoT) edges, coarse-grained entropy source-based physical-layer key generation (PKG) is often used, which results in a very low bit generation rate (BGR). In this paper, a novel PKG scheme, Hinge, designed to adapt to varying environmental conditions is introduced to optimize the trade-off between the bit mismatch rate (BMR) and BGR using fine-grained entropy sources on IoT devices. Hinge predicts channel reciprocity levels from one side and dynamically adjusts the quantization strategy, maintaining a low BMR while maximizing BGR. Compared with existing PKG solutions on Bluetooth devices, Hinge yields significant improvements in BGR, with a comparable BMR. Through extensive experiments, Hinge showcases its potential for providing a secure and efficient key generation mechanism for IoT devices in complex real-world scenarios.

The emerging integrated sensing and communication (ISAC) technologies show great potential for 5G NR, offering a new wireless-sensing infrastructure paradigm. Users can benefit from pervasive sensing applications in various scenarios without communication penalties. Given the diverse demands for sensing resources across different sensing tasks, elastic resource scheduling becomes crucial, particularly when resources are constrained. However, existing approaches often treat users equally, limiting the applicability in dealing with diverse sensing tasks in the real world. In this article, we introduce ElaSe , a pioneering sensing technique that enables elastic and prompt scheduling of sensing resources. At the core of ElaSe is the exploration of the user’s state to precisely determine the sensing resource requirements and schedule resources accordingly. We build the first model for matching sensing resources with sensing demands, and further propose a predictive scheduling scheme to eliminate delays by leveraging the 5G virtualized radio access network (vRAN). ElaSe has been implemented on a CPU-based 5G vRAN and commercial 5G user equipments. We conduct experiments to evaluate the performance of ElaSe under different settings. The results demonstrate that ElaSe outperforms the non-scheduling scheme, with a 34% reduction in trajectory tracking error and a 92% decrease in resource allocation error.

In remote industrial Internet of Things (IIoT) monitoring systems, the uncrewed aerial vehicle (UAV) serves as supplementary infrastructure to aggregate data from a large number of distributed sensors, and achieve industrial operation intelligence. In the wireless data aggregation process, using conventional orthogonal multiple access techniques face challenges such as scarce bandwidth, high communication latency and energy consumption. To tackle these issues, the over-the-air computation (AirComp) technique has emerged. It allows concurrent data transmissions from sensors, as well as integrates communication and computation processes, ultimately enabling fast data aggregation. However, the energy consumption issue remains unresolved. In this paper, we exploit spatial correlations among sensor measurements, and design an energy-efficient AirComp in UAV-assisted IIoT networks, where only a subset of sensors transmit data instead of all sensors. Then, we derive a closed-form expression for the mean square error (MSE) of each combination under a specific number of sensor transmissions. By jointly optimizing the UAV deployment and pre-coding coefficients of sensors, we formulate the problem of minimizing MSE for each combination of transmitted sensors. Furthermore, the MSE optimization algorithm is developed to output the average MSE of all combinations. Finally, we evaluate the average MSE and network lifetime performance of proposed scheme.

Fast and accurate obstacle avoidance is crucial to drone safety. Yet existing on-board sensor modules such as frame cameras and radars are ill-suited for doing so due to their low temporal resolution or limited field of view. This paper presentsBioDrone, a new design paradigm for drone obstacle avoidance using stereo event cameras. At the heart of BioDrone are three simple yet effective system designs inspired by the mammalian visual system, namely, a chiasm-inspired event filtering, a lateral geniculate nucleus (LGN)-inspired event matching, and a dorsal stream-inspired obstacle tracking. We implement BioDrone on FPGA through software-hardware co-design and deploy it on an industrial drone. In comparative experiments against two state-of-the-art event-based systems, BioDrone consistently achieves an obstacle detection rate of$> $90%, and an obstacle tracking error of$<$5.8 cm across all flight modes with an end-to-end latency of$<$6.4 ms, outperforming both baselines by over 44%.

Recent advances in mobile technologies (like WiFi 6 and 5G) do not seem to deliver the promised access bandwidth. To effectively characterize mobile access bandwidth in the wild, we work with a major commercial mobile bandwidth testing app to conduct a long-term (2020-2023) and large-scale (involving 4.76M users) measurement study in China, based on coarse-grained general statistics and fine-grained sampling diagnostics. Our study presents distinct facts as to WiFi, 5G, and 4G: in the past few years, the average WiFi download bandwidth exhibits a considerable rise (by 119.7% ), the average 5G download bandwidth constantly decreases (by a total of 20.2% ) despite the enormous infrastructure investments, while the average 4G download bandwidth first declines (by 22.1% ) and then increases (by 22.5% ). The situations of upload bandwidths are generally similar to those of download bandwidths, except that 5G upload bandwidths manifestN-shaped$(\nearrow \searrow \nearrow )$fluctuations. Our cross-layer and cross-technology analysis reveals a variety of impact factors as well as their complicated interplay as the root causes, such as the bottlenecks in underlying infrastructure (e.g., communication devices and wired Internet access), the traffic offloading from one access technology to another, the influence of the COVID-19 pandemic, and the side effects of aggressively migrating radio resources from 4G to 5G. With the longitudinal, holistic picture of today's mobile access bandwidth, we finally provide multifold practical implications on closing the technology gaps.

mmWave-based activity recognition technology has attracted widespread attention as it provides the ability of device-free, ubiquitous and accurate sensing. Recognition of human activities intrinsically demands to be real-time and continuous, but the state of the arts is still far limited with the capacity in this regard. The main obstacle lies in activity sequence segmentation, i.e., locating the boundaries between consecutive activities in an activity sequence. This is a daunting task, due to the unclear activity boundaries and the variable activity duration. In this paper, we proposeZuMa, the first mmWave-based approach to real-time continuous activity recognition. When resorting to a machine learning model for activity recognition, our insight is that the recognition confidence of the recognition model is highly correlated to the accuracy of activity sequence segmentation, so that the former can be utilized as a feedback metric to finely adjust the segmentation boundaries. Based on this insight,ZuMais a coarse-to-fine grained approach, which includes the fast coarse-grained activity chunk extraction and the find-grained explicit segmentation adjustment and recognition. We have implementedZuMawith the commercial mmWave radar and evaluated its performance under various settings. The results demonstrate thatZuMaachieves an average recognition error of 12.67%, which is 65.08% and 71.87% lower than that of the two baseline methods. The average recognition delay ofZuMais only 1.86 s.

Mobile air pollution sensing methods have emerged to collect air quality data with improved spatial and temporal resolutions. However, existing methodologies struggle to effectively process spatially mixed gas samples due to the highly dynamic fluctuations experienced by sensors, resulting in significant measurement deviations. We identify an opportunity to address this issue by exploring potential patterns within sensor measurements. To this end, we propose CatUA, a novel city-scale fine-grained air quality estimation system designed to deliver accurate mobile air quality data. First, we design AirBERT, a representation learning model specifically aimed at discerning mixed gas concentrations from sensor data. Second, we implement a Prompt-informed Training Strategy that leverages extensive unlabeled and minimal labeled city-scale data to enhance the performance of CatUA. Notably, the Auto-Prompt mechanism allows CatUA to conveniently acquire new knowledge tailored to specific downstream tasks. To ensure the practicality of CatUA, we have invested considerable effort in developing the software stack on our meticulously crafted Sensing Front-end, which has successfully gathered city-scale air quality data for over 1,200 hours. Experiments conducted on the collected data demonstrate that CatUA reduces sensing errors by 96.9% with a latency of only 44.9ms, outperforming the state-of-the-art baseline by 42.6%.

With recent advances on cellular technologies pushing the boundary of cellular performance, cellular reliability has become a key concern of their adoption and deployment. To fully understand cellular reliability, we work with a major Android phone vendor, Xiaomi, to conduct a long-term (2020-2024) and large-scale (involving 123M users) measurement study in China, with coarse-grained general statistics and fine-grained sampling diagnostics. Our measurement reveals contrasting evolution trends of cellular failures in different stages of the data connection: in the past five years, failures after connection establishment decrease remarkably (by 29%), while failures during connection setup exhibit a sharp increase (by 38%). Our analysis illustrates that the contrast stems from the joint impact of multiple stakeholders, including ISPs’ increasing deployment of 5G base stations, 5G infrastructure upgrade from NSA (Non-Standalone) to SA (Standalone) mode, software defects coming from Android’s adaptation to new cellular technologies, and so forth. Our work provides actionable insights for improving cellular reliability at scale. More importantly, we have built on our insights to develop enhancements that effectively address cellular reliability issues with remarkable real-world impact—our optimizations have reduced 38% cellular connection failures for 5G phones and 31% failure recovery time across all phones.

Backscatter is an enabling technology for battery-free sensing in today’s Artificial Intelligence of Things (AIOT). Building a backscatter sensing system, however, is a daunting task, due to two obstacles: the unaffordable power consumption of the microprocessor and the coexistence with the ambient carrier’s traffic. In order to address the issues, we present Leggiero, the first-of-its-kind analog WiFi backscatter with payload transparency, and its enhanced version, Leggiero+. A specially designed circuit based on the varactor diode directly converts fast-varying analog sensor signals into the RF (radio frequency) signal phase, eliminating the need for a microprocessor to interface between the radio and the sensor. By precisely locating the WiFi packet’s extra long training field (LTF) section and carefully designing the reference circuit, Leggiero embeds the analog phase into the channel state information (CSI). A commodity WiFi receiver without hardware modification can simultaneously decode the WiFi and the sensor data. We implement and evaluate Leggiero and Leggiero+ under varied settings. Results show the tag’s power consumption (excluding the power of the peripheral sensor module) is$30\mu $W at a 400Hz sampling rate,$4.8\times $and$4\times $lower than the state-of-the-art WiFi backscatter schemes. Leggiero+ demonstrates enhanced throughput, communication range, and analog signal reproduction accuracy. Our design supports a variety of sensing applications, while maintaining the WiFi carrier’s throughput performance.

Low Power Wide Area Networks (LPWANs) have emerged as a promising technology for facilitating large-scale, cost-effective connections through low-power, long-range communications. Nevertheless, the deployment of existing LPWANs is impeded by severe packet collisions. In this paper, we present OrthoRa, an innovative technology that significantly enhances the concurrency of low-power, long-range LPWAN transmissions. The cornerstone of OrthoRa lies in a groundbreaking design named Orthogonal Scatter Chirp Spreading Spectrum (OSCSS), which facilitates orthogonal packet transmissions while ensuring low signal-to-noise ratio (SNR) communication within LPWANs. Utilizing OrthoRa, different nodes can transmit packets encoded with unique orthogonal scatter chirps, enabling the receiver to decode collided packets from various nodes. We provide a theoretical validation of OrthoRa, demonstrating its capacity for high concurrency in low SNR communication. To surmount practical challenges inherent in real network deployments, we address the detection of multiple packets in collisions, the identification of scatter chirps for each packet’s decoding, and the precise synchronization of packets under Carrier Frequency Offset. We implemented OrthoRa on the HackRF One platform and conducted extensive performance evaluations. The results corroborate that OrthoRa amplifies network throughput and concurrency by a factor of 50 compared to LoRa and significantly outstripping the state-of-the-art in terms of robustness against collision time offset.

People can extract various kinds of information about the Internet from BGP routes tagged with BGP community values with known semantics. In this paper, we conduct a study on the following three issues related to BGP community semantics. First, we design a method to automatically collect self-reported semantics from the Internet and assemble the collected semantics described in natural language into a structured dictionary. The comparison with prior dictionaries shows many community values are exclusively covered by ours and many of them had been used when prior dictionaries were constructed, which confirms the effectiveness of our method. Second, based on this large-size dictionary, we are able to re-evaluate two recent algorithms designed for categorizing community values with unknown semantics, which is a task that, while easier than inferring the detailed semantics, is also very valuable. Our evaluation uncovers some issues within the algorithms that can contribute to their performance improvement. Third, we investigate the fundamental issue in extracting information using community semantics: whether ISPs' behavior is consistent with the published semantics. Our preliminary best-effort investigation reveals the potential risks of using the semantics of some categories of community values.

Drones have demonstrated their pivotal role in various applications such as search-and-rescue, smart logistics, and industrial inspection, with accurate localization playing an indispensable part. However, in high dynamic range and rapid motion scenarios, traditional visual sensors often face challenges in pose estimation. Event cameras, with their high temporal resolution, present a fresh opportunity for perception in such challenging environments. Current efforts resort to event-visual fusion to enhance the drone’s sensing capability. Yet, the lack of efficient event-visual fusion algorithms and corresponding acceleration hardware causes the potential of event cameras to remain underutilized. In this paper, we introduce EventBoost, an acceleration platform designed for drone-based applications with event-image fusion. We propose a suit of novel algorithms through software-hardware co-design on Zynq SoC, aimed at enhancing real-time localization precision and speed. EventBoost achieves enhanced visual fusion precision and markedly elevated processing efficiency. The performance comparison with two state-of-the-art systems shows EventBoost achieves 24.33% improvement in accuracy with 30 ms latency on resource-constrained platforms.

A heterogeneous micro aerial vehicles (MAV) swarm consists of resource-intensive but expensive advanced MAVs (AMAVs) and resource-limited but cost-effective basic MAVs (BMAVs), offering opportunities in diverse fields. Accurate and real-time localization is crucial for MAV swarms, but current practices lack a low-cost, high-precision, and real-time solution, especially for lightweight BMAVs. We find an opportunity to accomplish the task by transforming AMAVs into mobile localization infrastructures for BMAVs. However, turning this insight into a practical system is non-trivial due to challenges in location estimation with BMAVs’ unknown and diverse localization errors and resource allocation of AMAVs given coupled influential factors. This study proposes TransformLoc, a new framework that transforms AMAVs into mobile localization infrastructures, specifically designed for low-cost and resource- constrained BMAVs. We first design an error-aware joint location estimation model to perform intermittent joint location estimation for BMAVs and then design a proximity-driven adaptive grouping-scheduling strategy to allocate resources of AMAVs dynamically. TransformLoc achieves a collaborative, adaptive, and cost-effective localization system suitable for large-scale heterogeneous MAV swarms. We implement TransformLoc on industrial drones and validate its performance. Results show that TransformLoc outperforms baselines including SOTA up to 68% in localization performance, motivating up to 60% navigation success rate improvement.

Bluetooth Low Energy (BLE) is a prevalent technology in various applications due to its low power consumption and wide device compatibility. Despite its numerous advantages, the encryption methods of BLE often expose devices to potential attacks. To fortify security, we investigate the application of Physical-layer Key Generation (PKG), a promising technology that enables devices to generate a shared secret key from their shared physical environment. We propose a distinctive approach that capitalizes on the inherent characteristics of BLE to facilitate efficient PKG. We harness the constant tone extension within BLE protocols to extract comprehensive physical layer information and introduce an innovative method that employs Legendre polynomial quantization for PKG. This method facilitates the exchange of secret keys with a high key matching rate and a high key generation rate. The efficacy of our approach is validated through extensive experiments on a software-defined radio platform, underscoring its potential to enhance security in the rapidly expanding field of BLE applications.

The rapid development of WiFi localization poses a serious privacy threat, as eavesdroppers can locate WiFi devices without their consent. In this paper, we present WiCloak, the first system that protects WiFi device location privacy while supporting normal WiFi communication simultaneously. The high-level idea of WiCloak is to inject a fake channel into WiFi CSI at the transmitter, which renders the CIR and time information obtained by eavesdroppers meaningless. We mathematically prove that the injected fake channel is effective in any wireless environment and can strictly protect the location privacy of WiFi devices. To simultaneously support communication for commercial WiFi receivers, we propose a method to cancel out the fake channel impacts in decoding and prove that the method should not impact communication performance. WiCloak can work on commercial WiFi devices without any hardware modification. We evaluate the communication performance of WiCloak on commercial WiFi receivers (e.g., MacBook and Mac Studio) and demonstrate that it achieves the same packet reception rate as normal WiFi. We show that WiCloak increases the localization error by 22× to normal WiFi.

Integrated Sensing and Communication (ISAC) has been witnessed to be a new paradigm of wireless sensing in 5G networks. Users can benefit from pervasive sensing applications in various scenarios with no communication penalty. Given the diverse demands for sensing resources across different sensing tasks, elastic resource scheduling becomes crucial, particularly when resources are constrained. However, existing approaches often treat users equally, limiting their applicability in dealing with diverse sensing tasks in the real world. In this paper, we introduce ElaSe, a pioneering sensing technique that enables real-time elastic scheduling of sensing resources. At the core of ElaSa is the exploration of the user's state to precisely determine the sensing resource requirements and schedule resources accordingly. We build the first model for matching sensing resources with sensing demands, and further propose a predictive scheduling scheme to eliminate delays by leveraging the 5G virtualized radio access network (vRAN). We conduct experiments to evaluate the performance of ElaSe under different settings. The results demonstrate that ElaSe outperforms the non-scheduling scheme, with a 34% reduction in trajectory tracking error and a 92% decrease in resource allocation error.

Modern mobile systems are featured by their increasing interactivity with users, which however is accompanied by a severe side effect---users constantly suffer from slow UI responsiveness (SUR). To date, the community have limited understandings of this issue for the challenges of comprehensively measuring SUR events on massive mobile devices. As a major Android phone vendor, in this paper we close the knowledge gap by conducting the first large-scale, long-term measurement study on SUR with 47M devices. Our study identifies the critical factors that lead to SUR from the perspectives of device, system, application, and app market. Most importantly, we note that the largest root cause lies in the wide existence of "hogging" apps, which persistently occupy an unreasonable amount of system resources by leveraging the optimistic design of Android process management. We have built on the insights to remodel Android process states by fully considering their time-sensitive transitions and the actual behaviors of processes, with remarkable real-world impact---the occurrences of SUR are reduced by 60%, together with 10.7% saving of battery consumption.

Mobile task automation is an attractive technique that aims to enable voice-based hands-free user interaction with smartphones. However, existing approaches suffer from poor scalability due to the limited language understanding ability and the non-trivial manual efforts required from developers or endusers. The recent advance of large language models (LLMs) in language understanding and reasoning inspires us to rethink the problem from a model-centric perspective, where task preparation, comprehension, and execution are handled by a unified language model. In this work, we introduce AutoDroid, a mobile task automation system capable of handling arbitrary tasks on any Android application without manual efforts. The key insight is to combine the commonsense knowledge of LLMs and domain-specific knowledge of apps through automated dynamic analysis. The main components include a functionality-aware UI representation method that bridges the UI with the LLM, exploration-based memory injection techniques that augment the app-specific domain knowledge of LLM, and a multi-granularity query optimization module that reduces the cost of model inference. We integrate AutoDroid with off-the-shelf LLMs including online GPT-4/GPT-3.5 and on-device Vicuna, and evaluate its performance on a new benchmark for memory-augmented Android task automation with 158 common tasks. The results demonstrated that AutoDroid is able to precisely generate actions with an accuracy of 90.9%, and complete tasks with a success rate of 71.3%, outperforming the GPT-4-powered baselines by 36.4% and 39.7%.

Along with AIGC shines in CV and NLP, its potential in the wireless domain has also emerged in recent years. Yet, existing RF-oriented generative solutions are ill-suited for generating high-quality, time-series RF data due to limited representation capabilities. In this work, inspired by the stellar achievements of the diffusion model in CV and NLP, we adapt it to the RF domain and propose RF-Diffusion. To accommodate the unique characteristics of RF signals, we first introduce a novel Time-Frequency Diffusion theory to enhance the original diffusion model, enabling it to tap into the information within the time, frequency, and complex-valued domains of RF signals. On this basis, we propose a Hierarchical Diffusion Transformer to translate the theory into a practical generative DNN through elaborated design spanning network architecture, functional block, and complex-valued operator, making RF-Diffusion a versatile solution to generate diverse, high-quality, and time-series RF data. Performance comparison with three prevalent generative models demonstrates the RF-Diffusion's superior performance in synthesizing Wi-Fi and FMCW signals. We also showcase the versatility of RF-Diffusion in boosting Wi-Fi sensing systems and performing channel estimation in 5G networks.

LoRa has been widely used for the Internet of Things (IoT) due to its low power consumption and long communication range. The standard LoRa demodulation process condenses the energy of LoRa chirps to combat noise. However, there is an intrinsic frequency jump in real-life LoRa signals that standard demodulation neglects, reducing communication range in practice. We thoroughly study the frequency jump phenomenon and observe that it affects LoRa demodulation mainly in two folds: First, it makes each section of the signal shorter than the standard FFT perception range, introducing additional noise; Second, it induces a random phase jump that causes destructive addition of signal power. To mitigate the influence of frequency jump on LoRa demodulation, we propose LoRaTrimmer, a novel, fast, and noise-resilient LoRa decoding algorithm that optimally condenses LoRa signal power. LoRaTrimmer contains two innovative designs: First, we trim the perception range of FFT at the frequency jump, trimming off the additional noise; Second, we bypass the phase jump induced by frequency jump by probabilistic modeling and add up signal power constructively. Furthermore, we performed theoretical analysis to guarantee the performance of our method. Thorough experiments in various real-life environments show 1.70 to 2.49 dB SNR gain over the state-of-the-art and 3.44 to 3.79 dB SNR gain over FFT-based methods, translating to at most 1.67 times gain of coverage area. LoRaTrimmer is also robust under complex noise patterns, and capable of real-time decoding, with the only overhead being a slight increase in computational cost (0.51 to 3.17 ms per packet, compared with 0.23 to 0.94 ms of baseline methods).

Soil monitoring plays an essential role in agricultural systems. Rather than deploying sensors' antennas above the ground, burying them in the soil is an attractive way to retain a non-intrusive aboveground space. Low Power Wide-Area Network (LPWAN) has shown its long-distance and low-power features for aboveground Internet-of-Things (IoT) communication, presenting a potential of extending to underground cross-soil communication over a wide area, which however has not been investigated before. The variation of soil conditions brings significant signal polarization misalignment, degrading communication reliability. In this paper, we propose Demeter, a low-cost low-power programmable antenna design to keep reliable cross-soil communication automatically. First, we propose a hardware architecture to enable polarization adjustment on commercial-off-the-shelf (COTS) single-RF-chain LoRa radio. Moreover, we develop a low-power programmable circuit to obtain polarization adjustment. We further design an energy-efficient heuristic calibration algorithm and an adaptive calibration scheduling method to keep signal polarization alignment automatically. We implement Demeter with a customized PCB circuit and COTS devices. Then, we evaluate its performance in various soil types and environmental conditions. The results show that Demeter can achieve up to 11.6 dB SNR gain indoors and 9.94 dB outdoors, 4× horizontal communication distance, at least 20 cm deeper underground deployment, and up to 82% energy consumption reduction per day compared with the standard LoRa.

Drone-based rapid and accurate environmental edge detection is highly advantageous for tasks such as disaster relief and autonomous navigation. Current methods, using radar or cameras, raise deployment costs and burden lightweight drones with high computational demands. In this paper, we propose AirTouch, a system that transforms the ground effect from a stability "foe" in traditional flight control views, into a "friend" for accurate and efficient edge detection. Our key insight is that analyzing drone sensor readings and flight commands allows us to detect ground effect changes. Such changes typically indicate the drone flying over an edge, making this information valuable for edge detection. We approach this insight through theoretical analysis, algorithm design, and implementation, fully leveraging the ground effect as a new sensing modality without compromising drone flight stability, thereby achieving accurate and efficient scene edge detection. Extensive evaluations demonstrate that our system achieves a high detection accuracy with mean detection distance errors of 0.051m, outperforming the baseline performance by 86%.

WiFi backscatter localization is a promising technology for the Internet of Things. However, existing works cannot work well for large-scale and low-cost tags with commodity WiFi devices. We present Willow, which provides accurate localization for parallel backscatter tags with commodity WiFi devices. We design a packet-level orthogonal backscatter modulation method to generate multiple orthogonal backscatter signals and support in-band backscatter with ambient WiFi. We show that backscatter signals can be effectively extracted even under strong in-band interference. To work in real WiFi traffic, we propose adaptive packet selection-based modulation to guarantee the orthogonality of backscatter signals. For parallel localization, we propose an iterative inter-tag interference cancellation method and a location filtering method to remove location ambiguity. We theoretically analyze the effectiveness of our method in supporting parallel tags. We prototype Willow tags using low-cost hardware and implement Willow AP on commodity WiFi NIC AX200. Through extensive experiments, we show that Willow achieves a median localization error of 27 cm and supports 51 parallel tags, which is 2× and 17× better than the state-of-the-art method.

This paper introduces ChirpTransformer, a versatile LoRa encoding framework that harnesses broad chirp features to dynamically modulate data, enhancing network coverage, throughput, and energy efficiency. Unlike the standard LoRa encoder that offers only single configurable chirp feature, our framework introduces four distinct chirp features, expanding the spectrum of methods available for data modulation. To implement these features on commercial off-the-shelf (COTS) LoRa nodes, we utilize a combination of a software design and a hardware interrupt. ChirpTransformer serves as the foundation for optimizing encoding and decoding in three specific case studies: weak signal decoding for extended network coverage, concurrent transmission for heightened network throughput, and data rate adaptation for improved network energy efficiency. Each case study involves the development of an end-to-end system to comprehensively evaluate its performance. The evaluation results demonstrate remarkable enhancements compared to the standard LoRa. Specifically, ChirpTransformer achieves a 2.38 × increase in network coverage, a 3.14 × boost in network throughput, and a 3.93 × of battery lifetime.

Mobile air pollution sensing methods are developed to collect air quality data with higher spatial-temporal resolutions. However, existing methods cannot process the spatially mixed gas samples effectively due to the highly dynamic temporal and spatial fluctuations experienced by the sensor, leading to significant measurement deviations. We find an opportunity to tackle the problem by exploring the potential patterns from sensor measurements. In light of this, we propose MobiAir, a novel city-scale fine-grained air quality estimation system to deliver accurate mobile air quality data. First, we design AirBERT, a representation learning model to discern mixed gas concentrations. Second, we design a knowledge-informed training strategy leveraging massive unlabeled city-scale data to enhance the AirBERT performance. To ensure the practicality of MobiAir, we have invested significant efforts in implementing the software stack on our meticulously crafted Sensing Front-end, which has successfully gathered air quality data at a city-scale for more than 1200 hours. Experiments conducted on collected data show that MobiAir reduces sensing errors by 96.7% with only 44.9ms latency, outperforming the SOTA baseline by 39.5%.

Emerging mobile apps such as UHD video and AR/VR access diverse high-throughput hardware devices, e.g., video codecs, cameras, and image processors. However, today's mobile emulators exhibit poor performance when emulating these devices. We pinpoint the major reason to be the discrepancy between the guest's and host's memory architectures for hardware devices, i.e., the mobile guest's centralized memory on a system-on-chip (SoC) versus the PC/server's separated memory modules on individual hardware. Such a discrepancy makes the shared virtual memory (SVM) architecture of mobile emulators highly inefficient.

Multi-UAV systems have shown immense potential in handling complex tasks in large-scale, dynamic, and cold-start (i.e., limited prior knowledge) scenarios, such as wildfire suppression. Due to the dynamic and stochastic environmental conditions, the scheduling for sensing tasks (i.e., fire monitoring) and operation tasks (i.e., fire suppression) should be executed concurrently to enable real-time information collection and timely intervention of the environment. However, the planning inclinations of sensing and operation tasks are typically inconsistent and evolve over time, complicating the task of identifying the optimal strategy for each UAV. To solve this problem, this paper proposes SOScheduler, a collaborative multi-UAV scheduling framework for integrated sensing and operation in large-scale and dynamic wildfire environments. We introduce a spatio-temporal confidence-aware assessment model to dynamically and directly pinpoint locations that can optimally enhance the understanding of environmental dynamics and operational effectiveness, as well as a priority graph-instructed scalable scheduler to coordinate multi-UAV in an efficient manner. Experiments on real multi-UAV testbeds and large-scale physical feature-based simulations show that our SOScheduler reduces the fire expansion ratio by 59% and enhances the fire coverage ratio by 190% compared to state-of-the-art (SOTA) solutions.

Streaming services have billions of mobile subscribers, yet video piracy has cost service providers billions. Digital Rights Management (DRM), however, is still far from satisfactory. Unlike DRM, which attempts to prohibit the creation of pirated copies, fingerprinting may be used to track out the source of piracy. Nevertheless, existing fingerprinting-based streaming systems are not widely used since they fail to serve numerous users. In this paper, we present the design and evaluation of StreamingTag, a scalable piracy tracing system for mobile streaming services. StreamingTag adopts a segment-level fingerprint embedding scheme to remove the need of re-embedding the fingerprint into the video for each new viewer. The key innovations of StreamingTag include a scalable and CDN-friendly delivery framework, an accurate and lightweight temporal synchronization scheme, a polarized and randomized SVD watermarking scheme, and a collusion-resistant fingerprinting scheme. Experiment results show the good QoS of StreamingTag in terms of preparation latency, bandwidth consumption, and video fidelity. Compared with existing methods, the proposed three schemes improve the re-identification accuracy by 4-49x, the watermark extraction accuracy by 2.25x at most and 1.5x on average, and the recall rate of catching colluders by 26%.

With the desired functionality of moving object tracking, wireless pan-tilt cameras are able to play critical roles in a growing diversity of surveillance environments. However, today's pan-tilt cameras oftentimes underperform when tracking frequently moving objects like humans – they are prone to lose sight of objects and bring about excessive mechanical rotations that are especially detrimental to those energy-constrained outdoor scenarios. The ineffectiveness and high cost of all state-of-the-art tracking approaches are rooted in their adherence to the industry's simplicity principle, which leads to their stateless nature, performing gimbal rotations based only on the latest object detection. To address the issues, we design and implement WiseCam that wisely tunes the pan-tilt cameras to minimize mechanical rotation costs while maintaining long-term object tracking. This systematic tracking approach also tackles issues of motion-rotation speed gap and scattered moving objects, which is universally applicable to complex tracking scenarios. We examine the performance of WiseCam by experiments on two types of pan-tilt cameras with different motors. Results show that it significantly outperforms the state-of-the-art tracking approaches on both tracking duration and power consumption.

Almost all Android users have ever experienced poor responsiveness, including the common frame dropping events—slow rendering (SR) and frozen frames (FF), as well as the uncommon Application Not Responding (ANR) and System Not Responding (SNR) that directly disrupt user experience. This work takes two complementary approaches,controlled benchmarkingandin-the-wild crowdsourcing, to comprehensively understand their prevalence, characteristics, and root causes, which turn out to be significantly different from common understandings and prior studies. We find that SR, FF, ANR, and SNR all occur prevalently on all the studied hardware models of Android phones, and better hardware does not seem to relieve ANR/SNR. Most surprisingly, they are oftentimes ascribed to defective software design that incurs substantial resource overuse—lightweight apps can experience severe SR/FF events due toredundant UI rendering, and the most ANR/SNR events stem from Android's aggressive implementation ofwrite amplification mitigation. In fact, the former can be effectively overcome by simplifying the apps' UI hierarchy, and we design a practical approach to address almost all ($>$99%) of the latter while only decreasing 3% of the data write speed with large-scale deployment. We have released our measurement code/data to the research community.

As the development of the Internet of Things, location service plays a more important role in mobile computing. To provide location service for the already deployed devices and objects, we present LiTag, a visible light-based localization and posture estimation solution with commercial off-the-shelf (COTS) cameras. The core of LiTag is based on the design of a chip-less and battery-less optical tag which can show different color patterns from different observation directions. After capturing a photo containing the tag, LiTag can calculate the tag position and posture by combining the color pattern and the geometric relation in camera imaging. To solve the localization ambiguity, we propose an ambiguity-avoidance method based on a projection relationship. LiTag can work with a single camera without calibration, which significantly reduces the calibration overhead and deployment costs. We implement LiTag and evaluate its performance extensively. Results show that LiTag can provide the tag position with a median error of 1$cm$in the 2D plane, a median error of 5$cm$in the 3D space, and posture estimation with a median error of$0.8^{\circ }$. We believe that LiTag has high potential to provide a low-cost and easy-to-use solution for ubiquitous localization and posture estimation with widely deployed cameras.

Mobile emulation, which creates full-fledged software mobile devices on a physical PC/server, is pivotal to the mobile ecosystem. Unfortunately, existing mobile emulators perform poorly on graphics-intensive apps in terms of efficiency and compatibility. To address this, we introduce graphics projection , a novel graphics virtualization mechanism that adds a small-size projection space inside the guest memory, which processes graphics operations involving control contexts and resource handles without host interactions. While enhancing performance, the decoupled and asynchronous guest/host control flows introduced by graphics projection can significantly complicate emulators’ reliability issue diagnosis when faced with a variety of uncommon or non-standard app behaviors in the wild, hindering practical deployment in production. To overcome this drawback, we develop an automatic reliability issue analysis pipeline that distills the critical code paths across the guest and host control flows by runtime quarantine and state introspection. The resulting new Android emulator, dubbed Trinity, exhibits an average of 97% native hardware performance and 99.3% reliable app support, in some cases outperforming other emulators by more than an order of magnitude.

The edge-based multi-agent visual SLAM is crucial for emerging mobile applications like search-and-rescue, inventory automation, and industrial inspection. It uses a central node to manage the global map and schedule tasks for agents. However, as the number of agents increases, the system faces scalability challenges due to operational overhead, such as data redundancy, bandwidth consumption, and localization errors. In this paper, we introduce, a framework designed to enhance the scalability of collaborative visual SLAM service in edge offloading settings. consists of three system modules: a change log-based server-client synchronization mechanism, a priority-aware task scheduler, and a lean global map representation. These modules work together to address the challenges of data explosion problems. is open-source and compatible with the robotic operating system (ROS). Existing visual SLAM applications could incorporate through SwarmAPI, a set of well-packaged APIs, to compose SwarmMap’s function modules to enhance their performance and capacity in multi-agent scenarios. Comprehensive evaluations and a three-month case study at one of the world’s largest oilfields demonstrate that can serve 2$\times$more agents ($>$20 agents) than the state-of-the-arts with the same resource overhead, meanwhile maintaining an average trajectory error of 38$cm$, outperforming existing works by$>$55%.

The widespread use of intelligent devices and the development of mobile networks have led to the increasing popularity of live-streaming services worldwide. In addition to video and audio transmissions, a wide range of media content is also sent to audiences, such as player statistics for sports streams and subtitles for live news. However, due to the diverse transmission process between live streams and other media content, synchronizing them has become a significant challenge. Unfortunately, existing commercial solutions are not universal, requiring specific server cloud services or CDNs and limiting users’ free choices of web infrastructures. To address this issue, we propose a lightweight and universal solution called LSync, which inserts a series of audio signals containing metadata into the original audio stream. Based on the embedded metadata, a well-designed timeline-synchronizing solution helps to synchronize the information stream to the live stream. It brings no modifications to the original live broadcast process and thus fits prevalent live broadcast infrastructures. Evaluations show that the proposed solution reduces the signal processing delay to around 5% of an audio buffer length in mobile phones and ensures real-time signal processing. It achieves a channel utilization of more than 150 bps/kHz in a specific configuration, greatly outperforming recent works. Furthermore, the proposed synchronization mechanism reaches a precision of 24.84 ms on average, which matches people’s viewing habits.

The radio range of backscatter systems continues growing as new wireless communication primitives are continuously invented. Nevertheless, both the bit error rate and the packet loss rate of backscatter signals increase rapidly with the radio range, thereby necessitating the cooperation between the access point and the backscatter tags through a feedback loop. Unfortunately, the low-power nature of backscatter tags limits their ability to demodulate feedback signals from a remote access point and scales down to such circumstances. This paper presents, an ultra-low-power demodulator for long-range LoRa backscatter systems. is based on an observation that a frequency-modulated chirp signal can be transformed into an amplitude-modulated signal using a differential circuit. Moreover, we redesign a LoRa backscatter tag which integrates and a ring oscillator-based modulator. The LoRa backscatter tag enables re-transmission, rate adaption and channel hopping – three PHY-layer operations that are important to channel efficiency yet unavailable on existing long-range backscatter systems. We prototype and the LoRa backscatter tag on two PCB boards and evaluate their performance in different environments. Results show that achieves 3.5–5$\times$gain on the demodulation range, compared with state-of-the-art systems. Our ASIC simulation shows that the power consumption of and the LoRa backscatter tag are around 93.2$\mu W$and 94.7$\mu W$. Code and hardware schematics can be found at: https://github.com/ZangJac/Saiyan.

This paper presents RF-Transformer, a unified backscatter radio hardware abstraction that allows a low-power IoT device to directly communicate with heterogeneous wireless receivers. Unlike existing backscatter systems that are tailored to a specific wireless communication protocol, RF-Transformer provides a programmable interface to the micro-controller, allowing IoT devices to synthesize different types of protocol-compliant backscatter signals in the PHY layer. By leveraging the nonlinear characteristics of the negative impedance, RF-Transformer also achieves a cross-frequency backscatter design that enables IoT devices in harmonic frequency bands to communicate with each other. We implement a PCB prototype of RF-Transformer on 2.4 GHz ISM band and conduct extensive experiments. We leverage the software defined platform USRP to transmit the carrier signal and receive the backscatter signal to verify the efficacy of our design. Our extensive field studies show that RF-Transformer achieves 23.8 Mbps, 247.1 Kbps, 986.5 Kbps, and 27.3 Kbps throughput when generating standard Wi-Fi, ZigBee, Bluetooth, and LoRa signals.

Along with the proliferation of IoT devices, people have a lot of concerns on the privacy issues brought by them. Existing solutions, employing encryption or trying to hide the communication in PHY layer, often suffer from the limited capability of IoT devices. To address this issue, we propose Rustle, an acoustic communication design which builds covert connection among IoT devices using random noise. Noise signal can be easily generated and exchanged by the widely used speakers and microphones on IoT devices. Based on a fine-grained control of signal’s wave shape, Rustle generates a series of mutually uncorrelated random signals that contain “hidden patterns” to embed information. Extensive evaluations demonstrate that Rustle can achieve a lower than 1% BER while the eavesdropper’s error rate on detecting the signal is higher than 80%.

Industrial Identifiers (IID), such as GS1, Handle, and OID are fundamental to device identification in growing industrial networks. Appropriate resolution and addressing methods for those identifiers are designed for wide area networks (WAN). However, due to compatibility, efficiency, and security considerations, they are not suitable for local area networks (LANs) environments. Therefore, we propose a new industrial identification framework to handle LAN scenarios by industrial address. It mainly includes the Industrial Identifier Resolution Protocol (IIRP), which combines the IIRP table lookup, the IIRP request, and the response based on the data link layer frame transmission. It is implemented as a software plug-in on LAN devices without changing any network protocol or hardware, ensuring compatibility with existing network infrastructure. Our experiments also test the efficiency of the IIRP protocol.

Low Power Wide Area Networks (LPWANs) have been shown promising in connecting large-scale low-cost devices with low-power long-distance communication. However, existing LPWANs cannot work well for real deployments due to severe packet collisions. We propose OrthoRa, a new technology which significantly improves the concurrency for low-power long-distance LPWAN transmission. The key of OrthoRa is a novel design, Orthogonal Scatter Chirp Spreading Spectrum (OSCSS), which enables orthogonal packet transmissions while providing low SNR communication in LPWANs. Different nodes can send packets encoded with different orthogonal scatter chirps, and the receiver can decode collided packets from different nodes. We theoretically prove that OrthoRa provides very high concurrency for low SNR communication under different scenarios. For real networks, we address practical challenges of multiple-packet detection for collided packets, scatter chirp identification for decoding each packet and accurate packet synchronization with Carrier Frequency Offset. We implement OrthoRa on HackRF One and extensively evaluate its performance. The evaluation results show that OrthoRa improves the network throughput and concurrency by 50× compared with LoRa.

Flexible manufacturing is one of the core goals of Industry 4.0 and brings new challenges to current industrial control systems. Our detailed field study on auto glass industry revealed that existing production lines are laborious to reconfigure, difficult to upscale, and costly to upgrade during production switching. Such inflexibility arises from the tight coupling of devices, controllers, and control tasks. In this work, we propose a new architecture for industrial control systems named Control-as-a-Service (CaaS). CaaS transfers and distributes control tasks from dedicated controllers into Time-Sensitive Networking (TSN) switches. By combining control and transmission functions in switches, CaaS virtualizes the industrial TSN network to one Programmable Logic Controller (PLC). We propose a set of techniques that realize end-to-end determinism for in-network industrial control and a joint task and traffic scheduling algorithm. We evaluate the performance of CaaS on testbeds based on real-world networked control systems. The results show that the idea of CaaS is feasible and effective, and CaaS achieves absolute packet delivery, 42-45% lower latency, and three orders of magnitude lower jitter. We believe CaaS is a meaningful step towards the distribution, virtualization, and servitization of industrial control.

Virtual devices based on device emulation have been widely used in lab research of mobile app testing for their efficiency and low cost. However, it remains controversial to use virtual devices for app testing in industry, given the inherent difficulties of high-fidelity emulation across diverse mobile systems and devices. Hence, mobile app companies still rely on physical device farms or services like AWS Device Farm.

Fast and accurate obstacle avoidance is crucial to drone safety. Yet existing on-board sensor modules such as frame cameras and radars are ill-suited for doing so due to their low temporal resolution or limited field of view. This paper presents BioDrone, a new design paradigm for drone obstacle avoidance using stereo event cameras. At the heart of BioDrone is two simple yet effective system design inspired by the mammalian visual system, namely, a chiasm-inspired signal processing pipeline for fast event filtering and obstacle detection, and a lateral geniculate nucleus (LGN)-inspired event matching algorithm for accurate obstacle localization. To make BioDrone a practical solution, we further take significant engineering efforts to deploy the software stack on FPGA through software and hardware co-design. The performance comparison with two state-of-the-art event-based obstacle avoidance systems shows BioDrone achieves a consistently high obstacle detection rate of 96.1%. The average localization error of BioDrone is 6.8cm with a 4.7ms latency, outperforming both baselines by over 40%.

LoRa and its enabled LoRa wide-area network (LoRaWAN) have been seen as an important part of the next-generation network for massive Internet-of-Things (IoT). Due to LoRa's low-power and long-range nature, LoRa signals are much weaker than the noise floor, particularly in complex urban or semi-indoor environments. Therefore, weak signal decoding is critical to achieve the desired wide-area coverage in general. Existing work has shown the advantages of exploring deep neural networks (DNN) for weak signal decoding. However, the existing single-gateway based DNN decoder is hard to fully leverage the spatial information in multi-gateway scenarios. In this paper, we propose SRLoRa, an efficient DNN LoRa decoder that fully utilizes the spatial information from multiple gateways to decode extremely weak LoRa signals. Specifically, we design interleaving denoising and merging layers to improve signal quality at ultra-low SNR. We develop efficient merging on feature maps extracted by denoising DNNs to tolerate time misalignments among different signals. We define max and min operations in the merging layer to efficiently extract salient features and reduce noise, merging the features extracted from multiple gateways to guide future DNN layers to gradually improve signal quality. We implement SRLoRa with USPR N210 and commercial LoRa nodes and evaluate its performance indoors and outdoors. The results show that with four gateways, SRLoRa achieves SNR gain at 4.53--4.82 dB, which is 2.51× of Charm, leading to a 1.84× coverage area compared to standard LoRa in an urban deployment.

Long-range backscatter localization is a promising technology for the Internet of Things. Existing works cannot work well for distributed base stations and low-cost tags. We present LocRa, which provides accurate localization for long-range backscatter with distributed base stations. We present a novel method to extract accurate channel information and synchronize the phase of different base stations. To compensate for the frequency and phase error on low-cost tags, we combine multiple channel measurements and eliminate the error by aligning different channels. Finally, we exploit frequency domain characteristics of the backscatter signal to extend its bandwidth and improve the SNR, thereby enhancing the localization accuracy. We prototype LocRa tags using custom low-cost hardware and implement LocRa base stations on USRP. Through extensive experiments, we show that the localization error of LocRa is 6.8 cm and 88 cm when the tag is 5m and 50m away from the base station, which is 3.1× and 2.3× better than the state-of-the-arts methods.

Backscatter is an enabling technology for battery-free sensing in today's Artificial Intelligence of Things (AIOT). Building a backscatter-based sensing system, however, is a daunting task, due to two obstacles: the unaffordable power consumption of the microprocessor and the coexistence with the ambient carrier's traffic. In order to address the above issues, in this paper, we present Leggiero, the first-of-its-kind analog WiFi backscatter with payload transparency. Leveraging a specially designed circuit with a varactor diode, this design avoids using a microprocessor to interface between the radio and the sensor, and directly converts the analog sensor signal into the phase of RF (radio frequency) signal. By carefully designing the reference circuit on the tag and precisely locating the extra long training field (LTF) section of a WiFi packet, Leggiero embeds the analog phase value into the channel state information (CSI). A commodity WiFi receiver without hardware modification can simultaneously decode the WiFi and the sensor data. We implement Leggiero design and evaluate its performance under varied settings. The results show that the power consumption of the Leggiero tag (excluding the power of the peripheral sensor module) is 30μW at a sampling rate of 400Hz, which is 4.8× and 4× lower than the state-of-the-art WiFi backscatter schemes. The uplink throughput of Leggiero is suficient to support a variety of sensing applications, while keeping the WiFi carrier's throughput performance unaffected.

LoRa, as a representative Low-Power Wide-Area Network (LPWAN) technology, holds tremendous potential for various city and industrial applications. However, as there are few real large-scale deployments, it is unclear whether and how well LoRa can eventually meet its prospects. In this paper, we demystify the real performance of LoRa by deploying and measuring a citywide LoRa network, named CityWAN, which consists of 100 gateways and 19,821 LoRa end nodes, covering an area of 130 km2 for 12 applications. Our measurement focuses on the following perspectives: (i) Performance of applications running on the citywide LoRa network; (ii) Infrastructure efficiency and deployment optimization; (iii) Physical layer signal features and link performance; (iv) Energy profiling and cost estimation for LoRa applications. The results reveal that LoRa performance in urban settings is bottlenecked by the prevalent blind spots, and there is a gap between the gateway efficiency and network coverage for the infrastructure deployment. Besides, we find that LoRa links at the physical layer are susceptible to environmental variations, and LoRa and other LPWANs show diverse costs for different scenarios. Our measurement provides insights for large-scale LoRa network deployment and also for future academic research to fully unleash the potential of LoRa.

Web performance optimization services, or web performance optimizers (WPOs), play a critical role in today’s web ecosystem by improving page load speed and saving network traffic. However, WPOs are known for introducing visual distortions that disrupt the users’ web experience. Unfortunately, visual distortions are hard to analyze, test, and debug, due to their subjective measure, dynamic content, and sophisticated WPO implementations.

In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone’s daily life and profoundly altering the course of human society. The intention behind developing AI was and is to benefit humans by reducing labor, increasing everyday conveniences, and promoting social good. However, recent research and AI applications indicate that AI can cause unintentional harm to humans by, for example, making unreliable decisions in safety-critical scenarios or undermining fairness by inadvertently discriminating against a group or groups. Consequently, trustworthy AI has recently garnered increased attention regarding the need to avoid the adverse effects that AI could bring to people, so people can fully trust and live in harmony with AI technologies. A tremendous amount of research on trustworthy AI has been conducted and witnessed in recent years. In this survey, we present a comprehensive appraisal of trustworthy AI from a computational perspective to help readers understand the latest technologies for achieving trustworthy AI. Trustworthy AI is a large and complex subject, involving various dimensions. In this work, we focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Nondiscrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems. We also discuss the accordant and conflicting interactions among different dimensions and discuss potential aspects for trustworthy AI to investigate in the future.

Localization and navigation play a key role in many location-based services and have attracted numerous research efforts. In recent years, visual SLAM has been prevailing for autonomous driving. However, the ever-growing computation resources demanded by SLAM impede its applications to resource-constrained mobile devices. In this paper, we present the design, implementation, and evaluation ofedgeSLAM, an edge-assisted real-time semantic visual SLAM service running on mobile devices.edgeSLAMleverages the state-of-the-art semantic segmentation algorithm to enhance localization and mapping accuracy, and speeds up the computation-intensive SLAM and semantic segmentation algorithms by computation offloading. The key innovations ofedgeSLAMinclude an efficient computation offloading strategy, an opportunistic data sharing method, an adaptive task scheduling algorithm, and a multi-user support mechanism. We fully implementedgeSLAMand plan to open-source it. Extensive experiments are conducted under 3 datasets. The results show thatedgeSLAMcan run on mobile devices at 35fps and achieve 5cm localization accuracy from real-world experiments, outperforming existing solutions by more than 15%. We also demonstrate the usability ofedgeSLAMthrough 2 case studies of pedestrian localization and robot navigation. To the best of our knowledge,edgeSLAMis the first edge-assisted real-time semantic visual SLAM for mobile devices.

Vibration measurement is a crucial task in industrial systems, where vibration characteristics reflect health conditions and indicate anomalies of the devices. Previous approaches either work in an intrusive manner or fail to capture the micrometer-level vibrations. In this work, we propose mmVib, a practical approach to measure micrometer-level vibrations with mmWave radar. First, we derive a metric calledVibration Signal-to-Noise Ratio(VSNR) that highlights the directions of reducing measurement errors of tiny vibrations. Then, we introduce the design of mmVib based on the concept ofMulti-Signal Consolidation(MSC) for the error reduction and multi-object measurement. We implement a prototype of mmVib, and the experiments show that it achieves$3.946\%$relative amplitude error and$0.02487\%$relative frequency error in median. Typically, the average amplitude error is only$3.174um$when measuring the$100um$-amplitude vibration at around 5 meters. Compared to two existing mmWave-based approaches, mmVib reduces the 80th-percentile amplitude error by$69.21\%$and$97.99\%$respectively.

The signal phase is one of the most important metrics in RFID-based sensing, which is a useful technique enabling many significant applications. However, existing approaches of RFID-based sensing are often restricted in terms of the sensing capability or accuracy, due to the phase entanglement problem: the phase of the RFID signal is jointly affected by multiple factors, and the change in the signal phase cannot be directly attributed to any one of them. In order to tackle this problem, we propose RF-Prism, a versatile sensing approach that can simultaneously infer multiple physical factors (i.e., location, orientation, and material of targets), purely based on the phase readings. RF-Prism includes a comprehensive model to describe how different physical factors affect the phase of the received signal, and a complete design to disentangle the phase in the multi-frequency and multi-antenna scenario. We implement RF-Prism and evaluate its performance with extensive experiments. The results show that RF-Prism simultaneously achieves a mean localization error of$\text{7.61}~cm$, a mean orientation error of 9.83 degrees, a mean tracking error of$\text{6.12}~cm$, and 87.9% material identification accuracy, which outperforms state-of-the-art approaches.

The ability to localize acoustic sources can greatly improve the perception of smart devices (e.g., a smart speaker like Amazon Alexa). In this work, we study the problem of concurrently localizing multiple acoustic sources with a single smart device. Our proposal calledSymphonyis the first complete solution to tackle the above problem, including method, theory, and practice. The method stems from the insight that the geometric layout of microphones on the array determines the unique relationship among signals from the same source along the same arriving path. We also establish the theoretical model ofSymphony, which reveals the relation between localization performance (resolution and coverage) and impacting factors (sampling rate, array aperture, and array-wall distance). Moreover, the ability to separate and localize multiple sources is also studied theoretically and numerically. We implementSymphonywith different types of commercial off-the-shelf microphone arrays and evaluate its performance under different settings. The results show thatSymphonyhas a median localization error of 0.662 m.

Access bandwidth measurement is crucial to emerging Internet applications for network-aware content delivery. However, today’s bandwidth testing services (BTSes) are slow and costly—the tests take a long time to run, consume a great deal of data usage, and usually require large-scale test server deployments. The inefficiency and high cost of BTSes root in their methodologies that use excessive temporal/spatial redundancies for combating noises in Internet measurement. In particular, compared to downlink BTSes, uplink BTSes are subject to more severe performance problems and technical challenges. This paper presents FastUpBTS to make uplink BTS fast and cheap while maintaining high accuracy. The key idea is to strategically accommodate and exploit the noise rather than repetitively and exhaustively suppress the impact of noise. This is achieved by a novel statistical sampling framework termed fuzzy rejection sampling. We build FastUpBTS as an end-to-end BTS that implements fuzzy rejection sampling based on memorization-reinforced throughput denoising, data-driven server selection, and informed multi-homing support. Our evaluation shows that with only 30 test servers, FastUpBTS achieves the same level of accuracy compared to the state-of-the-art BTS (SpeedTest.net) that deploys ~16,000 servers. Most importantly, FastUpBTS makes bandwidth tests$5.4\times $faster and$6.8\times $more data-efficient.

Time-Sensitive Networking (TSN) is the most promising network technology for Industry 4.0. A series of IEEE standards on TSN introduce deterministic transmission into standard Ethernet. Under the current paradigm, TSN can only schedule the deterministic transmission of time-triggered critical traffic (TCT), neglecting the other type of traffic in industrial cyber physical systems, i.e., event-triggered critical traffic (ECT). So in this work, we propose a new paradigm for TSN scheduling named E-TSN, which can provide deterministic transmission for both TCT and ECT. The three techniques of E-TSN, i.e., probabilistic stream, prioritized slot sharing, and prudent reservation, enable the deterministic transmission of ECT in TSN, and at the same time, protect TCT from the impacts of ECT. We also develop and make public a TSN evaluation toolkit to fill the gap in TSN study between algorithm design and experimental validation. The experiments show that E-TSN can reduce the latency and jitter of ECT by at least an order of magnitude compared to state-of-the-art methods. By enabling reliable and timely delivery of ECT in TSN for the first time, E-TSN can broaden the application scope of TSN in industry.

Low Power Wide Area Networks (LPWAN) have become one of the key techniques to provide long-range, low-power communication for large-scale devices in the Internet of Things. However, LPWAN devices in real deployments (e.g., in buildings and basements) suffer from low-quality links due to signal attenuation, leading to coverage holes and significant deployment overhead. In this work, we propose Ostinato to enable communication for weak links and to enhance the coverage for real deployments of COTS LoRa. The key idea of Ostinato is to transform the original packet to a pseudo packet with repeated symbols and to concentrate the energy of multiple symbols to enhance the signal SNR. To address practical challenges, we reverse engineer the entire coding and modulation process of LoRa and propose a method to generate repeated symbols on COTS LoRa by manipulating input data bits. Thus, Ostinato can be directly used for widely deployed LoRa nodes without hardware modification. We achieve weak packet detection, synchronization, and effective decoding on the receiver side by concentrating energy from multiple symbols with phase offsets. We implement Ostinato on Software Defined Radio (SDR) platform and extensively evaluate its performance. The evaluation results show that Ostinato achieves an 8.5 dB gain on receiving sensitivity and 2.88× gain on the coverage compared with COTS LoRa.

The widespread of smart devices and the development of mobile networks brings the growing popularity of live streaming services worldwide. In addition to the video and audio transmission, a lot more media content is sent to the audiences as well, including player statistics for a sports stream, subtitles for living news, etc. However, due to the diverse transmission process between live streams and other media content, the synchronization of them has grown to be a great challenge. Unfortunately, the existing commercial solutions are not universal, which require specific server cloud services or CDN and limit the users’ free choices of web infrastructures. To address the issue, we propose a lightweight universal event-synchronizing solution for live streaming, called LSync, which inserts a series of audio signals containing metadata into the original audio stream. It brings no modification to the original live broadcast process and thus fits prevalent live broadcast infrastructure. Evaluations on real system show that the proposed solution reduces the signal processing delay by at most 5.62% of an audio buffer length in mobile phones and ensures real-time signal processing. It also achieves a data rate of 156.25 bps in a specific configuration and greatly outperforms recent works.

Along with the proliferation of Internet of Things (IoT) devices, people have a lot of concerns on the privacy issues brought by those devices. Existing solutions, employing strong encryption al-gorithms or trying to hide the communication in PHY layer, often suffer from the limited capability of IoT devices. In order to address the issue, we propose Rustle, an acoustic communication design which builds covert connection among IoT devices using random noise signal. Noise signal can be easily generated and exchanged by the widely used speakers and microphones on IoT devices. Based on the weak transitivity of signal's correlation and a fine-grained control of signal's wave shape, Rustle generates a series of mutu-ally uncorrelated random signals that contain “hidden patterns” to embed information. Extensive evaluations demonstrate that Rustle can achieve a lower than 1% BER while the eavesdropper's error rate on detecting the signal is higher than 80%.

This paper presents RF-Transformer, a unified backscatter radio hardware abstraction that allows a low-power IoT device to directly communicate with heterogeneous wireless receivers at the minimum power consumption. Unlike existing backscatter systems that are tailored to a specific wireless communication protocol, RF-Transformer provides a programmable interface to the micro-controller, allowing IoT devices to synthesize different types of protocol-compliant backscatter signals sharing radically different PHY-layer designs. To show the efficacy of our design, we implement a PCB prototype of RF-Transformer on 2.4 GHz ISM band and showcase its capability on generating standard ZigBee, Bluetooth, LoRa, and Wi-Fi 802.11b/g/n/ac packets. Our extensive field studies show that RF-Transformer achieves 23.8 Mbps, 247.1 Kbps, 986.5 Kbps, and 27.3 Kbps throughput when generating standard Wi-Fi, ZigBee, Bluetooth, and LoRa signals while consuming 7.6--74.2X less power than their active counterparts. Our ASIC simulation based on the 65-nm CMOS process shows that the power gain of RF-Transformer can further grow to 92--678X. We further integrate RF-Transformer with pressure sensors and present a case study on detecting foot traffic density in hallways. Our 7-day case studies demonstrate RF-Transformer can reliably transmit sensor data to a commodity gateway by synthesizing LoRa packets on top of Wi-Fi signals. Our experimental results also verify the compatibility of RF-Transformer with commodity receivers. Code and hardware schematics can be found at: https://github.com/LeFsCC/RF-Transformer.

Streaming services have billions of mobile subscribers, yet video piracy has cost service providers billions. Digital Rights Management (DRM), however, is still far from satisfactory. Unlike DRM, which attempts to prohibit the creation of pirated copies, fingerprinting may be used to track out the source of piracy. Nevertheless, the idea of piracy tracing is not widely used at the moment, since existing fingerprinting-based streaming systems fail to serve numerous users. In this paper, we present the design and evaluation of StreamingTag, a scalable piracy tracing system for mobile streaming services. StreamingTag adopts a segment-level fingerprint embedding scheme to remove the need of re-embedding the fingerprint into the video for each new viewer. The key innovations of StreamingTag include a scalable and CDN-friendly delivery framework, a polarized and randomized SVD watermarking scheme suitable for short segments, and a collusion-resistant fingerprinting scheme optimized for large-scale streaming services. Experiment results show the good QoS of StreamingTag in terms of preparation latency, bandwidth consumption, and video fidelity. Compared with existing SVD watermarking schemes, the proposed watermarking scheme improves the watermark extraction accuracy by 2.25x at most and 1.5x on average. Compared with existing collusion-resistant fingerprinting schemes, the proposed scheme catches more colluders and improves the recall rate by 26%.

Unlicensed LPWANs on ISM bands share the spectrum with various wireless techniques, such as Wi-Fi, Bluetooth, and ZigBee. The explosion of IoT deployments calls for an increasing need for long-range cross-technology communication (CTC) between LPWANs and other techniques. Yet, existing technologies cannot achieve real long-range CTC for commodity wireless. We propose L2X, which provides long-range CTC to diverse receivers with LoRa transmitters. At the heart of L2X, we design an energy-concentrating demodulation mechanism that de-spreads LoRa chirps over the air. Therefore, L2X enables non-LoRa receivers to detect and demodulate LoRa signals even under extremely low SNR. We address practical challenges in L2X design. We propose a packet detection method to detect low-SNR LoRa transmissions at non-LoRa receivers. To decode LoRa transmissions, we accurately synchronize the demodulation window with incoming packets and propose a cross-domain demodulation approach to enhance the demodulation SNR. We implement L2X, all using commodity devices, and extensively evaluate its performance. The results show that L2X achieves 1.2 km CTC with the signal -9 dB below the noise floor, improving the distance by 30X compared with state-of-the-arts.

After years of boom, drones and their applications are now entering indoors. Six-degree-of-freedom (6-DoF) pose tracking is the core of drone flight control, but existing solutions cannot be directly applied to indoor scenarios due to insufficient accuracy, low robustness to adverse texture and light conditions, and signal obstruction in indoor scenarios. To overcome the above limitations, we propose Wi-Drone, a Wi-Fi standalone 6-DoF tracking system for indoor drone flight control. Wi-Drone takes full advantage of both exte-roceptive and proprioceptive measurements of Wi-Fi to estimate the drone's absolute pose and relative motion, and fuse them in a tight-coupling manner to achieve their complementary benefits. We implement Wi-Drone and integrate it into a flight control system. The evaluation results show that Wi-Drone achieves a real-time performance with the average location accuracy of 26.1 cm and the rotation accuracy of 3.8°, which demonstrates its competency of flight control, compared to visual-inertial-based flight control. Such results also outperform existing Wi-Fi-based tracking solutions in terms of both dimensionality and accuracy.

Recent advances in mobile technologies such as 5G and WiFi 6E do not seem to deliver the promised mobile access bandwidth. To effectively characterize mobile access bandwidth in the wild, we work with a major commercial mobile bandwidth testing app to analyze mobile access bandwidths of 3.54M end users in China, based on fine-grained measurement and diagnostic information. Our analysis presents a surprising and frustrating fact---in the past two years, the average WiFi bandwidth remains largely unchanged, while the average 4G/5G bandwidth decreases remarkably. Our analysis further reveals the root causes---the bottlenecks in the underlying infrastructure (e.g., devices and wired Internet access) and side effects of aggressively migrating radio resources from 4G to 5G---with implications on closing the technology gaps. Additionally, our analysis provides insights on building ultra-fast, ultra-light bandwidth testing services (BTSes) at scale. Our new design dramatically reduces the test time of the commercial BTS from 10 seconds to 1 second on average, with a 15× reduction on the backend cost.

With the development of signal processing technology, the ubiquitous Wi-Fi devices open an unprecedented opportunity to solve the challenging human gesture recognition problem by learning motion representations from wireless signals. Wi-Fi-based gesture recognition systems, although yield good performance on specific data domains, are still practically difficult to be used without explicit adaptation efforts to new domains. Various pioneering approaches have been proposed to resolve this contradiction but extra training efforts are still necessary for either data collection or model re-training when new data domains appear. To advance cross-domain recognition and achieve fully zero-effort recognition, we propose Widar3.0, a Wi-Fi-based zero-effort cross-domain gesture recognition system. The key insight of Widar3.0 is to derive and extract domain-independent features of human gestures at the lower signal level, which represent unique kinetic characteristics of gestures and are irrespective of domains. On this basis, we develop a one-fits-all general model that requires only one-time training but can adapt to different data domains. Experiments on various domain factors (i.e. environments, locations, and orientations of persons) demonstrate the accuracy of 92.7% for in-domain recognition and 82.6%-92.4% for cross-domain recognition without model re-training, outperforming the state-of-the-art solutions.

Androidoverlayenables one app to draw over other apps by creating an extraViewlayer atop the hostView, which nevertheless can be exploited by malicious apps (malware) to attack users. To combat this threat, prior countermeasures concentrate on restricting the capabilities of overlays at the OS level while sacrificing overlays’ usability; recently, the overlay mechanism has been substantially updated to prevent a variety of attacks, which however can still be evaded by considerable adversaries. To address these shortcomings, a more pragmatic approach is to enableearly detectionof overlay-based malware during the app market review process, so that all the capabilities of overlays can stay unchanged. For this purpose, in this paper we first conduct a large-scale comparative study of overlay characteristics in benign and malicious apps, and then implement the OverlayChecker system to automatically detect overlay-based malware for one of the world’s largest Android app stores. In particular, we have made systematic efforts in feature engineering, UI exploration, emulation architecture, and run-time environment, thus maintaining high detection accuracy (97 percent precision and 97 percent recall) and short per-app scan time ($\sim$1.7 minutes) with only two commodity servers, under an intensive workload of$\sim$10K newly submitted apps per day.

Mobile video streaming occupies three-quarters of today's cellular network traffic. The quality of mobile videos becomes increasingly important for video providers to attract more users. For example, they invest in network bandwidth resources and conduct adaptive bitrate techniques to improve video quality. Prior adaptive bitrate (ABR) algorithms perform well under given throughput traces on broadband and WiFi networks. They may perform poorly for mobile video streaming due to the high network dynamics of cellular networks. To study the properties of throughput traces under cellular networks, we collect 4G network throughput traces for over four months in two large cities, Beijing and Suzhou in China. We derive the environment-specific Markov property of throughputs in the dataset. Accordingly, we propose NEIVA, an environment identification based technique to adaptively predict future throughput for different types of environments. We also implement NEIVA and integrate it with the state-of-the-art ABR algorithm, model predictive control (MPC) approach in our testbed for experiments. By emulating mobile video streaming under throughput traces in our dataset, NEIVA achieves 20 - 25 percent improvement on throughput prediction accuracy comparing to baseline predictors. Meanwhile, NEIVA achieves 11 - 20 percent user QoE improvement over MPC with baseline predictors.

Ubiquitous mobile sensors on human activity recognition pose the threat of leaking personal information that is implicitly contained within the time-series sensor signals and can be extracted by attackers. Existing protective methods only support specific sensitive attributes and require massive relevant sensitive ground truth for training, which is unfavourable to users. To fill this gap, we propose a novel data transformation framework for prohibiting the leakage of sensitive information from sensor data. The proposed framework transforms raw sensor data into a new format, where the sensitive information is hidden and the desired information (e.g., human activities) is retained. Training can be conducted without using any personal information as ground truth. Meanwhile, multiple attributes of sensitive information (e.g., age, gender) can be collectively hidden through a one-time transformation. The experimental results on two multimodal sensor-based human activity datasets manifest the feasibility of the presented framework in hiding users’ sensitive information (inference MAE increases$\sim$2 times and inference accuracy degrades$\sim$50%) without degrading the usability of the data for activity recognition (only$\sim$2% accuracy degradation).

Backscatter communication holds potential for ubiquitous and low-cost connectivity among low-power IoT devices. To avoid interference between the carrier signal and the backscatter signal, recent works propose a frequency-shifting technique to separate these two signals in the frequency domain. Such proposals, however, have to occupy the precious wireless spectrum that is already overcrowded, and increase the power, cost, and complexity of the backscatter tag. In this paper, we revisit the classic ON-OFF Keying (OOK) modulation and propose Aloba, a backscatter system that takes the ambient LoRa transmissions as the excitation and piggybacks the in- band OOK modulated signals over the LoRa transmissions. Our design enables the backscatter signal to work in the same frequency band of the carrier signal, meanwhile achieving flexible data rate at different transmission range. The key contributions of Aloba include: i) the design of a low-power backscatter tag that can pick up the ambient LoRa signals from other signals; ii) a novel decoding algorithm to demodulate both the carrier signal and the backscatter signal from their superposition. We further adopt link coding mechanism and interleave operation to enhance the reliability of backscatter signal decoding. We implement Aloba and conduct head-to-head comparison with the state-of-the-art LoRa backscatter system PLoRa in various settings. The experiment results show Aloba can achieve 39.5–199.4 Kbps data rate at various distances, 10.4–$52.4\times $higher than PLoRa.

LoRa, a representative Low-Power Wide Area Network (LPWAN) technology, has been shown as a promising platform to connect Internet of Things. Practical LoRa deployments, however, suffer from collisions, especially in dense networks and wide coverage areas expected by LoRa applications. Existing collision resolving approaches do not exploit the modulation properties of LoRa and thus cannot work well for low-SNR LoRa signals. We proposeNScaleto decompose concurrent transmissions by leveraging subtle inter-packet time offsets for low SNR LoRa collisions. NScale (1) translates subtle time offsets, which are vulnerable to noise, to robust frequency features, and (2) further amplifies the time offsets by non-stationary signal scaling, i.e., scaling the amplitude of a symbol differently at different positions. In practical implementation, we propose a noise resistant iterative symbol recovery method to combat symbol distortion in low SNR, and address frequency shifts incurred by CFO and packet time offsets in decoding. We propose optimized designs for diminishing the time costs of computation-intensive tasks and meeting the real-time requirements of LoRa collision resolving. We theoretically show that NScale introduces$3.3\times $for low SNR collided signals compared with other state-of-the-art methods.

Delta synchronization (sync) is crucial to the network-level efficiency of cloud storage services, especially when handling large files with small increments. Practical delta sync techniques are, however, only available for PC clients and mobile apps, but not web browsers—the most pervasive and OS-independent access method. To bridge this gap, prior work concentrates on either reversing the delta sync protocol or utilizing the native client, all striving around the tradeoffs among efficiency, applicability, and usability and thus forming an “impossible triangle.” Recently, we note the advent of WebAssembly (WASM) , a portable binary instruction format that is efficient in both encoding size and load time. In principle, the unique advantages of WASM can make web-based applications enjoy near-native runtime speed without significant cloud-side or client-side changes. Thus, we implement a straightforward WASM-based delta sync solution, WASMrsync, finding its quasi-asynchronous working manner and conventional In-situ Separate Memory Allocation greatly increase sync time and memory usage. To address them, we strategically devise sync-async code decoupling and streaming compilation, together with Informed In-place File Construction. The resulting solution, WASMrsync+, achieves comparable sync time as the state-of-the-art (most efficient) solution with nearly only half of memory usage, letting the “impossible triangle” reach a reconciliation.

The signal phase is one of the most important metrics in RFID-based sensing, which is a useful technique enabling many significant applications. However, existing approaches of RFID-based sensing are often restricted in terms of the sensing capability or accuracy, due to the phase entanglement problem: the phase of the RFID signal is jointly affected by multiple factors, and the change in the signal phase cannot be directly attributed to anyone of them. In order to tackle this problem, we propose RF-Prism, a versatile sensing approach that can simultaneously infer multiple physical factors (i.e., location, orientation, and material of targets), purely based on the phase readings. RF-Prism includes a comprehensive model to describe how different physical factors affect the phase of the received signal, and a complete design to disentangle the phase in the multi-frequency and multi-antenna scenario. We implement RF-Prism and evaluate its performance with extensive experiments. The results show that RF-Prism simultaneously achieves a mean localization error of 7.61 cm, a mean orientation error of 9.83 degrees, and 87.9% material identification accuracy, which outperforms state-of-the-art approaches.

The ability to detect outliers is crucial in data mining, with widespread usage in many fields, including fraud detection, malicious behavior monitoring, health diagnosis, etc. With the tremendous volume of data becoming more distributed than ever, global outlier detection for a group of distributed datasets is particularly desirable. In this work, we propose PIF (Privacy-preserving Isolation Forest), which can detect outliers for multiple distributed data providers with high efficiency and accuracy while giving certain security guarantees. To achieve the goal, PIF makes an innovative improvement to the traditional iForest algorithm, enabling it in distributed environments. With a series of carefully-designed algorithms, each participating party collaborates to build an ensemble of isolation trees efficiently without disclosing sensitive information of data. Besides, to deal with complicated real-world scenarios where different kinds of partitioned data are involved, we propose a comprehensive schema that can work for both horizontally and vertically partitioned data models. We have implemented our method and evaluated it with extensive experiments. It is demonstrated that PIF can achieve comparable AUC to existing iForest on average and maintains a linear time complexity without privacy violation.

Recently, mmWave has been widely used in fine-grained sensing applications due to its short wavelength and large bandwidth. One mmWave device usually can measure the target's 1D micro-displacement along the line-of-sight (LOS) direction. In this work, we try to empower mmWave with the capability of measuring 2D micro-displacements. Our insight is that although the mmWave reflection from one path contains only 1D observation, the spatial separability of mmWave offers an opportunity to separate multipath reflections from the received signal. Combining the coherent observations from multipath reflections can restore the 2D orbit of the target. Based on this insight, we present GWaltz, a mmWave sensing system that manages to measure sub-mm-level 2D orbits of rotating machinery. In GWaltz, we first reveal the relationship between the rotor's movement and the observed ghost multipath reflections (GMRs) and then design a set of novel signal processing techniques to restore the rotor orbit from the poor-quality GMR signals. We implement GWaltz with a commercial mmWave radar, and our evaluation results show that it achieves an absolute error of about 8.42um when measuring 100um-diameter rotor orbits.

Today’s video service providers all desire to deeply understand the ever changing factors on user QoE to attract more users. In this paper, we study the user engagement with respect to video quality metrics and improve user engagement in adaptive video streaming systems. We conduct a comprehensive study of the real data from iQIYI, covering 700K users and 150K videos. We find bitrate switch becomes the new dominant factor on user engagement instead of rebuffering events. We also observe the impact of rate of rebuffering is more dominant than rebuffering time. We examine novel interdependencies between quality metrics in the system, e.g., the positive correlation between bitrate switch and average bitrate, which is due to the context system strategy, i.e., conservative bitrate enhancing strategy adopted by iQIYI. To improve user engagement, we propose a new engagement centric QoE function based on real data and design server side ABR algorithm which leverages our new QoE function. We evaluate our method for online test in iQIYI, with 490K real users viewing 666K streams. The results show our approach outperforms existing approaches by significantly improving the viewing time, i.e., 2.8 minutes longer viewing time per user.

Carrying over 75% of the last-mile mobile Internet traffic, WiFi has inevitably become an enticing target for various security threats. In this work, we characterize a wide variety of real-world WiFi threats at an unprecedented scale, involving 19 million WiFi access points (APs) mostly located in China, by deploying a crowdsourced security checking system on 14 million mobile devices in the wild. Leveraging the collected data, we reveal the landscape of nationwide WiFi threats for the first time. We find that the prevalence, riskiness, and breakdown of WiFi threats deviate significantly from common understandings and prior studies. In particular, we detect attacks at around 4% of all WiFi APs, uncover that most WiFi attacks are driven by an underground economy, and provide strong evidence of web analytics platforms being the bottleneck of its monetization chain. Further, we provide insightful guidance for defending against WiFi attacks at scale, and some of our efforts have already yielded real-world impact---effectively disrupted the WiFi attack ecosystem.

LoRa, as a representative Low-Power Wide-Area Network (LPWAN) technology, can provide long-range communication for battery-powered IoT devices with a 10-year lifetime. LoRa links in practice, however, experience high dynamics in various environments. When the SNR falls below the threshold (e.g., in the building), a LoRa device disconnects from the network. We propose Falcon, which addresses the link dynamics by enabling data transmission for very low SNR or even disconnected LoRa links. At the heart of Falcon, we reveal that low SNR LoRa links that cannot deliver packets can still introduce interference to other LoRa transmissions. Therefore, Falcon transmits data bits on the low SNR link by selectively interfering with other LoRa transmissions. We address practical challenges in Falcon design. We propose a low-power channel activity detection method to detect other LoRa transmissions for selective interference. To interfere with the so-called interference-resilient LoRa, we accurately estimate the time and frequency offsets on LoRa packets and propose an adaptive frequency adjusting strategy to maximize the interference. We implement Falcon, all using commercial off-the-shelf LoRa devices, and extensively evaluate its performance. The results show that Falcon can provide reliable communication links for disconnected LoRa devices and achieves the SNR boundary upto 7.5 dB lower than that of standard LoRa.

Low-Power Wide-Area Networks (LPWANs) are an emerging Internet-of-Things (IoT) paradigm marked by low-power and long-distance communication. Among them, LoRa is widely deployed for its unique characteristics and open-source technology. By adopting the Chirp Spread Spectrum (CSS) modulation, LoRa enables low signal-to-noise ratio (SNR) communication. However, the standard demodulation method does not fully exploit the properties of chirp signals, thus yields a sub-optimal SNR threshold under which the decoding fails. Consequently, the communication range and energy consumption have to be compromised for robust transmission. This paper presents NELoRa, a neural-enhanced LoRa demodulation method, exploiting the feature abstraction ability of deep learning to support ultra-low SNR LoRa communication. Taking the spectrogram of both amplitude and phase as input, we first design a mask-enabled Deep Neural Network (DNN) filter that extracts multi-dimension features to capture clean chirp symbols. Second, we develop a spectrogram-based DNN decoder to decode these chirp symbols accurately. Finally, we propose a generic packet demodulation system by incorporating a method that generates high-quality chirp symbols from received signals. We implement and evaluate NELoRa on both indoor and campus-scale outdoor testbeds. The results show that NELoRa achieves 1.84-2.35 dB SNR gains and extends the battery life up to 272% (~0.38-1.51 years) in average for various LoRa configurations.

With recent advances on cellular technologies (such as 5G) that push the boundary of cellular performance, cellular reliability has become a key concern of cellular technology adoption and deployment. However, this fundamental concern has never been addressed due to the challenges of measuring cellular reliability on mobile devices and the cost of conducting large-scale measurements. This paper closes the knowledge gap by presenting the first large-scale, in-depth study on cellular reliability with more than 70 million Android phones across 34 different hardware models. Our study identifies the critical factors that affect cellular reliability and clears up misleading intuitions indicated by common wisdom. In particular, our study pinpoints that software reliability defects are among the main root causes of cellular data connection failures. Our work provides actionable insights for improving cellular reliability at scale. More importantly, we have built on our insights to develop enhancements that effectively address cellular reliability issues with remarkable real-world impact---our optimizations on Android's cellular implementations have effectively reduced 40% cellular connection failures for 5G phones and 36% failure duration across all phones.

Cross-Technology Communication (CTC) is an emerging technique that enables direct communication across different wireless technologies. The state-of-the-art works in this area propose physical-level CTC, in which the transmitters emulate signals that follow the receiver's standard. Physical-level CTC means considerable processing complexity at the transmitter, which does not apply to the communication from a low-end transmitter to a high-end receiver. This article proposes LEGO-Fi, which supports the CTC from ZigBee to WiFi and Bluetooth to WiFi. LEGO-Fi is a transmitter-transparent CTC technique, which leaves the processing complexity solely at the receiver side and therefore, makes a critical advance toward bidirectional high-throughput CTC. The key technique inside is cross-demapping, which stems from two key technical insights: 1) a ZigBee/Bluetooth packet leaves distinguishable features when passing the WiFi modules and 2) compared to ZigBee's/Bluetooth's simple encoding and modulation schemes, the rich processing capacity of WiFi offers extra flexibility to process a ZigBee/Bluetooth packet. The evaluation results show that LEGO-Fi achieves a throughput of 213.6 Kb/s in practice, which is, respectively, 13000×, 1200×, and 7.5× faster than FreeBee, ZigFi, and SymBee, the three existing ZigBee-to-WiFi CTC approaches. For the CTC from Bluetooth to WiFi, LEGO-Fi achieves a throughput of 904.1 Kb/s.

Indoor localization is becoming an emerging requirement in many large shopping malls. Existing indoor localization systems, however, require exhausted system bootstraps and calibration phases. The huge sunk cost usually hinders practical deployment of the indoor localization systems in large shopping malls. In contrast, we observe that floor-plan images of large shopping malls, which highlight the positions of many shops, are widely available in Google Maps, Gaode Maps, Baidu Maps, etc. According to several observed shops, people can localize themselves (called self-localization). However, due to the requirements of geometric sense and space transformation, not all people get used to this way. In this article, we propose EyeLoc, which uses smartphone vision to enable accurate self-localization on a floor-plan image. EyeLoc addresses several challenges, including developing a ubiquitous smartphone vision system, extracting efficient vision clues, and achieving robust measurement error mitigation. We implement EyeLoc in Android and evaluate its performance in emulated environment, two large shopping malls and a semioutdoor large Outlets. The results show that the 90-percentile errors of localization and heading direction are 5.97 m and 20° in 70 000 m2malls.

Clone attack is considered as a severely destructive threat in Internet of Things (IoT), because: 1) the attack may be easily launched due to the deficiency of hardware architecture and the limited resources against physical capture and compromise and 2) it may trigger a large variety of insider and outsider attacks. Different from traditional clone attack detection approaches that ground on a large amount of data traversing the network (e.g., locations and identities), this article tackles this problem by answering the following fundamental questions: do we really need so much raw information? whether there is an alternative for local event detection by a node far away from that event? when acquiring/tracing global knowledge of a system/network, do we really need a global collection effort? These questions are of much importance in a large variety of networks. Specifically, this article provides a collective memory design for global topology and identity tracing (GTI Tracing), via a localized computing paradigm within neighborhood. This localized paradigm computationally builds a connection of identity and topology from time and space domain to a new computation domain. Such a computation domain retains four properties: 1) transitivity; 2) global convergence; 3) determinacy; and 4) causality. With byte-size information at an arbitrary device, it can recover and keep tracing global topology and identity information, and thus providing deterministic detection of clones. Both theoretical analysis and experimental study have shown the advantages of the proposed design in both detection accuracy and privacy protection, at a cost of light communication, storage, and computation overhead at each device.

Eccentricity detection is a crucial issue for high-speed rotating machinery, which concerns the stability and safety of the machinery. Conventional techniques in industry for eccentricity detection are mainly based on measuring certain physical indicators, which are costly and hard to deploy. In this paper, we propose RED, a non-intrusive, low-cost, and real-time RFID-based eccentricity detection approach. Differing from the existing RFID-based sensing approaches, RED utilizes the temporal and phase distributions of tag readings as effective features for eccentricity detection. RED includes a Markov chain based model called RUM, which only needs a few sample readings from the tag to make a highly accurate and precise judgement. The design of RED further addresses practical issues, such as parameterizing the RUM model, making it robust to dynamic and noisy environments, and considering how the doppler shift may affect our system. We implement RED with COTS RFID reader and tags, and evaluate its performance across various scenarios. The overall accuracy is 93.6 percent and the detection latency is 0.68 seconds in average.

RFID tracking attracts a lot of research efforts in recent years. Most of the existing approaches, however, adopt an orientation-oblivious model. When tracking a target whose orientation changes, those approaches suffer from serious accuracy degradation. In order to achieve target tracking with pervasive applicability in various scenarios, in this article, we propose OmniTrack, an orientation-aware RFID tracking approach. Our study presents a generic model that discribes the linear relationship between the tag orientation and the phase change of the backscattered signals. Based on this finding, we propose an orientation-aware phase model to explicitly quantify the respective impact of the read-tag distance and the tag's orientation. OmniTrack addresses practical challenges in tracking the location and orientation of a mobile tag. Our experimental results demonstrate that OmniTrack achieves centimeter-level location accuracy and has significant advantages in tracking targets with varing orientations, compared to the state-of-the-art approaches.

Acoustic motion tracking has been viewed as a promising user interaction technique in many scenarios such as Virtual Reality (VR), Smart Appliance, video gaming, etc. Existing acoustic motion tracking approaches, however, suffer from long window of accumulated signal and time-consuming signal processing. They are inherently difficult to achieve both high accuracy and low delay. In this paper, we present Vernier, an efficient and accurate acoustic tracking method based on commodity mobile devices. We design a new approach to efficiently and accurately derive phase change and thus moving distance. Vernier significantly reduces the tracking delay/overhead by removing the complicated frequency analysis and long window of signal accumulation, while keeping a high tracking accuracy. We implement Vernier on Android, and evaluate its performance with COTS mobile devices including Samsung Galaxy S7 and Sony L50t. Experimental results show that Vernier outperforms previous approaches with a tracking error less than 4 mm. The tracking speed achieves 3× improvement to the previous phase based approaches and 10× to Doppler Effect based approaches. Vernier is also validated in applications like controlling and drawing, and we believe it is generally applicable in many real applications.

Hidden screen-camera communication techniques emerge as a new paradigm that embeds data imperceptibly into regular videos while remaining unobtrusive to human viewers. Three key goals on imperceptible, high rate, and reliable communication are desirable but conflicting, and existing solutions usually made a trade-off among them. In this paper, we present the design and implementation of CHROMACODE, a screen-camera communication system that achieves all three goals simultaneously. In our design, we consider for the first time color space for perceptually uniform lightness modifications. On this basis, we design an outcome-based adaptive embedding scheme, which adapts to both pixel lightness and regional texture. Last, we propose a concatenated code scheme for robust coding and devise multiple techniques to overcome various screen-camera channel errors. Our prototype and experiments demonstrate that CHROMACODE achieves remarkable raw throughputs of >700 kbps, data goodputs of 120 kbps with BER of 0.05, and with fully imperceptible flicker for viewing proved by user study, which significantly outperforms previous works.

As backscatter-based IoT applications get proliferated, how to exploit backscattered signals for efficient sensing becomes a significant issue. Backscatter-based sensing requires accurate estimation of a backscatter channel (phase and amplitude), which is distorted when multiple signals collide with each other. As a result, the state of the arts is limited to either parallel decoding of collided signal or channel estimation with clean signal. Motivated by the need of high sensing capacity, we in this article present Fireworks, the first approach for channel estimation of parallel backscattered signals. The insight of Fireworks is that although the channel is distorted due to collision, the movements of the ON-OFF Keying modulated signal still preserve the channel properties of the respective tags. By modeling the relationship between the channels and the signal's moving trajectory in the IQ domain, one can make accurate estimation of the channels directly from the collision. We address practical problems of Fireworks, such as the high computing complexity and the compatibility with the commercial MAC protocol, and implement Fireworks. The results show that Fireworks is able to estimate the channels of up to five tags in parallel. When applied to the tracking application, Fireworks achieves 2 ~ 4× improvement in the tracking accuracy, compared with the state-of-the-art approach.

Adaptive bitrate (ABR) algorithms are critical techniques for high quality-of-experience (QoE) Internet video delivery. Early ABR algorithms conducting the overall QoE function of fixed parameters are limited by the fact that the QoE of end-users are diverse such that the video bitrate is often chosen in a misleading way. State-of-the-art ABR algorithms like MPC and Pensieve utilize offline modeling techniques and result in performance degradation for online QoE diversity adaptation. To address this issue, we propose Elephanta, an online ABR algorithm for edge users, which incorporates user QoE perception interface and adaptation algorithm with flexible parameters. In order to avoid overhead from updating parameters online, we model video streaming as a renewal system and formulate the specific QoE function into flexible formats by setting constraints on corresponding QoE metrics. To validate parameter settings, we emulate Elephanta under 1500 throughput traces, including FCC broadband, $3G$ HSDPA data set from the Internet, as well as the $4G$ /LTE data set we collect. Evaluation results show that Elephanta achieves QoE improvement of 7% over MPC and 3% over Pensieve under QoE diversity in part because of its superior adaptability to QoE diversity. We implemented Elephanta in dash.js at the client side for subjective experiments. We observed the diverse QoE preferences across users and 19/21 users (strongly) agree that Elephanta is responsive to parameter changes while watching videos.

No abstract available.

editorial Free Access Share on Guest Editorial: Special Issue on Computational Intelligence in the Internet of Things Editor: Yunhao Liu Search about this author Authors Info & Claims ACM Transactions on Sensor NetworksVolume 17Issue 3August 2021 Article No.: 21pp 1–4https://doi.org/10.1145/3463812Published:05 August 2021Publication History 0citation75DownloadsMetricsTotal Citations0Total Downloads75Last 12 Months44Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF

Despite being crucial to today's mobile ecosystem, app markets have meanwhile become a natural, convenient malware delivery channel as they actually “lend credibility” to malicious apps. In the past few years, machine learning (ML) techniques have been widely explored for automated, robust malware detection, but till now we have not seen an ML-based malware detection solution applied at market scales. To systematically understand the real-world challenges, we conduct a collaborative study with T-Market, a popular Android app market that offers us large-scale ground-truth data. Our study illustrates that the key to successfully developing such systems is multifold, including feature selection and encoding, feature engineering and exposure, app analysis speed and efficacy, developer and user engagement, as well as ML model evolution. Failure in any of the above aspects could lead to the “wooden barrel effect” of the whole system. This article presents our judicious design choices and first-hand deployment experiences in building a practical ML-powered malware detection system. It has been operational at T-Market, using a single commodity server to check ~12K apps every day, and has achieved an overall precision of 98.9 percent and recall of 98.1 percent with an average per-app scan time of 0.9 minutes.

App markets, being crucial and critical for today's mobile ecosystem, have also become a natural malware delivery channel since they actually "lend credibility" to malicious apps. In the past decade, machine learning (ML) techniques have been explored for automated, robust malware detection. Unfortunately, to date, we have yet to see an ML-based malware detection solution deployed at market scales. To better understand the real-world challenges, we conduct a collaborative study with a major Android app market (T-Market) offering us large-scale ground-truth data. Our study shows that the key to successfully developing such systems is manifold, including feature selection/engineering, app analysis speed, developer engagement, and model evolution. Failure in any of the above aspects would lead to the "wooden barrel effect" of the entire system. We discuss our careful design choices as well as our first-hand deployment experiences in building such an ML-powered malware detection system. We implement our design and examine its effectiveness in the T-Market for over one year, using a single commodity server to vet ~ 10K apps every day. The evaluation results show that this design achieves an overall precision of 98% and recall of 96% with an average per-app scan time of 1.3 minutes.

New drug development is an extremely time-consuming and high-risk process. [1]It has been widely valued by the biomedical industry to fully explore the new uses of existing drugs and reorientate them. [2]How to find drug disease with potential therapeutic relationship from a large number of unproven relationship pairs is the research focus of drug reorientation. With the help of machine learning model, we can improve the enrichment degree of potential drug disease relationship pairs, and reduce the false positive rate of prediction. In the past few years, a series of graph based convolutional network models have been developed to calculate the information latent feature representation of nodes and links. Researchers at home and abroad have done a lot of research on network embedding technology based on biomedical data, and have achieved a series of important research results. Among them, the research methods used can be divided into two categories: one is the traditional machine learning algorithm based on artificial feature extraction, the other is the method based on deep learning. For example, kipf and welling [3]proposed a new graph convolution network (GCN) with parts of existing models, DeepDR [4] and DTINet [5] based on node characteristics and their connections, which can be used for node classification. Aiming at the problem of imbalance of drug information data samples, the invention provides a drug relocation method based on deep learning multi-source heterogeneous network. In order to avoid the limitations of traditional feature extraction methods, such as highly dependent on the experience and knowledge of medical staff, strong subjectivity, consuming a lot of time and energy to complete, and extracting high-quality features with distinguishing features often exists In this paper, with the help of graph convolution encoder model and variational auto encoder neural network, we can automatically learn the characteristics of multi-source and heterogeneous drug low-dimensional network, and complete the drug relocation of drug disease association prediction.

Accurate recognition and annotation of the important functional elements in the genome is an important prerequisite to understand the coding mode of complex regulatory networks in the one-dimensional genome. Despite rapid advances in sequencing and recognition technologies, accurately calling non-coding variant effects from large-scale sequence reads remains challenging. Here we present a deep neural network-based algorithmic framework, DeepMSA, which directly learns a regulatory sequence code from large-scale chromatin-profiling data,enabling to evaluate chromatin effects caused by SNP(single nucleotide polymorphism).

Micro finger gesture recognition is an emerging approach to realize more friendly interaction between human and smart devices, especially for small wearable devices, such as smartwatches and virtual reality glasses. This paper proposes airFinger, a novel solution utilizing NIR light sensing to realize both real-time gesture recognition and finger tracking aiming at micro finger gestures. Using a custom NIR-based sensor with novel algorithms to capture subtle finger movements, airFinger enables to detect a rich set of micro finger gestures and track finger movements in terms of scrolling direction, velocity, and displacement. Besides, airFinger is capable of effective noise mitigation, gesture segmentation, and reducing false recognition due to the unintentional actions of users. Extensive experimental results demonstrate that airFinger has robustness against individual diversity, gesture inconsistency, and many other impacts. The overall performance reaches an average accuracy as high as 98.72% over a set of 8 micro finger gestures among 10, 000 gesture samples collected from 10 volunteers.

As the proliferation of backscatter-based applications, exploiting backscatter-based sensing becomes more important. Due to the requirement of accurate estimation of backscatter channels (phase and amplitude), which is often distorted when multiple signals collide with each other, existing works are generally limited to either parallel decoding of collided signals or with non-collided signals only. Motivated by our observation that a channel can be distorted during collisions, the movements of the ON-OFF Keying modulated signal still preserve channel properties of the respective tags, we propose the first approach to channel estimation of parallel 2backscattered signals, called Fireworks. We model the relationship between the channel and the signal moving trajectory in the In-phase and Quadrature (IQ) domain and implement this design in our lab. The results show that Fireworks is able to estimate up to five channels in parallel. When applied to the tracking application, Fireworks achieves 2~4× improvement in the tracking accuracy, compared with the state-of-the-art approach.

Vibration measurement is a crucial task in industrial systems, where vibration characteristics reflect the health and indicate anomalies of the objects. Previous approaches either work in an intrusive manner or fail to capture the micrometer-level vibrations. In this work, we propose mmVib, a practical approach to measure micrometer-level vibrations with mmWave radar. By introducing a Multi-Signal Consolidation (MSC) model to describe the properties of the reflected signals, we exploit the inherent consistency among those signals to accurately recover the vibration characteristics. We implement a prototype of mmVib, and the experiments show that this design achieves 8.2% relative amplitude error and 0.5% relative frequency error in median. Typically, the median amplitude error is 3.4um for the 100um-amplitude vibration. Compared to two existing approaches, mmVib reduces the 80th-percentile amplitude error by 62.9% and 68.9% respectively.

Almost every Android user has unsatisfying experiences regarding responsiveness, in particular Application Not Responding (ANR) and System Not Responding (SNR) that directly disrupt user experience. Unfortunately, the community have limited understanding of the prevalence, characteristics, and root causes of unresponsiveness. In this paper, we make an in-depth study of ANR and SNR at scale based on fine-grained system-level traces crowdsourced from 30,000 Android systems. We find that ANR and SNR occur prevalently on all the studied 15 hardware models, and better hardware does not seem to relieve the problem. Moreover, as Android evolves from version 7.0 to 9.0, there are fewer ANR events but more SNR events. Most importantly, we uncover multifold root causes of ANR and SNR and pinpoint the largest inefficiency which roots in Android's flawed implementation of Write Amplification Mitigation (WAM). We design a practical approach to eliminating this largest root cause; after large-scale deployment, it reduces almost all (>99%) ANR and SNR caused by WAM while only decreasing 3% of the data write speed. In addition, we document important lessons we have learned from this study, and have also released our measurement code/data to the research community.

LoRa, a representative Low-Power Wide Area Network (LPWAN) technology, has been shown as a promising platform to connect Internet of Things. Practical LoRa deployments, however, suffer from collisions, especially in dense networks and wide coverage areas expected by LoRa applications. Existing collision resolution approaches do not exploit the coding properties of LoRa and thus cannot work well for low SNR LoRa signals. We propose NScale to decompose concurrent transmissions by leveraging subtle inter-packet time offsets for low SNR LoRa collisions. NScale (1) translates subtle time offsets, which are vulnerable to noise, to robust frequency features, and (2) further amplifies the time offsets by non-stationary signal scaling, i.e., scaling the amplitude of a symbol differently at different positions. In practical implementation, we propose a noise resistant iterative symbol recovery method to combat symbol distortion in low SNR, and address frequency shifts incurred by CFO and packet time offsets in decoding. We theoretically show that NScale introduces < 1.7 dB SNR loss compared with the original LoRa. We implement NScale on USRP N210 and evaluate its performance in both indoor and outdoor networks. NScale is implemented in software at the gateway and can work for COTS LoRa nodes without any modification. The evaluation results show that NScale improves the network throughput by 3.3x for low SNR collided signals compared with other state-of-the-art methods.

Today's smart devices like fitness tracker, smartwatch, etc., often employ Bluetooth Low Energy (BLE) for data transmission. Such devices thus become our information portal, e.g., SMS message and notifications are delivered to those devices through BLE. In this study, we present BlueDoor, which can obtain unauthorized information from smart devices via BLE vulnerability. We thoroughly examine the BLE protocol, and leverage its intrinsic properties designed for low-cost embedded and wearable devices to bypass the encryption and authentication in BLE. By mimicking a low capacity device to downgrade the process of encryption key negotiation and authentication, BlueDoor can enforce a new key with the peripheral BLE device and pass the authentication without user participation. As a result, BlueDoor can extract BLE packets as well as read/write stored data on BLE devices. We show that BlueDoor works well on the fundamental design tradeoff of using BLE on diverse embedded and wearable devices, and thus can be generalized to various BLE devices. We implement the BlueDoor design and examine its performance on 15 COTS BLE enabled smart devices, including fitness trackers, smartwatch, smart bulb, etc. The results show that BlueDoor can break the information flow and obtain different types of information (e.g., SMS message, notifications) delivered to BLE devices. In addition to privacy threats, this further means traditional operations such as using SMS for verification in widely adopted authentication, are insecure.

Backscatter communication holds potential for ubiquitous and low-cost connectivity among low-power IoT devices. To avoid interference between the carrier signal and the backscatter signal, recent works propose a frequency-shifting technique to separate these two signals in the frequency domain. Such proposals, however, have to occupy the precious wireless spectrum that is already overcrowded, and increase the power, cost, and complexity of the backscatter tag. In this paper, we revisit the classic ON-OFF Keying (OOK) modulation and propose Aloba, a backscatter system that takes the ambient LoRa transmissions as the excitation and piggybacks the in-band OOK modulated signals over the LoRa transmissions. Our design enables the backsactter signal to work in the same frequency band of the carrier signal, meanwhile achieving good tradeoff between transmission range and link throughput. The key contributions of Aloba include: i) the design of a low-power backscatter tag that can pick up the ambient LoRa signals from other signals; ii) a novel decoding algorithm to demodulate both the carrier signal and the backscatter signal from their superposition. The design of Aloba completely unleashes the backscatter tag's ability in OOK modulation and achieves flexible data rate at different transmission range. We implement Aloba and conduct head-to-head comparison with the state-of-the-art LoRa backscatter system PLoRa in various settings. The experiment results show Aloba can achieve 39.5--199.4 Kbps data rate at various distances, 10.4--52.4X higher than PLoRa.

The widespread adoption and ubiquity of smart devices equipped with microphones (e.g., cellphones, smartwatches, etc.) unfortunately create many significant privacy risks. In recent years, there have been several cases of people's conversations being secretly recorded, sometimes initiated by the device itself. Although some manufacturers are trying to protect users' privacy, to the best of our knowledge, there is not any effective technical solution available. In this work, we present Patronus, a system that can both prevent unauthorized devices from making secret recordings while allowing authorized devices to record conversations. Patronus prevents unauthorized speech recording by emitting what we call a scramble, a low-frequency noise generated by inaudible ultrasonic waves. The scramble prevents unauthorized recordings by leveraging the nonlinear effects of commercial off-the-shelf microphones. The frequency components of the scramble are randomly determined and connected with linear chirps, and the frequency period is fine-tuned so that the scramble pattern is hard to attack. Patronus allows authorized speech recording by secretly delivering the scramble pattern to authorized devices, which can use an adaptive filter to cancel out the scramble. We implement a prototype system and conduct comprehensive experiments. Our results show that only 19.7% of words protected by Patronus' scramble can be recognized by unauthorized devices. Furthermore, authorized recordings have 1.6x higher perceptual evaluation of speech quality (PESQ) score and, on average, 50% lower speech recognition error rates than unauthorized recordings.

Wi-Fi imaging has attracted significant interests due to the ubiquitous availability of Wi-Fi devices today. In this paper, we present Wi-Fi See It All (WiSIA), a versatile Wi-Fi imaging system built upon commercial off-the-shelf (COTS) Wi-Fi devices, which is able to simultaneously detect objects and humans, segment their boundaries, and identify them within the image plane. To achieve this, WiSIA utilizes three techniques. First, instead of constructing the image plane at the receiver side using a high-cost antenna array and complex parameter estimation, WiSIA pushes the image plane to the object side with two pairs of transceivers and 2D-IFFT. Second, WiSIA extracts the specific physical signature of the signals reflected from multiple objects to segment their boundaries. Third, WiSIA incorporates a cGAN (conditional Generative Adversarial Network) to enhance the boundary of different objects. We have implemented WiSIA using COTS Wi-Fi devices and evaluated it using a rich set of experiments. Our results demonstrate the efficacy of WiSIA. It outperforms the state-of-the-art vision-based method in dark and occlusion scenarios, demonstrating its superiority in such challenge scenarios.

Sound recognition is an important and popular function of smart devices. The location of sound is basic information associated with the acoustic source. Apart from sound recognition, whether the acoustic sources can be localized largely affects the capability and quality of the smart device's interactive functions. In this work, we study the problem of concurrently localizing multiple acoustic sources with a smart device (e.g., a smart speaker like Amazon Alexa). The existing approaches either can only localize a single source, or require deploying a distributed network of microphone arrays to function. Our proposal called Symphony is the first approach to tackle the above problem with a single microphone array. The insight behind Symphony is that the geometric layout of microphones on the array determines the unique relationship among signals from the same source along the same arriving path, while the source's location determines the DoAs (direction-of-arrival) of signals along different arriving paths. Symphony therefore includes a geometry-based filtering module to distinguish signals from different sources along different paths and a coherence-based module to identify signals from the same source. We implement Symphony with different types of commercial off-the-shelf microphone arrays and evaluate its performance under different settings. The results show that Symphony has a median localization error of 0.694m, which is 68% less than that of the state-of-the-art approach.

The development of Internet of Things calls for ubiquitous and low-cost localization and posture estimation. We present LiTag, a visible light based localization and posture estimation solution with COTS cameras. The core of LiTag is based on the design of a chip-less and battery-less optical tag which can show different color patterns from different observation directions. After capturing a photo containing the tag, LiTag can calculate the tag position and posture by combining the color pattern and the geometry relation between the camera image plane and the real world. Unlike existing marker-based visible localization and posture estimation approaches, LiTag can work with a single camera without calibration, which significantly reduces the calibration overhead and deployment costs. We implement LiTag and evaluate its performance extensively. Results show that LiTag can provide the tag position with a median error of 1.6 cm in the 2D plane, a median error of 12 cm in the 3D space, and posture estimation with a median error of 1°. We believe that LiTag has a high potential to provide a low-cost and easy-to-use solution for ubiquitous localization and posture estimation with existing widely deployed cameras.

Brain-computer interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions. Electroencephalography (EEG)-based BCI is one of the promising solutions due to its convenient and portable instruments. Despite the extensive research of EEG in recent years, it is still challenging to interpret EEG signals effectively due to its nature of noise and difficulties in capturing the inconspicuous relations between EEG signals and specific brain activities. Most existing works either only consider EEG as chain-like sequences while neglecting complex dependencies between adjacent signals or requiring complex preprocessing. In this paper, we introduce two deep learning-based frameworks with novel spatio-temporal preserving representations of raw EEG streams to precisely identify human intentions. The two frameworks consist of both convolutional and recurrent neural networks effectively exploring the preserved spatial and temporal information in either a cascade or a parallel manner. Extensive experiments on a large scale movement intention EEG dataset (108 subjects, 3 145 160 EEG records) have demonstrated that the proposed frameworks achieve high accuracy of 98.3% and outperform a set of state-of-the-art and baseline models. The developed models are further evaluated with a real-world brain typing BCI and achieve a recognition accuracy of 93% over five instruction intentions suggesting good generalization over different kinds of intentions and BCI systems.

Biometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, traditional biometric authentication systems (e.g., face recognition, iris, retina, voice, and fingerprint) are at increasing risks of being tricked by biometric tools such as anti-surveillance masks, contact lenses, vocoder, or fingerprint films. In this article, we design a multimodal biometric authentication system named DeepKey, which uses both Electroencephalography (EEG) and gait signals to better protect against such risk. DeepKey consists of two key components: an Invalid ID Filter Model to block unauthorized subjects, and an identification model based on attention-based Recurrent Neural Network (RNN) to identify a subject’s EEG IDs and gait IDs in parallel. The subject can only be granted access while all the components produce consistent affirmations to match the user’s proclaimed identity. We implement DeepKey with a live deployment in our university and conduct extensive empirical experiments to study its technical feasibility in practice. DeepKey achieves the False Acceptance Rate (FAR) and the False Rejection Rate (FRR) of 0 and 1.0%, respectively. The preliminary results demonstrate that DeepKey is feasible, shows consistent superior performance compared to a set of methods, and has the potential to be applied to the authentication deployment in real-world settings.

Accurate forecast of citywide crowd flows on flexible region partition benefits urban planning, traffic management, and public safety. Previous research either fails to capture the complex spatiotemporal dependencies of crowd flows or is restricted on grid region partition that loses semantic context. In this paper, we propose DeepFlowFlex, a graph-based model to jointly predict inflows and outflows for each region of arbitrary shape and size in a city. Analysis on cellular datasets covering 2.4 million users in China reveals dependencies and distinctive patterns of crowd flows in not only the conventional space and time domains, but also the speed domain, due to the diverse transportation modes in the mobility data. DeepFlowFlex explicitly groups crowd flows with respect to speed and time, and combines graph convolutional long short-term memory networks and graph convolutional neural networks to extract complex spatiotemporal dependencies, especially long-term and long-distance inter-region dependencies. Evaluations on two big cellular datasets and public GPS trace datasets show that DeepFlowFlex outperforms the state-of-the-art deep learning and big-data-based methods on both grid and non-grid city map partition.

Recent years have witnessed the rapid growth of mobile virtual network operators (MVNOs), which operate on top of existing cellular infrastructures of base carriers, while offering cheaper or more flexible data plans compared to those of the base carriers. In this paper, we present a two-year measurement study towards understanding various fundamental aspects of today's MVNO ecosystem, including its architecture, customers, performance, economics, and the complex interplay with the base carrier. Our study focuses on a large commercial MVNO with one million customers, operating atop a nation-wide base carrier. Our measurements clarify several key concerns raised by MVNO customers, such as inaccurate billing and potential performance discrimination with the base carrier. We also leverage big data analytics, statistical modeling, and machine learning to address the MVNO's key concerns with regard to data usage prediction, data plan reselling, customer churn mitigation, and billing delay reduction. Our proposed techniques can help achieve higher revenues and improved services for commercial MVNOs.

This paper considers malicious link detection in multi-hop wireless sensor networks (WSNs). Existing work on malicious link detection generally requires that the detection process being performed at the intermediate nodes, leading to considerable overhead in system design, as well as unstable detection accuracy due to limited resources and the uncertainty in the loyalty of the intermediate nodes themselves. In this paper, we propose an efficient and robust malicious link detection scheme by exploiting the statistics of packet delivery rates only at the base station. More specifically, first, we present a secure packet transmission protocol to ensure that except the base station, any intermediate nodes on the route cannot access the contents and routing paths of the packets. Second, we design a malicious link detection algorithm that can effectively detect the irregular dropout at every hop (or link) along the routing path. We prove that the proposed algorithm has guaranteed false alarm rate and low miss detection rate. Simulation results are provided to validate the proposed approaches.

With the explosive growth of Internet traffic, multi-source content delivery has been introduced for improving the performance and quality-of-experience (QoE) of Internet services. Upgrading from single-source content delivery to multi-source content delivery, however, may not always lead to a better performance. Instead, a decline in terms of delivery speed often occurs. By conducting a comprehensive study, we show that the underlying reason of this counter-intuitive phenomenon is actually due to the cask effect of data sources at both macro and micro level. Specifically, at the macro level, data sources with different types are highly heterogeneous in terms of delivery performance, which means data sources with certain types are particularly easy to become the "short boards". At the micro level, for the data sources chosen by a client, the high diversity of participation time (DPT) of the sources could impair the acceleration effect. Motivated by the above findings, we design MDR (Multi-source Delivery Redirector), a middleware that contains two optimizations to improve the acceleration effect. One is the feature-greedy selection algorithm which can avoid selecting data sources with inferior types, and the other is the DPT-driven shuffle strategy which can avoid using unstable data sources. Simulation-based experiments show that the MDR outperforms existing approaches in terms of overall downloading performance.

Adaptive bitrate (ABR) algorithms have been critical techniques for high quality-of-experience (QoE) Internet video delivery. Prior work designs ABR algorithms by conducting the overall QoE function of fixed parameters. However, the QoE of end users are diverse and video bitrate may be chosen in a misleading way when leaving out the diversity. State-of-the-art ABR algorithms like MPC, Pensieve utilize off-line modeling techniques and result in performance degradation for online QoE diversity adaption. To address this issue, we propose Elephanta, an online flexible ABR algorithm for edge users which incorporates (1) user QoE perception interface and (2) adaption algorithm with flexible parameters. To avoid overheads for updating parameters online, we model video streaming as a renewal system and formulate specific QoE function into flexible formats by setting constraints on corresponding QoE metrics. To validate parameter setting, we emulate Elephanta under 5 thousand throughput traces including FCC broadband, 3G HSDPA data set from the Internet and 4G/LTE data set collected by ourselves. Accordingly, we implement Elephanta in dash.js at client side for user test. Evaluation results show that Elephanta achieves QoE improvement by 21.1% over MPC, in part for its superior adaptability to QoE diversity.

Electroencephalography (EEG) signals reflect and measure activities in certain brain areas. Its zero clinical risk and easy-to-use features make it a good choice of providing insights into the cognitive process. However, effective analysis of time-varying EEG signals remains challenging. First, EEG signal processing and feature engineering are time-consuming and highly rely on expert knowledge, and most existing studies focus on domain-specific classification algorithms, which may not apply to other domains. Second, EEG signals usually have low signal-to-noise ratios and are more chaotic than other sensor signals. In this regard, we propose a generic EEG-based cognitive activity recognition framework that can adaptively support a wide range of cognitive applications to address the above issues. The framework uses a reinforced selective attention model to choose the characteristic information among raw EEG signals automatically. It employs a convolutional mapping operation to dynamically transform the selected information into a feature space to uncover the implicit spatial dependency of EEG sample distribution. We demonstrate the effectiveness of the framework under three representative scenarios: intention recognition with motor imagery EEG, person identification, and neurological diagnosis, and further evaluate it on three widely used public datasets. The experimental results show our framework outperforms multiple state-of-the-art baselines and achieves competitive accuracy on all the datasets while achieving low latency and high resilience in handling complex EEG signals across various domains. The results confirm the suitability of the proposed generic approach for a range of problems in the realm of brain-computer Interface applications.

Existing indoor navigation solutions usually require pre-deployed comprehensive location services with precise indoor maps and, more importantly, all rely on dedicatedly installed or existed infrastructure. In this paper, we present Pair-Navi, an infrastructure-free indoor navigation system that circumvents all these requirements by reusing a previous traveler's (i.e. leader) trace experience to navigate future users (i.e. followers) in a Peer-to-Peer (P2P) mode. Our system leverages the advances of visual SLAM on commercial smartphones. Visual SLAM systems, however, are vulnerable to environmental dynamics in the precision and robustness and involve intensive computation that prohibits real-time applications. To combat environmental changes, we propose to cull non-rigid contexts and keep only the static and rigid contents in use. To enable real-time navigation on mobiles, we decouple and reorganize the highly coupled SLAM modules for leaders and followers. We implement Pair-Navi on commodity smartphones and validate its performance in three diverse buildings. Our results show that Pair-Navi achieves an immediate navigation success rate of 98.6%, which maintains as 83.4% even after two weeks since the leaders' traces were collected, outperforming the state-of-the-art solutions by >50%. Being truly infrastructure-free, Pair-Navi sheds lights on practical indoor navigations for mobile users.

Cross-Technology Communication (CTC) is an emerging technique that enables direct communication across different wireless technologies. The state-of-the-art works in this area propose physical-level CTC, in which the transmitters emulate signals that follow the receiver's standard. Physical-level CTC means considerable processing complexity at the transmitter, which doesn't apply to the communication from a low-end transmitter to a high-end receiver, e.g. from ZigBee to WiFi. This paper presents transmitter-transparent cross-technology communication, which leaves the processing complexity solely at the receiver side and therefore makes a critical advance toward bidirectional high-throughput CTC. We implement our proposal as LEGO-Fi, the communication from ZigBee to WiFi. The key technique inside is cross-demapping, which stems from two key technical insights: (1) A ZigBee packet leaves distinguishable features when passing the WiFi modules. (2) Compared to ZigBee's simple encoding and modulation schemes, the rich processing capacity of WiFi offers extra flexibility to process a ZigBee packet. The evaluation results show that LEGO-Fi achieves a throughput of 213.6Kbps, which is respectively 13000× and 1200× faster than FreeBee and ZigFi, the two existing ZigBee-to-WiFi CTC approaches.

Liquid leakage detection is a crucial issue in modern industry, which concerns industrial safety. Traditional solutions, which generally rely on specialized sensors, suffer from intrusive deployment, high cost, and high power consumption. Such problems prohibit applying those solutions for large-scale and continuously industrial monitoring. In this work, we present a RFID-based solution, TwinLeak, to detect liquid leakage using COTS RFID devices. Detecting the leakage accurately with coarse-grained RSSI and phase readings of tags has been a daunting task, which is especially challenging when low detection delay is required. Our system achieves these goals based on the fact that the inductive coupling between two adjacent tags is highly sensitive to the liquid leaked between them. Therefore, instead of judging according to the signals of each individual tag, TwinLeak utilizes the relationship between the signals of two tags as an effective feature for leakage detection. Specifically, Twin-Leak extracts discriminative signal features from short segments of signals and instantly identifies leakage using a light-weight classifier. A model-guided method for leakage progress tracking is further devised to simultaneously estimate the leakage volume and rate. We implement TwinLeak, evaluate its performance across various scenarios, and deploy it in a real-world industrial IoT system. In average, TwinLeak achieves a TPR higher than 97.2%, a FPR lower than 0.5%, and a relative property estimation error around 10%, while triggering early alarms after only about 4.6mL liquid leaks.

Understanding customer mobility patterns to commercial districts is crucial for urban planning, facility management, and business strategies. Trade areas are a widely applied measure to quantity where the visitors are from. Traditional trade area analysis is limited to small-scale or store-level studies because information such as visits to competitor commercial entities and place of residence is collected by labour-intensive questionnaires or heavily biased location-based social media data. In this paper, we propose CellTradeMap, a novel district-level trade area analysis framework using mobile flow records (MFRs), a type of fine-grained cellular network data. CellTradeMap extracts robust location information from the irregularly sampled, noisy MFRs, adapts the generic trade area analysis framework to incorporate cellular data, and enhances the original trade area model with cellular-based features. We evaluate CellTradeMap on a large-scale cellular network dataset covering 3.5 million mobile phone users in a metropolis in China. Experimental results show that the trade areas extracted by CellTradeMap are aligned with domain knowledge and CellTradeMap can model trade areas with a high predictive accuracy.

RFID tracking has attracted significant interest from both academia and industry due to its low cost and ease of deployment. Previous works focus more on tracking in 2D space or separately consider tracking of the location and the orientation. They especially struggle in 3D situations due to the increase in the degree of freedom and the limited information conveyed by the RFID tags. In this paper, we propose 3D-OmniTrack, an approach that can accurately track the 3D location and orientation of an object. We introduce a polarization-sensitive phase model in an RFID system, which takes into consideration both the distance and the 3D posture of an object. Based on this model, we design an algorithm to accurately track the object in 3D space. We conduct real-world experiments and present results that show 3D-OmniTrack can achieve centimeter-level location accuracy with the average orientation error of 5°. 3D-OmniTrack has significant advantages in both the accuracy and the efficiency, compared with state-of-the-art approaches.

With the popularization of advanced cellular networks, mobile video occupies nearly three quarters of cellular network traffic. While previous adaptive bitrate (ABR) algorithms perform well under broadband network, their performance degrades in cellular networks due to throughput fluctuation. Through real world 4G/LTE network measurement, we find that throughput in cellular networks exhibits high fluctuation. It follows Markov behaviors with different states and different transition probability among states. We further find that the transition probability is stable along time but varies significantly under different environments. This inspires us to design ABR algorithms by improving throughput prediction in cellular networks. We propose NEIVA, a network environment identification based video bitrate adaption method in cellular networks. NEIVA trains a network environment identifier based on throughput data and trains a hidden Markov model (HMM) based throughput predictor for different environments. In online video bitrate selection, NEIVA utilizes the environment identifier to select the model for corresponding environment. Then NEIVA predicts future network performance by combining offline model and online throughput data. We implement NEIVA with MPC and evaluate it in real environment. The evaluation results show that with manually identifying environment, NEIVA improves 20% -- 25% bandwidth prediction accuracy and 11% -- 20% QoE improvement over the baseline predictors. With online environment identification, online NEIVA achieves 3.8% and 11.1% average QoE improvement over MPC and HMM, respectively.

Playing Android games on Windows x86 PCs has gained enormous popularity in recent years, and the de facto solution is to use mobile emulators built with the AOVB (Android-x86 On VirtualBox) architecture. When playing heavy 3D Android games with AOVB, however, users often suffer unsatisfactory smoothness due to the considerable overhead of full virtualization. This paper presents DAOW, a game-oriented Android emulator implementing the idea of direct Android emulation, which eliminates the overhead of full virtualization by directly executing Android app binaries on top of x86-based Windows. Based on pragmatic, efficient instruction rewriting and syscall emulation, DAOW offers foreign Android binaries direct access to the domestic PC hardware through Windows kernel interfaces, achieving nearly native hardware performance. Moreover, it leverages graphics and security techniques to enhance user experiences and prevent cheating in gaming. As of late 2018, DAOW has been adopted by over 50 million PC users to run thousands of heavy 3D Android games. Compared with AOVB, DAOW improves the smoothness by 21% on average, decreases the game startup time by 48%, and reduces the memory usage by 22%.

Playing Android games with Windows x86 PCs is now popular, and the common solution is to use mobile emulators built with the AOVB (Android-x86 On VirtualBox) architecture. Nevertheless, running heavy 3D Android games on AOVB incurs considerable overhead of full virtualization, thus often leading to unsatisfactory smoothness. To tackle this issue, we present DAOW, a commercial game-oriented Android emulator implementing the idea of direct Android emulation, which eliminates the overhead of full virtualization by providing foreign Android binaries with direct access to the domestic PC hardware through Windows kernel interfaces. In this demo, we will demonstrate that DAOW essentially outperforms traditional AOVB-based emulators in terms of running smoothness, game startup time, and memory usage.

With the wide adoption, Linux-based IoT devices have emerged as one primary target of today's cyber attacks. Traditional malware-based attacks can quickly spread across these devices, but they are well-understood threats with effective defense techniques such as malware fingerprinting and community-based fingerprint sharing. Recently, fileless attacks---attacks that do not rely on malware files---have been increasing on Linux-based IoT devices, and posing significant threats to the security and privacy of IoT systems. Little has been known in terms of their characteristics and attack vectors, which hinders research and development efforts to defend against them. In this paper, we present our endeavor in understanding fileless attacks on Linux-based IoT devices in the wild. Over a span of twelve months, we deploy 4 hardware IoT honeypots and 108 specially designed software IoT honeypots, and successfully attract a wide variety of real-world IoT attacks. We present our measurement study on these attacks, with a focus on fileless attacks, including the prevalence, exploits, environments, and impacts. Our study further leads to multi-fold insights towards actionable defense strategies that can be adopted by IoT vendors and end users.

Recent years have witnessed the rapid growth of mobile virtual network operators (MVNOs), which operate on top of the existing cellular infrastructures of base carriers while offering cheaper or more flexible data plans compared to those of the base carriers. In this paper, we present a nearly two-year measurement study towards understanding various key aspects of today's MVNO ecosystem, including its architecture, performance, economics, customers, and the complex interplay with the base carrier. Our study focuses on a large commercial MVNO with \reviseabout 1 million customers, operating atop a nation-wide base carrier. Our measurements clarify several key concerns raised by MVNO customers, such as inaccurate billing and potential performance discrimination with the base carrier. We also leverage big data analytics and machine learning to optimize an MVNO's key businesses such as data plan reselling and customer churn mitigation. Our proposed techniques can help achieve %will lead to higher revenues and improved services for commercial MVNOs.

As a key UI feature of Android, overlay enables one app to draw over other apps by creating an extra View layer on top of the host View. While greatly facilitating user interactions with multiple apps at the same time, it is often exploited by malicious apps (malware) to attack users. To combat this threat, prior countermeasures concentrate on restricting the capabilities of overlays at the OS level, while barely seeing adoption by Android due to the concern of sacrificing overlays' usability. To address this dilemma, a more pragmatic approach is to enable the early detection of overlay-based malware at the app market level during the app review process, so that all the capabilities of overlays can stay unchanged. Unfortunately, little has been known about the feasibility and effectiveness of this approach for lack of understanding of malicious overlays in the wild. To fill this gap, in this paper we perform the first large-scale comparative study of overlay characteristics in benign and malicious apps using static and dynamic analyses. Our results reveal a set of suspicious overlay properties strongly correlated with the malice of apps, including several novel features. Guided by the study insights, we build OverlayChecker, a system that is able to automatically detect overlay-based malware at market scales. OverlayChecker has been adopted by one of the world's largest Android app stores to check around 10K newly submitted apps per day. It can efficiently (within 2 minutes per app) detect nearly all (96%) overlay-based malware using a single commodity server.

Wi-Fi based sensing systems, although sound as being deployed almost everywhere there is Wi-Fi, are still practically difficult to be used without explicit adaptation efforts to new data domains. Various pioneering approaches have been proposed to resolve this contradiction by either translating features between domains or generating domain-independent features at a higher learning level. Still, extra training efforts are necessary in either data collection or model re-training when new data domains appear, limiting their practical usability. To advance cross-domain sensing and achieve fully zero-effort sensing, a domain-independent feature at the lower signal level acts as a key enabler. In this paper, we propose Widar3.0, a Wi-Fi based zero-effort cross-domain gesture recognition system. The key insight of Widar3.0 is to derive and estimate velocity profiles of gestures at the lower signal level, which represent unique kinetic characteristics of gestures and are irrespective of domains. On this basis, we develop a one-fits-all model that requires only one-time training but can adapt to different data domains. We implement this design and conduct comprehensive experiments. The evaluation results show that without re-training and across various domain factors (i.e. environments, locations and orientations of persons), Widar3.0 achieves 92.7% in-domain recognition accuracy and 82.6%-92.4% cross-domain recognition accuracy, outperforming the state-of-the-art solutions. To the best of our knowledge, Widar3.0 is the first zero-effort cross-domain gesture recognition work via Wi-Fi, a fundamental step towards ubiquitous sensing.

Self-tracking at 6 degrees of freedom in real-time is essential in lots of emerging applications such as VR/AR/MR simulation, indoor navigation, and so on. With the development of built-in sensors in smartphones, many self-tracking solutions have appeared. Many researchers try to utilize vision-based approaches combined with an Inertial Measure Unit (IMU) to realize self-tracking with smartphones. After testing these approaches, however, we find that tracking would be lost in four such common scenarios: 1) When the IMU rotates fast or for a long period of time, it will cause serious delays in orientation tracking; 2) The scenes where background features are not distinct enough; 3) When the smartphone moves fast, image features become quite different in successive frames; 4) Unstructured scenes where background features are not static. To address these issues, we propose iTracker, which utilizes Real-time Step-Length Adaption Algorithm to solve the scenario (1) and a Parallel-Multi-State Local Recovery method to deal with scenarios (2)-(4). Extensive experiments show that iTracker realizes robust and accurate self-tracking in these four scenarios with an error of 0.7% throughout the whole trajectory.

A brain-computer interface (BCI) acquires brain signals, analyzes, and translates them into commands that are relayed to actuation devices for carrying out desired actions. With the widespread connectivity of everyday devices realized by the advent of the Internet of Things (IoT), BCI can empower individuals to directly control objects such as smart home appliances or assistive robots, directly via their thoughts. However, realization of this vision is faced with a number of challenges, most importantly being the issue of accurately interpreting the intent of the individual from the raw brain signals that are often of low fidelity and subject to noise. Moreover, preprocessing brain signals and the subsequent feature engineering are both time-consuming and highly reliant on human domain expertise. To address the aforementioned issues, in this paper, we propose a unified deep learning-based framework that enables effective human-thing cognitive interactivity in order to bridge individuals and IoT objects. We design a reinforcement learning-based selective attention mechanism (SAM) to discover the distinctive features from the input brain signals. In addition, we propose a modified long short-term memory to distinguish the interdimensional information forwarded from the SAM. To evaluate the efficiency of the proposed framework, we conduct extensive real-world experiments and demonstrate that our model outperforms a number of competitive state-of-the-art baselines. Two practical real-time human-thing cognitive interaction applications are presented to validate the feasibility of our approach.

Conventional spinning inspection systems, equipped with separated sensors (e.g., accelerometer, laser, etc.) and communication modules, are either very expensive and/or suffering from occlusion and narrow field of view. The recently proposed RFID-based sensing solution draws much attention due to its intriguing features, such as being cost-effective, applicable to occluded objects, auto-identification, etc. However, this solution only works in quiet settings where both the reader and spinning object remain absolutely stationary, as their shaking would ruin the periodicity and sparsity of the spinning signal, making it impossible to be recovered. To overcome such limitation, this work introduces Tagtwins, a robust spinning sensing system that can work in noisy settings. It addresses the challenge by attaching dual RFID tags on the spinning surface and developing a new formulation of spinning signal that is shaking-resilient, even if the shaking involves unknown trajectories. Our main contribution lies in two newly developed techniques. First, we propose relative spinning signal using dual tags' readings and analytically demonstrate its feasibility in various settings. Second, we introduce dual compressive reading to inspect high-frequency spinning with relatively low reading rate of RFIDs. We have implemented Tagtwins with commercial RFID devices and evaluated it extensively. Experimental results show that Tagtwins can inspect the rotation frequency with high accuracy and robustness.

Radio-frequency identification (RFID) systems, as major enablers of automatic identification, are currently supplemented with various interesting sensing functions, e.g., motion tracking. All these sensing applications forcedly require much higher reading rate (i.e., sampling rate) such that any fast movement of tagged objects can be accurately captured in a timely manner through tag readings. However, COTS RFID systems suffer from an extremely low individual reading rate when multiple tags are present, due to their intense channel contention in the link layer. In this work, we present a holistic system, called Tagwatch, a rate-adaptive reading system for COTS RFID devices. This work revisits the reading rate from a distinctive perspective: mobility. We observe that the reading demands of mobile tags are considerably more urgent than those of stationary tags because the states of the latter nearly remain unchanged; meanwhile, only a few tags (e.g., <; 20%) are actually in motion despite the existence of a massive amount of tags in practice. Thus, Tagwatch adaptively improves the reading rates for mobile tags by cutting down the readings of stationary tags. Our main contribution is a two-phase reading design, wherein the mobile tags are discriminated in the Phase I and exclusively read in the Phase II. We built a prototype of Tagwatch with COTS RFID readers and tags. Results from our microbenchmark analysis demonstrate that the new design outperforms the reading rate by 3:2× when 5 percent of tags are moving.

Understanding and predicting cellular traffic at large-scale and fine-granularity is beneficial and valuable to mobile users, wireless carriers, and city authorities. Predicting cellular traffic in modern metropolis is particularly challenging because of the tremendous temporal and spatial dynamics introduced by diverse user Internet behaviors and frequent user mobility citywide. In this paper, we characterize and investigate the root causes of such dynamics in cellular traffic through a big cellular usage dataset covering 1.5 million users and 5,929 cell towers in a major city of China. We reveal intensive spatio-temporal dependency even among distant cell towers, which is largely overlooked in previous works. To explicitly characterize and effectively model the spatio-temporal dependency of urban cellular traffic, we propose a novel decomposition of in-cell and inter-cell data traffic, and apply a graph-based deep learning approach to accurate cellular traffic prediction. Experimental results demonstrate that our method consistently outperforms the state-of-the-art time-series based approaches and we also show through an example study how the decomposition of cellular traffic can be used for event inference.

The wide adoption of smart devices with onboard cameras facilitates photo capturing and sharing, but greatly increases people's concern on privacy infringement. Here, we seek a solution to respect the privacy of persons being photographed in a smarter way that they can be automatically erased from photos captured by smart devices according to their intention. To make this work, we need to address three challenges: 1) how to enable users explicitly express their intentions without wearing any visible specialized tag, and 2) how to associate the intentions with persons in captured photos accurately and efficiently. Furthermore, 3) the association process itself should not cause portrait information leakage and should be accomplished in a privacy-preserving way. In this work, we design, develop, and evaluate a system, called COIN (Cloak Of INvisibility), that enables a user to flexibly express her privacy requirement and empowers the photo service provider (or image taker) to exert the privacy protection policy. Leveraging the visual distinguishability of people in the field-of-view and the dimension-order-independent property of vector similarity measurement, COIN achieves high accuracy and low overhead. We implement a prototype system, and our evaluation results on both the trace-driven and real-life experiments confirm the feasibility and efficiency of our system.

Fake base station (FBS) crime is a type of wireless communication crime that has appeared recently. The key to enforcing the laws on regulating FBS based crime is not only to arrest but also to convict criminals effectively. Much work on FBS discovering, localization, and tracking can assist the arresting, but the problem of collecting evidence accurately to support a proper conviction has not been addressed yet.

The past years have witnessed the rapid conceptualization and development of wireless sensing based on Channel State Information (CSI) with commodity WiFi devices.Many research efforts have been devoted to promote WiFi sensing by innovating applications, refining models and optimizing algorithms. A critical issue of Cross-Technology Interference (CTI), however, is surprisingly unnoticed and largely unexplored in the existing literature. In this paper, we demonstrate that CTI poses severe impacts on CSI measurements and further degrades the performance of CSI-based sensing. Based on in-depth understanding of such impacts, we present PERFIC to deal with CTI for CSI on commercial WiFi. We first exploit the inherent cyclostationarity property of different signals to detect CTI and further identify the specific distorted subcarriers on CSI. For each interfered CSI, we then propose to mitigate the impacts of CTI by amending the abnormal subcarriers. We conduct experiments on typical wireless sensing applications, including human detection and activity classification, using off-the-shelf WiFi devices. The results demonstrate that PERFIC yields a remarkable performance gain of >30% with high efficiency and outperforms existing robust classifiers.By providing interference-free CSI that is amendable to existing and emerging CSI-based sensing applications, PERFIC underpins new insights for improving the sensitivity and reliability of wireless sensing.

Vital signs such as heart rate and heartbeat interval are currently measured by electrocardiograms (ECG) or wearable physiological monitors. These techniques either require contact with the patient's skin or are usually uncomfortable to wear, rendering them too expensive and user-unfriendly for daily monitoring. In this paper, we propose a new noninvasive technology to generate an Acousticcardiogram (ACG) that precisely monitors heartbeats using inaudible acoustic signals. ACG uses only commodity microphones and speakers commonly equipped on ubiquitous off-the-shelf devices, such as smartphones and laptops. By transmitting an acoustic signal and analyzing its reflections off human body, ACG is capable of recognizing the heart rate as well as heartbeat rhythm. We employ frequency-modulated sound signals to separate reflection of heart from that of background motions and breath, and continuously track the phase changes of the acoustic data. To translate these acoustic data into heart and breath rates, we leverage the dual microphone design on COTS mobile devices to suppress direct echo from speaker to microphones, identify heart rate in frequency domain, and adopt an advanced algorithm to extract individual heartbeats. We implement ACG on commercial devices and validate its performance in real environments. Experimental results demonstrate ACG monitors user's heartbeat accurately, with median heart rate estimation error of 0.6 beat per minute (bpm), and median heartbeat interval estimation error of 19 ms.

Conventional spinning inspection systems, equipped with separated sensors (e.g., accelerometer, laser, etc.) and communication modules, are either very expensive and/or suffering from occlusion and narrow field of view. The recently proposed RFID-based sensing solution draws much attention due to its intriguing features, such as being cost-effective, applicable to occluded objects and auto-identification, etc. However, this solution only works in quiet settings where the reader and spinning object remain absolutely stationary, as their shaking would ruin the periodicity and sparsity of the spinning signal, making it impossible to be recovered. This work introduces Tagtwins, a robust spinning sensing system that can work in noisy settings. It addresses the challenge by attaching dual RFID tags on the spinning surface and developing a new formulation of spinning signal that is shaking-resilient, even if the shaking involves unknown trajectories. Our main contribution lies in two newly developed techniques, relative spinning signal and dual compressive reading. We analytically demonstrate that our solution can work in various settings. We have implemented Tagtwins with COTS RFID devices and evaluated it extensively. Experimental results show that Tagtwins can inspect the rotation frequency with high accuracy and robustness.

Acoustic motion tracking has been viewed as a promising user interaction technique in many scenarios such as Virtual Reality (VR), Smart Appliance, video gaming, etc. Existing acoustic motion tracking approaches, however, suffer from long window of accumulated signal and time-consuming signal processing. Consequently, they are inherently difficult to achieve both high accuracy and low delay. We propose Vernier, an efficient and accurate acoustic tracking method on commodity mobile devices. In the heart of Vernier lies a novel method to efficiently and accurately derive phase change and thus moving distance. Vernier significantly reduces the tracking delay/overhead by removing the complicated frequency analysis and long window of signal accumulation, while keeping a high tracking accuracy. We implement Vernier on Android, and evaluate its performance on COTS mobile devices including Samsung Galaxy S7 and Sony L50t. Evaluation results show that Vernier outperforms previous approaches with a tracking error less than 4 mm. The tracking speed achieves 3×improvement to existing phase based approaches and 10×to Doppler Effect based approaches. Vernier is also validated in applications like controlling and drawing, and we believe it is generally applicable in many real applications.

Eccentricity detection is a crucial issue for highspeed rotating machinery, which concerns the stability and safety of the machinery. Conventional techniques in industry for eccentricity detection are mainly based on measuring certain physical indicators, which are costly and hard to deploy. In this paper, we propose RED, a non-intrusive, low-cost, and realtime RFID-based eccentricity detection approach. Differing from the existing RFID-based sensing approaches, RED utilizes the temporal and phase distributions of tag readings as effective features for eccentricity detection. RED includes a Markov chain based model called RUM, which only needs a few sample readings from the tag to make a highly accurate and precise judgement. We implement RED with commercial-of-the-shelf RFID reader and tags, and evaluate its performance across various scenarios. The overall accuracy is 93.59% and the detection latency is 0.68 seconds in average.

RFID tracking attracts a lot of research efforts in recent years. Most of the existing approaches, however, adopt an orientation-oblivious model. When tracking a target whose orientation changes, those approaches suffer from serious accuracy degradation. In order to achieve target tracking with pervasive applicability in various scenarios, we in this paper propose OmniTrack, an orientation-aware RFID tracking approach. Our study discovers the linear relationship between the tag orientation and the phase change of the backscattered signals. Based on this finding, we propose an orientation-aware phase model to explicitly quantify the respective impact of the read-tag distance and the tags orientation. OmniTrack addresses practical challenges in tracking the location and orientation of a mobile tag. Our experimental results demonstrate that OmniTrack achieves centimeterlevel location accuracy and has significant advantages in tracking targets with varing orientations, compared to the state-of-the-art approaches.

This paper reveals the performance anomaly (i.e., the decline of delivery speed) when the client upgrades a task from single-source content delivery to multi-source content delivery. This anomaly is mainly caused by two aspects: (1) data sources with different types vary greatly in terms of acceleration reward (AR), and data sources with certain types are particularly easy to become inferior; (2) When the data sources remain fixed for a period of time, the large diversity of participant time (DPT) of data sources disturb the acceleration and the data sources with less participant time are inferior. Combing these insights, we figure out that the multi-source content delivery is limited by the so-called cask effect, i.e., the acceleration effect mainly depends on the inferior data sources.

Indoor localization gains increasingly attentions in the era of Internet of Things. Among various technologies, WiFi-based systems that leverage Received Signal Strengths (RSSs) as location fingerprints become the mainstream solutions. However, RSS fingerprints suffer from critical drawbacks of spatial ambiguity and temporal instability that root in multipath effects and environmental dynamics, which degrade the performance of these systems and therefore impede their wide deployment in real world. Pioneering works overcome these limitations at the costs of ubiquity as they mostly resort to additional information or extra user constraints. In this paper, we present the design and implementation of MatLoc, an indoor localization system purely based on WiFi fingerprints, which jointly mitigates spatial ambiguity and temporal instability and derives reliable performance without impairing the ubiquity. The key idea is to embrace the spatial awareness of RSS values in a novel form of RSS Spatial Gradient (RSG) matrix for enhanced WiFi fingerprints. We devise techniques for the representation, construction, and comparison of the proposed fingerprint form, and integrate them all in a practical system, which follows the classical fingerprinting framework and requires no more inputs than any previous RSS fingerprint based systems. Extensive experiments in different environments demonstrate that MatLoc significantly improves the accuracy in both localization and tracking scenarios by about 30% to 50% compared with five state-of-the-art approaches.

Hidden screen-camera communication techniques emerge as a new paradigm that embeds data imperceptibly into regular videos while remaining unobtrusive to human viewers. Three key goals on imperceptible, high rate, and reliable communication are desirable but conflicting, and existing solutions usually made a trade-off among them. In this paper, we present the design and implementation of ChromaCode, a screen-camera communication system that achieves all three goals simultaneously. In our design, we consider for the first time color space for perceptually uniform lightness modifications. On this basis, we design an outcome-based adaptive embedding scheme, which adapts to both pixel lightness and regional texture. Last, we propose a concatenated code scheme for robust coding and devise multiple techniques to overcome various screen-camera channel errors. Our prototype and experiments demonstrate that ChromaCode achieves remarkable raw throughputs of >700 kbps, data goodputs of 120 kbps with BER of 0.05, and with fully imperceptible flicker for viewing proved by user study, which significantly outperforms previous works.

This paper presents Widar2.0, the first WiFi-based system that enables passive human localization and tracking using a single link on commodity off-the-shelf devices. Previous works based on either specialized or commercial hardware all require multiple links, preventing their wide adoption in scenarios like homes where typically only one single AP is installed. The key insight underlying Widar2.0 to circumvent the use of multiple links is to leverage multi-dimensional signal parameters from one single link. To this end, we build a unified model accounting for Angle-of-Arrival, Time-of-Flight, and Doppler shifts together and devise an efficient algorithm for their joint estimation. We then design a pipeline to translate the erroneous raw parameters into precise locations, which first finds parameters corresponding to the reflections of interests, then refines range estimates, and ultimately outputs target locations. Our implementation and evaluation on commodity WiFi devices demonstrate that Widar2.0 achieves better or comparable performance to state-of-the-art localization systems, which either use specialized hardwares or require 2 to 40 Wi-Fi links.

Device-free passive detection is an emerging technology to detect whether there exist any moving entities in the areas of interest without attaching any device to them. It is an essential primitive for a broad range of applications including intrusion detection for safety precautions, patient monitoring in hospitals, child and elder care at home, and so forth. Despite the prevalent signal feature Received Signal Strength (RSS), most robust and reliable solutions resort to a finer-grained channel descriptor at the physical layer, e.g., the Channel State Information (CSI) in the 802.11n standard. Among a large body of emerging techniques, however, few of them have explored the full potential of CSI for human detection. Moreover, space diversity supported by nowadays popular multiantenna systems are not investigated to a comparable extent as frequency diversity. In this article, we propose a novel scheme for device-free PAssive Detection of moving humans with dynamic Speed (PADS). Both full information (amplitude and phase) of CSI and space diversity across multiantennas in MIMO systems are exploited to extract and shape sensitive metrics for accuracy and robust target detection. We prototype PADS on commercial WiFi devices, and experiment results in different scenarios demonstrate that PADS achieves great performance improvement in spite of dynamic human movements.

Mobile crowdsourcing is an emerging complex problem solving paradigm that makes use of pervasive mobile devices equipped with multi-functional sensors. Recently, vehicles have also been increasingly adopted for mobile crowdsourcing, as the vehicles, as well as drivers, can provide diverse sensing capability and predictable mobility. Existing mobile crowdsourcing algorithms mostly recruit workers to complete one kind of sensing tasks, i.e., location-based query tasks or automatic sensing tasks. In this paper, we investigate the possibility of recruiting a set of vehicles to simultaneously complete these two categories of tasks, so as to maximize the sensing utility of each participant. We first model the worker recruitment for vehicle-based crowdsourcing as a bi-objective optimization problem with respect to the sensing capability and predictable mobility of vehicles. The recruitment problem is proven to be NP-hard, and we design two heuristic algorithms based on the bi-objective greedy strategy and the multi-objective genetic algorithm to find the solutions. The experimental results with a real-world traffic trace data set show that the proposed algorithms outperform some existing algorithms in finding solutions that maximize both objectives.

Recent years have witnessed the advance of RFID-based localization techniques that demonstrate high precision. Many efforts have been made locating RFID tags accurately with a mandatory assumption that the RFID reader's position is known in advance. Unfortunately, calibrating reader's location manually is always time-consuming and laborious in practice. In this paper, we present Tagspin, an approach using COTS tags to pinpoint the reader (antenna) quickly and easily with high accuracy. Tagspin enables each tag to emulate a circular antenna array by uniformly spinning on the edge of a rotating disk. We design an SAR-based method for estimating the angle spectrum of the target reader. Compared to previous AoA-based techniques, we employ an enhanced power profile modeling the relative signal power received from the reader along different spatial directions, which is more accurate and immune to ambient noise as well as measurement errors caused by hardware characteristics. Besides, we find that tag's phase measurements in practice are related to its orientation. To the best of our knowledge, we are the first to point out this fact and quantify the relationship between them. By calibrating the phase shifts caused by orientation, the positioning accuracy can be improved by 3:7×. We have implemented Tagspin with COTS RFID devices and evaluated it extensively. Experimental results show that Tagspin achieves mean accuracy of 7:3 cm with standard deviation of 1:8 cm in 3D space.

Modern mobile devices are equipped with multiple antennas, which brings various wireless sensing applications such as accurate localization, contactless human detection, and wireless human-device interaction. A key enabler for these applications is phased array signal processing, especially Angle of Arrival (AoA) estimation. However, accurate AoA estimation on commodity devices is non-trivial due to limited number of antennas and uncertain phase offsets. Previous works either rely on elaborate calibration or involve contrived human interactions. In this paper, we aim to enable practical AoA measurements on commodity off-the-shelf (COTS) mobile devices. The key insight is to involve users' natural rotation to formulate a virtual spatial-temporal antenna array and conduce a relative incident signal of measurements at two orientations. Then by taking the differential phase, it is feasible to remove the phase offsets and derive the accurate AoA of the equivalent incoming signal, while the rotation angle can also be captured by built-in inertial sensors. On this basis, we propose Differential MUSIC (D-MUSIC), a relative form of the standard MUSIC algorithm that eliminates the unknown phase offsets and achieves accurate AoA estimation on COTS mobile devices with only one rotation. We further extend DMUSIC to 3-D space, integrate extra measurements during rotations for higher estimation accuracy, and fortify it in multipath-rich scenarios. We prototype D-MUSIC on commodity WiFi infrastructure and evaluate it in typical indoor environments. Experimental results demonstrate a superior performance with average AoA estimation errors of 130 with only three measurements and 50 with at most 10 measurements. Requiring no modifications or calibration, D-MUSIC is envisioned as a promising scheme for practical AoA estimation on COTS mobile devices.

In-air interaction acts as a key enabler for ambient intelligence and augmented reality. As an increasing popular example, exergames, and the alike gesture recognition applications, have attracted extensive research in designing accurate, pervasive and low-cost user interfaces. Recent advances in wireless sensing show promise for a ubiquitous gesture-based interaction interface with Wi-Fi. In this work, we extract complete information of motion-induced Doppler shifts with only commodity Wi-Fi. The key insight is to harness antenna diversity to carefully eliminate random phase shifts while retaining relevant Doppler shifts. We further correlate Doppler shifts with motion directions, and propose a light-weight pipeline to detect, segment, and recognize motions without training. On this basis, we present WiDance, a Wi-Fi-based user interface, which we utilize to design and prototype a contactless dance-pad exergame. Experimental results in typical indoor environment demonstrate a superior performance with an accuracy of 92%, remarkably outperforming prior approaches.

Radio-frequency identification (RFID) systems, as major enablers of automatic identification, are currently supplemented with various interesting sensing functions, e.g., motion tracking. All these sensing applications forcedly require much higher reading rate (i.e., sampling rate) such that any fast movement of tagged objects can be accurately captured in a timely manner through tag readings. However, COTS RFID systems suffer from an extremely low individual reading rate when multiple tags are present, due to their intense channel contention in the link layer. In this work, we present a holistic system, called Tagwatch, a rate-adaptive reading system for COTS RFID devices. This work revisits the reading rate from a distinctive perspective: mobility. We observe that the reading demands of mobile tags are considerably more urgent than those of stationary tags because the states of the latter nearly remain unchanged; meanwhile, only a few tags (e.g., < 20%) are actually in motion despite the existence of a massive amount of tags in practice. Thus, Tagwatch adaptively improves the reading rates for mobile tags by cutting down the readings of stationary tags. Our main contribution is a two-phase reading design, wherein the mobile tags are discriminated in the Phase I and exclusively read in the Phase II. We built a prototype of Tagwatch with COTS RFID readers and tags. Results from our microbenchmark analysis demonstrate that the new design outperforms the reading rate by 3.2x when 5% of tags are moving.

In recent years, WiFi-based sensing applications have been proliferated due to growing capacities of the physical layer. Channel State Information (CSI), which depicts the characteristics of propagation environment and reflects different human behaviors, can be easily obtained on commodity WiFi devices with slight driver modification. For the sake of higher accuracy and robustness of CSI-based sensing, a variety of research efforts have been devoted to model refinement, algorithm optimization and data sanitization. Radio frequency interference (RFI) is a crucial problem, which, however, is surprisingly overlooked and largely unexplored. The sensing performance can be significantly boosted by identifying and properly handling the interfered CSI measurements. In this paper, we demonstrate that it is feasible to identify the interfered CSI measurements due to the unique properties induced by RFI. We propose two RFI detection algorithms by utilizing cyclostationary analysis from different angles. Experimental results on off-the-shelf WiFi devices show that both algorithms are robustly stable for different scenarios and can achieve a remarkable overall accuracy of > 90%.

Understanding and predicting cellular traffic at large-scale and fine-granularity is beneficial and valuable to mobile users, wireless carriers and city authorities. Predicting cellular traffic in modern metropolis is particularly challenging because of the tremendous temporal and spatial dynamics introduced by diverse user Internet behaviours and frequent user mobility citywide. In this paper, we characterize and investigate the root causes of such dynamics in cellular traffic through a big cellular usage dataset covering 1.5 million users and 5,929 cell towers in a major city of China. We reveal intensive spatio-temporal dependency even among distant cell towers, which is largely overlooked in previous works. To explicitly characterize and effectively model the spatio-temporal dependency of urban cellular traffic, we propose a novel decomposition of in-cell and inter-cell data traffic, and apply a graph-based deep learning approach to accurate cellular traffic prediction. Experimental results demonstrate that our method consistently outperforms the state-of-the-art time-series based approaches and we also show through an example study how the decomposition of cellular traffic can be used for event inference.

In recent years, both the RFID and computer vision technologies have been widely employed in indoor scenarios aimed at different goals while faced with respective limitations. For example, the RFID-based EAS system is useful in quickly identifying tagged objects but the accompanying false alarm problem is troublesome and hard to tackle with except that the accurate trajectory of the target tag can be easily acquired. On the other side, the CV system performs fairly well in tracking multiple moving objects precisely while finding it difficult to screen out the specific target among them. To overcome the above limitations, we present TagVision, a hybrid RFID and computer vision system for fine-grained localization and tracking of tagged objects. A fusion algorithm is proposed to organically combine the position information given by the CV subsystem, and phase data output by the RFID subsystem. In addition, we employ the probabilistic model to eliminate the measurement error caused by thermal noise and device diversity. We have implemented TagVision with COTS camera and RFID devices and evaluated it extensively in our lab environment. Experimental results show that TagVision can achieve 98% blob matching accuracy and 10.33mm location tracking precision.

Millions of people nowadays share their television (TV) experience with other people through social media like Twitter or Facebook with mobile devices. It is generally believed that TV has been repurposed for social networks, called as social television. A key functionality of social television is that it allows the viewers to interchange their comments through mobile devices, thus creating the impression of watching TV like alongside a group of friends. To do so, mobile devices have to be aware of the current media context (identifier and progress) of what the TV is playing, known as synchronizing context from TVs to mobile devices. Unfortunately, most legacy systems are not able to track the playing progresses or need to upgrade TV devices. To address the issue, we design a purely software-based solution, called as TagScreen, which inserts a series of hidden sound markers into the audio of the content. TagScreen supports longrange or multipath-resistant synchronization at second-level, being independent of device diversity over severely frequency-selective acoustic channels. We implement TagScreen by using COTS TVs and mobile devices. The system has been extensively tested on 150 movies and 150 TV series across five different environments. Results show that TagScreen has a mean recognition accuracy of 98% up to 35m, and a mean tracking accuracy of 97%.

Various pioneering approaches have been proposed for Wi-Fi-based sensing, which usually employ learning-based techniques to seek appropriate statistical features, yet do not support precise tracking without prior training. Thus to advance passive sensing, the ability to track fine-grained human mobility information acts as a key enabler. In this paper, we propose Widar, a Wi-Fi-based tracking system that simultaneously estimates a human's moving velocity (both speed and direction) and location at a decimeter level. Instead of applying statistical learning techniques, Widar builds a theoretical model that geometrically quantifies the relationships between CSI dynamics and the user's location and velocity. On this basis, we propose novel techniques to identify frequency components related to human motion from noisy CSI readings and then derive a user's location in addition to velocity. We implement Widar on commercial Wi-Fi devices and validate its performance in real environments. Our results show that Widar achieves decimeter-level accuracy, with a median location error of 25 cm given initial positions and 38 cm without them and a median relative velocity error of 13%.

Crowdsourced mobile network access (CMNA), in which mobile users can share their Internet access with others, is a promising paradigm for addressing users' increasing needs for ubiquitous connectivity and alleviating cellular network congestion. In this paper, we study the operator-assisted CMNA model, in which a mobile virtual network operator (MVNO) incentivizes its subscribers to operate as mobile WiFi hotspots (hosts) through reimbursement and gets revenue from the relayed traffic. Despite of the promising performance, practical strategies for MVNO and hosts have not been studied yet. Existing works usually assume both MVNO and hosts can obtain complete information, and ignore the accompanied overhead in backhaul and privacy threats to users. Such assumptions are unrealistic in practice. To address this issue, we first systematically characterize the revenue loss for both MVNO and hosts with incomplete market information. Based on the analysis, we propose a novel partial cooperation strategy (PCS) to enable appropriate information exchange between MVNO and hosts with little overhead. With adaptive reimbursement and subtle information control, our PCS efficiently improves MVNO's revenue at equilibrium, and also satisfies the hosts' rationality. Through extensive evaluation on data from the real world, we demonstrate our PCS can improve MVNO's revenue by 23% at equilibrium, compared with the results without PCS.

Mobile crowdsourcing is deemed as a powerful technique to solve traditional problems. But the crowdsourced data from smartphones are generally low quality, which can induce crucial challenges and hurt the applicability of crowdsourcing applications. This paper presents our study to address such challenges in a concrete application, namely, floorplan generation. Existing proposals mostly rely on infrastructural references or accurate data sources, which are restricted in terms of applicability and pervasiveness. Our proposal called SenseWit is motivated by the observation that people's behavior offers meaningful clues for location inference. The noise, ambiguity, and behavior diversity contained in the crowdsourced data, however, mean non-trivial challenges in generating high-quality floorplans. We propose: 1) a novel concept called Nail to identify featured locations in indoor space and 2) a heuristic pathlet bundling algorithm to progressively discover the internal layouts of a floorplan. We implement SenseWit and conduct real-world experiments in different spaces to demonstrate its efficacy. This paper offers an efficient technique to obtain high-quality structures (either logical or physical) from low-quality data. We believe it can be generalized to other crowdsourcing applications.

Most of existing indoor navigation systems work in a client/server manner, which needs to deploy comprehensive localization services together with precise indoor maps a prior. In this paper, we design and realize a peer-to-peer navigation system (ppNav), on smartphones, which enables the fast-to-deploy navigation services, avoiding the requirements of pre-deployed location services and detailed floorplans. ppNav navigates a user to the destination by tracking user mobility, promoting timely walking tips and alerting potential deviations, according to a previous traveller's trace experience. Specifically, we utilize the ubiquitous WiFi fingerprints in a novel diagrammed form and extract both radio and visual features of the diagram to track relative locations and exploit fingerprint similarity trend for deviation detection. We further devise techniques to lock on a user to the nearest reference path in case he/she arrives at an uncharted place. Consolidating these techniques, we implement ppNav on commercial mobile devices and validate its performance in real environments. Our results show that ppNav achieves delightful performance, with an average relative error of 0.9 m in trace tracking and a maximum delay of nine samples (about 4.5 s) in deviation detection.

To save energy, wireless sensor networks often run in a low-duty-cycle mode, where the radios of sensor nodes are scheduled between ON and OFF states. For nodes to communicate with each other, low power listening (LPL) and low power probing (LPP) are two types of rendezvous mechanisms. Nodes with LPL or LPP rely on signal strength or probe packets to detect potential transmissions, and then keep the radio-ON for communications. Unfortunately, in co-existing environments, signal strength and probe packets are susceptible to interference, resulting in undesirable radio ON time when the signal strength of interference is above a threshold or a probe packet is interfered. To address the issue, we propose ZiSense, a low duty cycling mechanism resilient to interference. Instead of checking the signal strength or decoding the probe packets, ZiSense detects the ZigBee signals and wakes up nodes accordingly. On sensor nodes with limited information and resource, we carefully study and extract short-term features purely from the time-domain RSSI sequence, and design a rule-based approach to efficiently identify the existence of ZigBee. We theoretically analyze the benefit of ZiSense in different environments and implement a prototype in TinyOS with TelosB motes. We examine ZiSense performance under controlled interference and office environments. The evaluation results show that, compared with the state-of-the-art rendezvous mechanisms, ZiSense significantly reduces the energy consumption.

Packet corruption caused by collision is a critical problem that hurts the performance of wireless networks. Conventional medium access control (MAC) protocols resort to collision avoidance to maintain acceptable efficiency of channel utilization. According to our investigation and observation, however, collision avoidance comes at the cost of miscellaneous overhead, which oppositely hurts channel utilization, not to mention the poor resiliency and performance of those protocols in face of dense networks or intensive traffic. Discovering the ability to tolerate collisions at the physical layer implementations of wireless networks, we in this paper propose Coco, a protocol that advocates simultaneous accesses from multiple senders to a shared channel, i.e., optimistically allowing collisions instead of simply avoiding them. With a simple but effective design, Coco addresses the key challenges in achieving collision tolerance, such as precise sender alignment and the control of transmission concurrency. We implement Coco in 802.15.4 networks and evaluate its performance through extensive experiments with 21 TelosB nodes. The results demonstrate that Coco is light-weight and enhances channel utilization by at least 20 percent in general cases, compared with state-of-the-arts protocols.

Crowdsensing applications require individuals to share local and personal sensing data with others to produce valuable knowledge and services. Meanwhile, it has raised concerns especially for location privacy. Users may wish to prevent privacy leak and publish as many non-sensitive contexts as possible. Simply suppressing sensitive contexts is vulnerable to the adversaries exploiting spatio-temporal correlations in the user's behavior. In this work, we present PLP, a crowdsensing scheme which preserves privacy while it maximizes the amount of data collection by filtering a user's context stream. PLP leverages a conditional random field to model the spatio-temporal correlations among the contexts, and proposes a speed-up algorithm to learn the weaknesses in the correlations. Even if the adversaries are strong enough to know the filtering system and the weaknesses, PLP can still provably preserve privacy, with little computational cost for online operations. PLP is evaluated and validated over two real-world smartphone context traces of 34 users. The experimental results show that PLP efficiently protects privacy without sacrificing much utility.

In this work, we design and develop Montage for real-time multi-user formation tracking and localization by off-the-shelf smartphones. Montage achieves submeter-level tracking accuracy by integrating temporal and spatial constraints from user movement vectorestimation and distance measuring. In Montage, we designed a suite of novel techniques to surmount a variety of challenges in real-time tracking, without infrastructure and fingerprints, and without any a priori user-specific (e.g., stride-length and phoneplacement) or site-specific (e.g., digitalized map) knowledge: (1) a coded audio tone to support multi-user tracking with minimal latency, in the presence of high noise, multi-path effect, and Doppler Shift, (2) an innovative stride-length and walking direction estimation method without a priori knowledge of user and site, and (3) a vector-based multi-user tracking scheme which connects successive localization snapshots to refine users' locations and generate continuous moving traces. We implemented, deployed, and evaluated Montage in both outdoor and indoor environment. Our experimental results (847 traces from 15 users) show that the stride-length estimated by Montage over all users has error within 9cm, and the moving-direction estimated by Montage is within 20 degrees. For real-time tracking, Montage provides meter-second-level formation tracking accuracy with off-the-shelf mobile phones.

Cardinality estimation is one of the fundamental problems in large-scale radio frequency identification systems. While many efforts have been made to achieve faster approximate counting, the accuracy of estimates itself has not received enough attention. Specifically, most state-of-the-art schemes share a two-phase paradigm implicitly or explicitly, which needs a rough estimate first and then refines it to a final estimate meeting the desired accuracy; we observe that the final estimate can largely deviate from the expectation due to the skewed rough estimate, i.e., the accuracy of final estimates is not rigorously bounded. This negative impact is hidden because former solutions either assume perfect rough estimates or rough estimates that can be produced by uniform random data or perfect hash functions that can turn any data into uniform random data. Unfortunately, both of them are hard to meet in practice. To address the above issues, we propose a novel scheme, namely, “rigorous and practical cardinality (RPC)” estimation. RPC adopts the two-phase paradigm, in which the rough estimate is derived in the first phase using pairwise-independent hashing. In the second phase, we employ t-wise-independent hashing to reinforce the rough estimate to meet arbitrary accuracy requirements. We validate the effectiveness and performance of RPC through theoretical analysis and extensive simulations. The results show that the RPC can meet the desired accuracy all the time with diverse practical settings while previous designs fail with non-uniform data.

Many object localization applications need the relative locations of a set of objects as oppose to their absolute locations. Although many schemes for object localization using radio frequency identification (RFID) tags have been proposed, they mostly focus on absolute object localization and are not suitable for relative object localization because of large error margins and the special hardware that they require. In this paper, we propose an approach called spatial-temporal phase profiling (STPP) to RFID-based relative object localization. The basic idea of STPP is that by moving a reader over a set of tags during which the reader continuously interrogating the tags, for each tag, the reader obtains a sequence of RF phase values, which we call a phase profile, from the tag's responses over time. By analyzing the spatial-temporal dynamics in the phase profiles, STPP can calculate the spatial ordering among the tags. In comparison with prior absolute object localization schemes, STPP requires neither dedicated infrastructure nor special hardware. We implemented STPP and evaluated its performance in two real-world applications: locating misplaced books in a library and determining the baggage order in an airport. The experimental results show that STPP achieves about 84% ordering accuracy for misplaced books and 95% ordering accuracy for baggage handling. We further leverage the controllable reader antenna and upgrade STPP to infer the spacing between each pair of tags. The result shows that STPP could achieve promising performance on distance ranging.

Traditional vibration inspection systems, equipped with separated sensing and communication modules, are either very expensive (e.g., hundreds of dollars) and/or suffer from occlusion and narrow field of view (e.g., laser). In this paper, we present an RFID-based solution, Tagbeat, to inspect mechanical vibration using COTS RFID tags and readers. Making sense of micro and high-frequency vibration using random and low-frequency readings of tag has been a daunting task, especially challenging for achieving sub-millisecond period accuracy. Our system achieves these three goals by discerning the change pattern of backscatter signal replied from the tag, which is attached on the vibrating surface and displaced by the vibration within a small range. This paper introduces three main innovations. First, it shows how one can utilize COTS RFID to sense mechanical vibration and accurately discover its period with a few periods of short and noisy samples. Second, a new digital microscope is designed to amplify the micro-vibration-induced weak signals. Third, Tagbeat introduces compressive reading to inspect high-frequency vibration with relatively low RFID read rate. We implement Tagbeat using a COTS RFID device and evaluate it with a commercial centrifugal machine. Empirical benchmarks with a prototype show that Tagbeat can inspect the vibration period with a mean accuracy of 0.36ms and a relative error rate of 0.03%. We also study three cases to demonstrate how to associate our inspection solution with the specific domain requirements.

Even though indoor smoking ban is being put into practice in civilized countries, existing vision or sensor-based smoking detection methods cannot provide ubiquitous detection service. In this paper, we take the first attempt to build a ubiquitous passive smoking detection system, Smokey, which leverages the patterns smoking leaves on WiFi signal to identify the smoking activity even in the non-line-of-sight and through-wall environments. We study the behaviors of smokers and leverage the common features to recognize the series of motions during smoking, avoiding the target-dependent training set to achieve the high accuracy. We design a foreground detection-based motion acquisition method to extract the meaningful information from multiple noisy subcarriers even influenced by posture changes. Without the requirement of target's compliance, we leverage the rhythmical patterns of smoking to detect the smoking activities. We also leverage the diversity of multiple antennas to enhance the robustness of Smokey. Due to the convenience of integrating new antennas, Smokey is scalable in practice for ubiquitous smoking detection. We prototype Smokey with the commodity WiFi infrastructure and evaluate its performance in real environments. Experimental results show Smokey is accurate and robust in various scenarios.

Shopping behavior data is of great importance in understanding the effectiveness of marketing and merchandising campaigns. Online clothing stores are capable of capturing customer shopping behavior by analyzing the click streams and customer shopping carts. Retailers with physical clothing stores, however, still lack effective methods to comprehensively identify shopping behaviors. In this paper, we show that backscatter signals of passive RFID tags can be exploited to detect and record how customers browse stores, which garments they pay attention to, and which garments they usually pair up. The intuition is that the phase readings of tags attached to items will demonstrate distinct yet stable patterns in a time-series when customers look at, pick out, or turn over desired items. We design ShopMiner, a framework that harnesses these unique spatial-temporal correlations of time-series phase readings to detect comprehensive shopping behaviors. We have implemented a prototype of ShopMiner with a COTS RFID reader and four antennas, and tested its effectiveness in two typical indoor environments. Empirical studies from two-week shopping-like data show that ShopMiner is able to identify customer shopping behaviors with high accuracy and low overhead, and is robust to interference.

It has been a consensus that a certain relationship exists between personal emotions and usage pattern of the smartphone. Based on users’ emotions and personalities, more and more applications are developed to provide intelligent automation services on the smartphone, such as music recommendations or stranger introductions on social networking sites. Most existing work studies this relationship by learning large amounts of samples, which are manually labeled and collected from smartphone users. The manual labeling process, however, is very time-consuming and labor-intensive. To address this issue, we propose iSelf, a system that provides a general service of automatic detection of a user’s emotions in cold-start conditions with a smartphone. With the technology of transfer learning, iSelf achieves high accuracy given only a few labeled samples. We also embed a hybrid public/personal inference engine and validation system into iSelf, to make it maintain updates continuously. Through extensive experiments in real traces, the inferring accuracy is tested above 74% and can be improved increasingly through validation and updates. The application program interface has been open online for other developers.

Many cloud platforms emerge to meet urgent requirements for large-volume personal image store, sharing and search. Though most would agree that images contain rich sensitive information (e.g., people, location and event) and people's privacy concerns hinder their participation into untrusted services, today's cloud platforms provide little support for image privacy protection. Facing large-scale images from multiple users, it is extremely challenging for the cloud to maintain the index structure and schedule parallel computation without learning anything about the image content and indices. In this work, we introduce a novel system PIC: A Privacy-preserving Image search system on Cloud, which is a step towards feasible cloud services which provide secure content-based large-scale image search with fine-grained access control. Users can search on others' images if they are authorized by the image owners. Majority of the computationally intensive jobs are handled by the cloud, and a querier can now simply send the query and receive the result. Specially, to deal with massive images, we design our system suitable for distributed and parallel computation and introduce several optimizations to further expedite the search process. Our security analysis and prototype system evaluation results show that PIC successfully protects the image privacy at a low cost of computation and communication.

Mobile crowdsourcing is deemed as a powerful technique to solve traditional problems. But the crowdsourced data from smartphones are generally with low quality, which induce crucial challenges and hurt the applicability of crowdsourcing applications. This paper presents our study to address such challenges in a concrete application, namely floorplan generation. In order to utilize pedestrians' traces for indoor location inference, existing proposals mostly rely on infrastructural references or accurate data sources, which are by nature restricted in terms of applicability and pervasiveness. Our proposal called SenseWit is motivated by the observation that people's behavior offers meaningful clues for location inference. The noise, ambiguity, and behavior diversity contained in the crowdsourced data, however, mean non-trivial challenges in generating high-quality floorplans. We propose 1) a novel concept called Nail to identify featured locations in indoor space and 2) a heuristic pathlet bundling algorithm to progressively discover the internal layouts. We implement SenseWit and conduct real-world experiments in different spaces. Our work offers an efficient technique to obtain high-quality structures (either logical or physical) from low-quality data. We believe it can be generalized to other crowdsourcing applications.

Indoor localization is of great importance to a wide range of applications in shopping malls, office buildings and public places. The maturity of computer vision (CV) techniques and the ubiquity of smartphone cameras hold promise for offering sub-meter accuracy localization services. However, pure CV-based solutions usually involve hundreds of photos and pre-calibration to construct image database, a labor-intensive overhead for practical deployment. We present ClickLoc, an accurate, easy-to-deploy, sensor-enriched, image-based indoor localization system. With core techniques rooted in semantic information extraction and optimization-based sensor data fusion, ClickLoc is able to bootstrap with few images. Leveraging sensor-enriched photos, ClickLoc also enables user localization with a single photo of the surrounding place of interest (POI) with high accuracy and short delay. Incorporating multi-modal localization with Manifold Alignment and Trapezoid Representation, ClickLoc not only localizes efficiently, but also provides image-assisted navigation. Extensive experiments in various environments show that the 80-percentile error is within 0.26m for POIs on the floor plan, which sheds light on sub-meter level indoor localization.

The wide adoption of smart devices with onboard cameras facilitates photo capturing and sharing, but greatly increases people's concern on privacy infringement. Here we seek a solution to respect the privacy of persons being photographed in a smarter way that they can be automatically erased from photos captured by smart devices according to their requirements. To make this work, we need to address three challenges: 1) how to enable users explicitly express their privacy protection intentions without wearing any visible specialized tag, and 2) how to associate the intentions with persons in captured photos accurately and efficiently. Furthermore, 3) the association process itself should not cause portrait information leakage and should be accomplished in a privacy-preserving way. In this work, we design, develop, and evaluate a system, called COIN (Cloak Of INvisibility), that enables a user to flexibly express her privacy requirement and empowers the photo service provider (or image taker) to exert the privacy protection policy. Leveraging the visual distinguishability of people in the field-of-view and the dimension-order-independent property of vector similarity measurement, COIN achieves high accuracy and low overhead. We implement a prototype system, and our evaluation results on both the trace-driven and real-life experiments confirm the feasibility and efficiency of our system.

Industry 4.0 brings forward higher requirements on the monitoring of industrial production. Video surveillance based on Wireless Mesh Networks (WMNs) has demonstrated its effectiveness in a number of applications. Different from some typical applications of WMN that solve the "last mile" Internet access problem, WMN-based video surveillance for industrial monitoring is very likely to work in extreme circumstances or requiring high performance. Thus, the guarantee of video quality is the key for the success of industrial video surveillance. Through extensive experimental research, we find that the state of the art mapping and queuing algorithms are approaching complication and the room for improvement is decreasing. The possible solution for performance breakthrough lies in exploiting the potentials of data granularity for mapping. In this work, we propose IMesh, a video transmission solution based on WMN, which takes frame type, frame location, data packets and other factors into consideration and quantifies their impacts on video quality. The proposed approach is particularly suitable for video surveillance in industrial production under aggressive conditions. To the best of our knowledge, IMesh is the first one that differentiates, prioritizes, and schedules video data in the packet level, which is the finest granularity one can achieve while keep the MAC layer protocol unchanged. Experiment results show that the proposed solution outperforms previous works both in terms of video quality and packet delay.

Recent years have witnessed the advance of RFID-based localization techniques that demonstrate high precision. Many efforts have been made locating RFID tags with a mandatory assumption that the RFID reader's position is known in advance. Unfortunately, calibrating reader's location manually is always time-consuming and laborious in practice. In this paper, we present Tagspin, an approach using COTS tags to pinpoint the reader (antenna) quickly and easily with high accuracy. Tagspin enables each tag to emulate a circular antenna array by uniformly spinning on the edge of a rotating disk. We design an SAR-based method for estimating the angle spectrum of the target reader. Compared to previous AoA-based techniques, we employ an enhanced power profile modeling the signal power received from the reader along different spatial directions, which is more accurate and immune to ambient noise as well as measurement errors caused by hardware characteristics. Besides, we find that tag's phase measurements in practice are related to its orientation. To the best of our knowledge, we are the first to point out this fact and quantify the relationship between them. By calibrating the phase shifts caused by orientation, the positioning accuracy can be improved by 3.7×. We have implemented Tagspin withCOTS RFID devices and evaluated it extensively. Experimentalresults show that Tagspin achieves mean accuracy of 7.3cm with standard deviation of 1.8cm in 3D space.

Most of existing indoor navigation systems work in a client/server manner, which needs to deploy comprehensive localization services together with precise indoor maps a prior. In this paper, we design and realize a Peer-to-Peer navigation system, named ppNav, on smartphones, which enables the fast-to-deploy navigation services, avoiding the requirements of pre-deployed location services and detailed floorplans. ppNav navigates a user to the destination by tracking user mobility, promoting timely walking tips, and alerting potential deviations, according to a previous traveller's trace experience. Specifically, we utilize the ubiquitous WiFi fingerprints in a novel diagrammed form and extract both radio and visual features of the diagram to track relative locations and exploit fingerprint similarity trend for deviation detection. Consolidating these techniques, we implement ppNav on commercial mobile devices and validate its performance in real environments. Our results show that ppNav achieves delightful performance, with an average relative error of 0.9m in trace tracking and a maximum delay of 9 samples (about 4.5s) in deviation detection.

Backscatter communication networks receive much attention recently due to the small size and low power of backscatter nodes. As backscatter communication is often influenced by the dynamic wireless channel quality, rate adaptation becomes necessary. Most existing approaches share a common drawback: they do not distinguish channel qualities from different nodes or sub-channels. Consequently, the transmission rate may be improperly selected, resulting in low network throughput. Through extensive experimental studies, we observe that channel diversity plays a significant role in rate selection. Therefore, there are opportunities of exploiting channel diversity for better rate adaptation, improving network throughput. In this paper, we propose a Channel-Aware Rate Adaptation framework (CARA) for backscatter communication networks. By employing a lightweight channel probing scheme, we are able to obtain fine-grained channel information that enables accurate channel estimation. We further design a novel channel selection algorithm, benefiting as many backscatter nodes as possible. On each selected channel, CARA chooses data rate with respect to the node that has the best channel condition. We implement CARA on commercial readers and the experiment results show that CARA achieves up to 4× goodput gain compared with state-of-the-art rate adaptation scheme.

Modern mobile devices are equipped with multiple antennas, which brings various wireless sensing applications such as accurate localization, contactless human detection and wireless human-device interaction. A key enabler for these applications is phased array signal processing, especially Angle of Arrival (AoA) estimation. However, accurate AoA estimation on commodity devices is non-trivial due to limited number of antennas and uncertain phase offsets. Previous works either rely on elaborate calibration or involve contrived human interactions. In this paper, we aim to enable practical AoA measurements on commodity off-the-shelf (COTS) mobile devices. The key insight is to involve users' natural rotation to formulate a virtual spatial-temporal antenna array and conduce a relative incident signal of measurements at two orientations. Then by taking the differential phase, it is feasible to remove the phase offsets and derive the accurate AoA of the equivalent incoming signal, while the rotation angle can also be captured by built-in inertial sensors. On this basis, we propose Differential MUSIC (D-MUSIC), a relative form of the standard MUSIC algorithm that eliminates the unknown phase offsets and achieves accurate AoA estimation on COTS mobile devices with only one rotation. We further extend D-MUSIC to 3-D space and fortify it in multipath-rich scenarios. We prototype D-MUSIC on commodity WiFi infrastructure and evaluate it in typical indoor environments. Experimental results demonstrate a superior performance with an average AoA estimation error of 13°. Requiring no modifications or calibration, D-MUSIC is envisioned as a promising scheme for practical AoA estimation on COTS mobile devices.

Even though indoor smoking ban is being put into practice in civilized countries, existing vision or sensor-based smoking detection methods cannot provide ubiquitous smoking detection. In this paper, we take the first attempt to build a ubiquitous passive smoking detection system, which leverages the patterns smoking leaves on WiFi signals to identify the smoking activity even in the non-line-of-sight and through-wall environments. We study the behaviors of smokers and leverage the common features to recognize the series of motions during smoking, avoiding the target-dependent training set to achieve the high accuracy. We design a foreground detection based motion acquisition method to extract the meaningful information from multiple noisy subcarriers even influenced by posture changes. Without requirements of target's compliance, we leverage the rhythmical patterns of smoking to reduce the detection false positives. We prototype Smokey with the commodity WiFi infrastructure and evaluate its performance in real environments. Experimental results show Smokey is accurate and robust in various scenarios.

The proliferation of mobile devices has enabled extensive mobile-data supported applications, e.g., exercise and activity recognition and quantification. Typically, these applications need predefined features and are only applicable to predefined activities. In this work, we address the issue of deep understanding of arbitrary activities and semantic searching of any activity over massive mobile sensing data. The challenges stem from the rich dynamics and the wide-spectrum of activities that a human being could perform. We propose a hierarchical activity representation, extract common bases of motion data in an unsupervised manner by leveraging the power of deep neural networks, and propose a universal multi-resolution representation for all activities without prior knowledge. Based on this representation, we design an innovative system Lasagna to manage and search motion data semantically. We implement a prototype system and our comprehensive evaluations show that our system can achieve highly accurate activity classification (with precision 98.9%) and search (with recall almost 100% and precision about 90%) over a diverse set of activities.

Traditional vibration inspection systems, equipped with separated sensing and communication modules, are either very expensive (e.g., hundreds of dollars) and/or suffer from occlusion and narrow field of view (e.g., laser). In this work, we present an RFID-based solution, Tagbeat, to inspect mechanical vibration using COTS RFID tags and readers. Making sense of micro and high-frequency vibration using random and low-frequency readings of tag has been a daunting task, especially challenging for achieving sub-millisecond period accuracy. Our system achieves these three goals by discerning the change pattern of backscatter signal replied from the tag, which is attached on the vibrating surface and displaced by the vibration within a small range. This work introduces three main innovations. First, it shows how one can utilize COTS RFID to sense mechanical vibration and accurately discover its period with a few periods of short and noisy samples. Second, a new digital microscope is designed to amplify the micro-vibration-induced weak signals. Third, Tagbeat introduces compressive reading to inspect high-frequency vibration with relatively low RFID read rate. We implement Tagbeat using a COTS RFID device and evaluate it with a commercial centrifugal machine. Empirical benchmarks with a prototype show that Tagbeat can inspect the vibration period with a mean accuracy of 0.36ms and a relative error rate of 0.03%. We also study three cases to demonstrate how to associate our inspection solution with the specific domain requirements.

Radio Frequency Identification (RFID) is a key emerging technology for supply chain systems. By attaching RFID tags to various products, product-related data can be efficiently indexed, retrieved and shared among multiple participants involved in an RFID-enabled supply chain. The flexible data access property, however, raises security and privacy concerns. In this paper, we target at security and privacy issues in RFID-enabled supply chain systems. We investigate RFID-enabled Third-party Supply chain (RTS) systems and identify several inherent security and efficiency requirements. We further design a Secure RTS system called SRTS, which leverages RFID tags to deliver computation-lightweight crypto-IDs in the RTS system to meet both the security and efficiency requirements. SRTS introduces a Private Verifiable Signature (PVS) scheme to generate computation-lightweight crypto-IDs for product batches, and couples the primitive in RTS system through careful design. We conduct theoretical analysis and experiments to demonstrate the security and efficiency of SRTS.

Duty cycling mode is widely adopted in wireless sensor networks to save energy. Existing duty-cycling protocols cannot well adapt to different data rates and dynamics, resulting in a high energy consumption in real networks. Improving those protocols may require global information or heavy computation and thus may not be practical, leading to many empirical parameters in real protocols. To fill the gap between the application requirement and protocol performance, in this paper, we analyze the energy consumption for duty cycled sensor networks with different data rates. Our analysis shows that existing protocols cannot lead to an efficient energy consumption in various scenarios. Based on the analysis, we design a light-weight adaptive duty-cycling protocol (LAD), which reduces the energy consumption under different data rates and protocol dynamics. LAD can adaptively adjust the protocol parameters according to network conditions such as data rate and achieve an optimal energy efficiency. To make LAD practical in real network, we further pre-calculate optimal parameters offline and store them on sensor nodes, which significantly reduces the computation time. We theoretically validate the performance improvement of the protocol. We implement the protocol in TinyOS and extensively evaluate it on 40 TelosB nodes. The evaluation results show the energy consumption can be reduced by 28.2-40.1 percent compared with state-of-the-art protocols. Results based on data from a 1,200-node operational network further show the effectiveness and scalability of the design.

To guarantee reliability, bulk data dissemination relies on the negotiation scheme in which senders and receivers negotiate transmission schedule through a three-way handshake procedure. However, we find negotiation incurs a long dissemination time and seriously defers the network-wide convergence. On the other hand, the flooding approach, which is conventionally considered inefficient and energy-consuming, can facilitate bulk data dissemination if appropriately incorporated. This motivates us to pursue a delicate tradeoff between negotiation and flooding in the bulk data dissemination. We propose SurF (Survival of the Fittest), a bulk data dissemination protocol which adaptively adopts negotiation and leverages flooding opportunistically. SurF incorporates a time-reliability model to estimate the time efficiencies (flooding versus negotiation) and dynamically selects the fittest one to facilitate the dissemination process. We implement SurF in TinyOS 2.1.1 and evaluate its performance with 40 TelosB nodes. The results show that SurF, while retaining the dissemination reliability, reduces the dissemination time by 40 percent in average, compared with the state-of-the-art protocols.

Accurate maps are increasingly important with the growth of smart phones and the development of location-based services. Several crowdsourcing based map generation protocols that rely on users to provide their traces have been proposed. Being creative, however, those methods pose a significant threat to user privacy as the traces can easily imply user behavior patterns. On the flip side, crowdsourcing-based map generation method does need individual locations. To address the issue, we present a systematic participatory-sensing-based high-quality map generation scheme, PMG, that meets the privacy demand of individual users. To be specific, the individual users merely need to upload unorganized sparse location points to reduce the risk of exposing users’ traces and utilize theCrust, a technique from computational geometry for curve reconstruction, to estimate the unobserved map as well as evaluate the degree of privacy leakage. Experiments show that our solution is able to generate high-quality maps for a real environment that is robust to noisy data. The difference between the ground-truth map and the produced map is less than 10 m, even when the collected locations are about 32 m apart after clustering for the purpose of removing noise.

Sleep quality plays a vital role in personal health. A great deal of effort has been paid to design sleep quality monitoring systems, providing services ranging from bedtime monitoring to sleep activity detection. However, as sleep quality is closely related to the distribution of sleep duration over different sleep stages, neither the bedtime nor the intensity of sleep activities is able to reflect sleep quality precisely. We present Sleep Hunter, a mobile service that provides a fine-grained detection of sleep stage transition for sleep quality monitoring and intelligent wake-up call. The rationale is that each sleep stage is accompanied by specific body movements and acoustic signals. Leveraging the built-in sensors on smartphones, Sleep Hunter integrates these physical activities with sleep environment, inherent temporal relation, and personal factors by a statistical model for a fine-grained sleep stage detection. Based on the duration of each sleep stage, Sleep Hunter further provides sleep quality report and smart call service for users. Experimental results from over 30 sets of nocturnal sleep data show that our system is superior to existing actigraphy-based sleep quality monitoring systems, and achieves satisfying detection accuracy compared with dedicated polysomnography-based devices.

Radio frequency identification (RFID) technology has been widely adopted in a variety of applications from logistics to access control. Many applications gain benefits from knowing the exact position of an RFID-tagged object. Existing localization algorithms in wireless network, however, can hardly be directly employed due to tag's limited capabilities in terms of energy and memory. In this paper, we propose BackPos, a fine-grained backscatter positioning technique using the commercial off-the-shelf (COTS) RFID products with detected phases. Our studies show that the phase is a stable indicator highly related to tag's position and preserved over frequency or tag orientation, but challenged by its periodicity and tag's diversity. We attempt to infer the distance differences from phases detected by antennas under triangle constraint. Further, hyperbolic positioning using the distance differences is employed to shrink the tag's candidate positions until finding out the real one. In combination with interrogation zone, we finally relax the triangle constraint and allow arbitrary deployment of antennas by sacrificing the feasible region. We implement a prototype of BackPos with COTS RFID products and evaluate this design in various scenarios. The results show that BackPos achieves the mean accuracy of 12:8cm with variance of 3:8 cm.

Received Signal Strength (RSS) maps provide fundamental information for mobile users, aiding the development of conflict graph and improving communication quality to cope with the complex and unstable wireless channels. In this paper, we present CARM: a scheme that exploits crowd-sensing to construct outdoor RSS maps using smartphone measurements. An alternative yet impractical approach in literature is to appeal to professionals with customized devices. Our work distinguishes itself from previous studies by supporting off-the-shelf smartphone devices, and more importantly, by mitigating the error-prone nature and inaccuracies of these devices to build RSS maps through crowd-sensing. The main challenges are that, we need to calibrate error-prone smartphone measurements with “inaccurate” and “incomplete” data. To address these challenges, we build the measurement error model of smartphone based on the experimental observations and analyses. Moreover, we propose an iterative method based on Davidon-Fletcher-Powell (DFP) algorithm, to estimate the parameters for the error models of each smartphone and the signal propagation models of each AP simultaneously. The key intuition is that, the calibrated measurements based on the error model are constrained by the physics of the signal propagation model. Finally, a model-driven RSS map construction scheme is built upon these two models with these estimated parameters. The theoretical analyses prove the optimality and convergence of this iterative method. Also, the crowd-sensing experiments show that, CARM can achieve an accurate RSS map, decreasing the average error from 19.8 to 8.5 dBm.

Counting the number of tags is a fundamental issue and has a wide range of applications in RFID systems. Most existing protocols, however, only apply to the scenario where a single reader counts the number of tags covered by its radio, or at most the union of tags covered by multiple readers. They are unable to achieve more complex counting objectives, i.e., counting the number of tags in a composite set expression such as (S1∪ S2) - (S3∩ S4). This type of counting has realistic significance as it provides more diversity than existing counting scenario, and can be applied in various applications. We formally introduce the RFID composite counting problem, which aims at counting the tags in an arbitrary set expression and obtain its strong lower bounds on the communication cost. We then propose a generic Composite Counting Framework (CCF) that provides estimates for any set expression with desired accuracy. The communication cost of CCF is proved to be within a small factor from the optimal. We build a prototype system for CCF using USRP software defined radio and Intel WISP computational tags. Also, extensive simulations are conducted to evaluate the performance of CCF. The experimental results show that CCF is generic, accurate and time-efficient.

Radio Frequency Identification (RFID) technology plays an important role in supply chain logistics and inventory control. In these applications, a series of scanning operations at different locations are often needed to cover the entire inventory (tags). In such continuous scanning scenario, adjacent scans inevitably read overlapping tags multiple times. Most existing methods suffer from low scanning efficiency when the overlap is small, since they do not distinguish the size of overlap which is an important factor of scanning performance. In this paper, we analytically unveil the fundamental relationship between the performance of continuous scanning and the size of overlap, deriving a critical threshold for the selection of scanning strategy. Further, we design an accurate estimator to approximate the overlap. Combining the estimate and a compact data structure, an adaptive scanning scheme is introduced to achieve low communication time. Through detailed analysis and extensive simulations, we demonstrate that the proposed scheme significantly outperforms previous approach in total scanning time.

Opportunistic routing, offering relatively efficient and adaptive forwarding in low-duty-cycled sensor networks, generally allows multiple nodes to forward the same packet simultaneously, especially in networks with intensive traffic. Uncoordinated transmissions often incur a number of duplicate packets, which are further forwarded in the network, occupy the limited network resource, and hinder the packet delivery performance. Existing solutions to this issue, e.g., overhearing or coordination based approaches, either cannot scale up with the system size, or suffer high control overhead. We present Duplicate-Detectable Opportunistic Forwarding (DOF), a duplicate-free opportunistic forwarding protocol for low-duty-cycled wireless sensor networks. DOF enables senders to obtain the information of all potential forwarders via a slotted acknowledgment scheme, so the data packets can be sent to the deterministic next-hop forwarder. Based on light-weight coordination, DOF explores the opportunities as many as possible and removes duplicate packets from the forwarding process. We implement DOF and evaluate its performance on an indoor testbed with 20 TelosB nodes. The experimental results show that DOF reduces the average duplicate ratio by 90%, compared to state-of-the-art opportunistic protocols, and achieves 61.5% enhancement in network yield and 51.4% saving in energy consumption.

By attaching RFID tags to products, supply chain participants can identify products and create product data to record the product particulars in transit. Participants along the supply chain share their product data to enable information exchange and support critical decisions in production operations. Such an information sharing essentially requires a data access control mechanism when the product data relate to sensitive business issues. However, existing access control solutions are ill-suited to the RFID-enabled supply chain, as they are not scalable in handling a huge number of tags, introduce vulnerability to the product data, and perform poorly to support privilege revocation of product data. We present a new scalable industry data access control system that addresses these limitations. Our system provides an item-level data access control mechanism that defines and enforces access policies based on both the participants' role attributes and the products' RFID tag attributes. Our system further provides an item-level privilege revocation mechanism by allowing the participants to delegate encryption updates in revocation operation without disclosing the underlying data contents. We design a new updatable encryption scheme and integrate it with ciphertext policy-attribute-based encryption to implement the key components of our system.

Delay is an important metric to understand and improve system performance. While existing approaches focus on aggregated delay statistics in pre-programmed granularity and provide results such as average and deviation, those approaches may not provide fine-grained delay measurement and thus may miss important delay characteristics. For example, delay anomaly, which is a critical system performance indicator, may not be captured by coarse-grained approaches. We propose a new measurement structure design called order preserving aggregator (OPA). Based on OPA, we can efficiently encode and recover the ordering and loss information by exploiting inherent data characteristics. We then propose a two-layer design to convey both ordering and time stamp, and efficiently derive per-packet delay/loss measurement. We evaluate our approach both analytically and experimentally. The results show that our approach can achieve per-packet delay measurement with an average of per-packet relative error at 2%, and an average of aggregated relative error at 10-5, while introducing additional communication overhead in the order of 10-4in terms of number of packets. While at a low data rate, the computation overhead of OPA is acceptable. Reducing the computation and communication overhead under high data rate, to make OPA more practical in real applications, will be our future direction.

Detecting and diagnosing anomalies in networked embedded systems like sensor networks is a very difficult task, due to the variable workloads and severe resource constraints. In this paper, we focus on how to aid bug diagnosis after the system has been deployed. We notice that most node-level debugging tools can provide detailed program information inside the node but fail to detect when and where a problem occurs in the network. On the other hand, most network-level diagnosis tools can effectively detect a problem from the network but fail to narrow down the problem within the node because they lack detailed program information. To close the gap, we propose D2, a new method for post-deployment anomaly detection and diagnosis in networked embedded systems by combining program profiling and symptom mining. D2 employs binary instrumentation to perform lightweight function count profiling. Based on the statistics, D2 uses PCA (Principal Component Analysis) based approach for automatically detecting network anomalies. Compared with previous methods, D2 is able to point programmers closer to the most likely causes by a novel approach combining statistical tests and program call graph analysis. We implement our method based on TinyOS 2.1.1 and evaluate its effectiveness by case studies in the development of a working sensor network. Results show that our method can aid programmers to diagnose problems quickly in real-world sensor network systems, and at the same time, incurs an acceptable overhead to the running system.

Sensor networks are widely used in various domains like the intelligent transportation systems. Users issue queries to sensors and collect sensing data. Due to the low quality sensing devices or random link failures, sensor data are often noisy. In order to increase the reliability of the query results, continuous queries are often employed. In this work we focus on continuous holistic queries like Median. Existing approaches are mainly designed for non-holistic queries like Average. However, it is not trivial to answer holistic ones due to their non-decomposable property. We first propose two schemes based on the data correlation between different rounds, with one for getting the exact answers and the other one for deriving the approximate results. We then combine the two proposed schemes into a hybrid approach, which is adaptive to the data changing speed. We evaluate this design through extensive simulations. The results show that our approach significantly reduces the traffic cost compared with previous works while maintaining the same accuracy.

Recently, carrying control signals on passing data packets has emerged as a promising direction for efficient control information transmission. With control messages carried on data payload, the extra air time needed for control packets like RTS/CTS is eliminated and thus channel utilization is improved. However, carrying control signals on the data payload of a packet requires the data packet to have a sufficiently large SNR, otherwise both the data packet and the control messages are lost. In this paper, we propose Hitchhike, a technique that utilizes the preamble field to carry control messages. Hitchhike completely decouples the control messages from the payload and therefore the superposition of (multiple) control messages has little adverse effect on the operation of the payload decoding. We implement and evaluate Hitchhike in the USRP2 platform with five nodes. Evaluation results demonstrate the feasibility and effectiveness of Hitchhike. Compared with the state-of-the-art, e.g., side-channel in 802.15.4, Hitchhike improves the detection accuracy of control messages by 40% and reduces the data loss caused by control messages by 15%.

Indoor localization is of great importance to a wide range of applications in the era of mobile computing. Current mainstream solutions rely on Received Signal Strength (RSS) of wireless signals as fingerprints to distinguish and infer locations. However, those methods suffer from fingerprint ambiguity that roots in multipath fading and temporal dynamics of wireless signals. Though pioneer efforts have resorted to motion-assisted or peer-assisted localization, they neither work in real time nor work without the help of peer users, which introduces extra costs and constraints, and thus degrades their practicality. To get over these limitations, we propose Argus, an image-assisted localization system for mobile devices. The basic idea of Argus is to extract geometric constraints from crowdsourced photos, and to reduce fingerprint ambiguity by mapping the constraints jointly against the fingerprint space. We devise techniques for photo selection, geometric constraint extraction, joint location estimation, and build a prototype that runs on commodity phones. Extensive experiments show that Argus triples the localization accuracy of classic RSS-based method, in time no longer than normal WiFi scanning, with negligible energy consumption.

Facing a large number of personal photos and limited resource of mobile devices, cloud plays an important role in photo storing, sharing and searching. Meanwhile, some recent reputation damage and stalk events caused by photo leakage increase people's concern about photo privacy. Though most would agree that photo search function and privacy are both valuable, few cloud system supports both of them simultaneously. The center of such an ideal system is privacy-preserving outsourced image similarity measurement, which is extremely challenging when the cloud is untrusted and a high extra overhead is disliked. In this work, we introduce a framework POP, which enables privacy-seeking mobile device users to outsource burdensome photo sharing and searching safely to untrusted servers. Unauthorized parties, including the server, learn nothing about photos or search queries. This is achieved by our carefully designed architecture and novel non-interactive privacy-preserving protocols for image similarity computation. Our framework is compatible with the state-of-the-art image search techniques, and it requires few changes to existing cloud systems. For efficiency and good user experience, our framework allows users to define personalized private content by a simple check-box configuration and then enjoy the sharing and searching services as usual. All privacy protection modules are transparent to users. The evaluation of our prototype implementation with 31,772 real-life images shows little extra communication and computation overhead caused by our system.

Wireless-based device-free human sensing has raised increasing research interest and stimulated a range of novel location-based services and human-computer interaction applications for recreation, asset security and elderly care. A primary functionality of these applications is to first detect the presence of humans before extracting higher-level contexts such as physical coordinates, body gestures, or even daily activities. In the presence of dense multipath propagation, however, it is non-trivial to even reliably identify the presence of humans. The multipath effect can invalidate simplified propagation models and distort received signal signatures, thus deteriorating detection rates and shrinking detection range. In this paper, we characterize the impact of human presence on wireless signals via ray-bouncing models, and propose a measurable metric on commodity WiFi infrastructure as a proxy for detection sensitivity. To achieve higher detection rate and wider sensing coverage in multipath-dense indoor scenarios, we design a lightweight sub carrier and path configuration scheme harnessing frequency diversity and spatial diversity. We prototype our scheme with standard WiFi devices. Evaluations conducted in two typical office environments demonstrate a detection rate of 92.0% with a false positive of 4.5%, and almost 1x gain in detection range given a minimal detection rate of 90%.

Over the past few years we have observed a phenomenal growth of smartphones. Smartphones are equipped with various hardware and software resources such as Bluetooth, camera and gravity sensors. If these resources are not managed appropriately, it may cause severe problems such as battery drains and system crashes. However, the specifications of resource management are usually implicit. In this paper, we investigate the problem of mining resource management specifications from off-the-shelf apps. Our key insight is that if a set of operations to a resource are frequently performed in a specific order, it must contain the specifications of how to manage the resource. We design a tool named Automatic Resource Specification Miner (ARSM), to automatically extract resource management specifications in smartphones. In our experiments, ARSM can mine tens of rules from 100 top rated Android apps within six hours. Our work is orthogonal to existing studies on diagnosing smartphone apps. With the resource management specifications discovered, ARSM can help them pinpoint more bugs in apps.

Mobile videos contain rich information which could be utilized for various applications, like criminal investigation and scene reconstruction. Today's crowd-sourced mobile video retrieval systems are built on video content comparison, and their wide adoption has been hindered by onerous computation of CV algorithms and redundant networking traffic of the video transmission. In this work, we propose to leverage Field of View(FoV) as a content-free descriptor to measure video similarity with little accuracy loss. Based on FoV, our system can filter out unmatched videos before any content analysis and video transmission, which dramatically cuts down the computation and communication cost for crowd-sourced mobile video retrieval. Moreover, we design a video segmentation algorithm and an R-Tree based indexing structure to further reduce the networking traffic for mobile clients and potentiate the efficiency for the cloud server. We implement a prototype system and evaluate it from different aspects. The results show that FoV descriptors are much smaller and significantly faster to extract and match compared to content descriptors, while the FoV based similarity measurement achieves comparable search accuracy with the content-based method. Our evaluation also shows that the proposed retrieval scheme is scalable with data size and can response in less than 100ms when the data set has tens of thousands of video segments, and the networking traffic between the client and the server is negligible.

In distributed networks such as wireless ad hoc networks, local and lossy logs are often available on individual nodes. We propose REFILL, which analyzes lossy and unsynchronized logs collected from individual nodes and reconstructs the network behaviors. We design an inference engine based on protocol semantics to abstract states on each node. Further we leverage inherent and implicit event correlations in and between nodes to connect interference engines and analyze logs from different nodes. Based on unsynchronized and incomplete logs, REFILL can reconstruct network behavior, recover the network scenario and understand what has happened in the network. We show that the result of REFILL can be used to guide protocol design, network management, diagnosis, etc. We implement REFILL and apply it to a large-scale wireless sensor network project. REFILL provides a detailed per-packet tracing information based on event flows. We show that REFILL can reveal and verify fundamental issues, like locating packet loss positions and root causes. Further, we present implications and demonstrate how to leverage REFILL to enhance network performance.

Many cloud platforms emerge to meet urgent requirements for large-volume personal image store, sharing and search. Though most would agree that images contain rich sensitive information (e.g., People, location and event) and people's privacy concerns hinder their participation into untrusted services, today's cloud platforms provide little support for image privacy protection. Facing large-scale images from multiple users, it is extremely challenging for the cloud to maintain the index structure and schedule parallel computation without learning anything about the image content and indices. In this work, we introduce a novel system PIC: a Privacy-preserving Image search system on Cloud, which is a step towards feasible cloud services which provide secure content-based large-scale image search with fine-grained access control. Users can search on others' images if they are authorized by the image owners. Majority of the computationally intensive jobs are handled by the cloud, and a querier can now simply send the query and receive the result. Specially, to deal with massive images, we design our system suitable for distributed and parallel computation and introduce several optimizations to further expedite the search process. Our security analysis and prototype system evaluation results show that PIC successfully protects the image privacy at a low cost of computation and communication.

Crowdsensing applications require individuals toshare local and personal sensing data with others to produce valuableknowledge and services. Meanwhile, it has raised concernsespecially for location privacy. Users may wish to prevent privacyleak and publish as many non-sensitive contexts as possible.Simply suppressing sensitive contexts is vulnerable to the adversariesexploiting spatio-temporal correlations in users' behavior.In this work, we present PLP, a crowdsensing scheme whichpreserves privacy while maximizes the amount of data collectionby filtering a user's context stream. PLP leverages a conditionalrandom field to model the spatio-temporal correlations amongthe contexts, and proposes a speed-up algorithm to learn theweaknesses in the correlations. Even if the adversaries are strongenough to know the filtering system and the weaknesses, PLPcan still provably preserves privacy, with little computationalcost for online operations. PLP is evaluated and validated overtwo real-world smartphone context traces of 34 users. Theexperimental results show that PLP efficiently protects privacywithout sacrificing much utility.

Doors are important landmarks for indoor positioning systems. Hence an accurate and light-weight door detection approach is highly desired. The state-of-the-art solutions are either vision based or infrastructure based, which incur nontrivial device or management cost. This paper presents a novel approach, Light-weight Magnetic-based Door Detection (LMDD), which only relies on the information from built-in sensors of a smartphone. LMDD detects a door by analyzing the change of magnetic signal and extracting special features caused by doors. It is light-weight in both computation and infrastructure cost. We have implemented a prototype of LMDD that has been installed on various Android phones. Experimental results show that LMDD achieves door detection accuracy of 74% in average, ranging from 66% to 85% in various typical environments such as offices, classrooms, residential houses, and a hospital.

For today's computer users, the mouse plays such an important role that it dominates the interaction interface in personal computer for nearly half a century since it was invented. However, the mouse is gradually unfit for the demand of modern 3D display techniques, e.g. 3D-projection or -screen, for the reason that the relevant interactions are confined in a surface. Although some new methods such as computer vision based techniques attempt to bridge the human-computer barrier, they suffer from many limitations such as ambiguity in multitargets and dependence on light. This paper presents a battery-free device called Tagball for 3D human-computer interaction via RFID tags. Tagball devises a control ball, on which N passive tags are attached, for users to generate two basic kinds of interactive commands: translation and rotation. Instead of locating N tags independently, we model the ball as a whole in a more cooperative way under the circumstance that their geometric relationships are known in advance. In addition, we consider the phase values measured by M RF antennas for these N tags as observations of the ball state. Our key innovations are the studies on motion behaviors of a group of tags by using Extended Kalman Filter, and the implementation based on purely Commercial Off-The-Shelf (COTS) RFID products. The systematical evaluation shows that Tagball traces the ball translation to 1.5cm and identifies ball orientation to 1.8° in 3D space.

To stay competitive, plenty of data mining techniques have been introduced to help stores better understand consumers' behaviors. However, these studies are generally confined within the customer transaction data. Actually, another kind of `deep shopping data', e.g. which and why goods receiving much attention are not purchased, offers much more valuable information to boost the product design. Unfortunately, these data are totally ignored in legacy systems. This paper introduces an innovative system, called TagBooth, to detect commodities' motion and further discover customers' behaviors, using COTS RFID devices. We first exploit the motion of tagged commodities by leveraging physical-layer information, like phase and RSS, and then design a comprehensive solution to recognize customers' actions. The system has been tested extensively in the lab environment and used for half a year in real retail store. As a result, TagBooth generally performs well to acquire deep shopping data with high accuracy.

To meet the demand of more intelligent automation services on smartphone, more and more applications are developed based on users' emotion and personality. It has been a consensus that a relationship exists between personal emotions and usage pattern of smartphone. Most of existing work studies this relationship by learning manually labeled samples collected from smartphone users. The manual labeling process, however, is time-consuming, labor-intensive and money-consuming. To address this issue, we propose iSelf, a system which provides a general service of automatic detection for user's emotions in cold-start conditions with smartphone. Using transfer learning technology, iSelf achieves high accuracy given only a few labeled samples. We also develop a hybrid public/personal inference engine and validation system, so as to make iSelf maintain continuous update. Through extensive experiments, the inferring accuracy is tested about 75% and can be improved increasingly through validation and update.

The continual proliferation of mobile devices has stimulated the development of opportunistic encounter-based networking and has spurred a myriad of proximity-based mobile applications. A primary cornerstone of such applications is to discover neighboring devices effectively and efficiently. Despite extensive protocol optimization, current neighbor discovery modalities mainly rely on radio interfaces, whose energy and wake up delay required to initiate, configure and operate these protocols hamper practical applicability. Unlike conventional schemes that actively emit radio tones, we exploit ubiquitous audio events to discover neighbors passively. The rationale is that spatially adjacent neighbors tend to share similar ambient acoustic environments. We propose AIR, an effective and efficient neighbor discovery protocol via low power acoustic sensing to reduce discovery latency. Especially, AIR substantially increases the discovery probability of the first time they turn the radio on. Compared with the state-of-the-art neighbor discovery protocol, AIR significantly decreases the average discovery latency by around 70%, which is promising for supporting vast proximity-based mobile applications.

The proliferation of mobile computing has prompted WiFi-based indoor localization to be one of the most attractive and promising techniques for ubiquitous applications. A primary concern for these technologies to be fully practical is to combat harsh indoor environmental dynamics, especially for long-term deployment. Despite numerous research on WiFi fingerprint-based localization, the problem of radio map adaptation has not been sufficiently studied and remains open. In this work, we propose AcMu, an automatic and continuous radio map self-updating service for wireless indoor localization that exploits the static behaviors of mobile devices. By accurately pinpointing mobile devices with a novel trajectory matching algorithm, we employ them as mobile reference points to collect real-time RSS samples when they are static. With these fresh reference data, we adapt the complete radio map by learning an underlying relationship of RSS dependency between different locations, which is expected to be relatively constant over time. Extensive experiments for 20 days across 6 months demonstrate that AcMu effectively accommodates RSS variations over time and derives accurate prediction of fresh radio map with average errors of less than 5dB. Moreover, AcMu provides 2x improvement on localization accuracy by maintaining an up-to-date radio map.

WiFi technology has fostered numerous mobile computing applications, such as adaptive communication, finegrained localization, gesture recognition, etc., which often achieve better performance or rely on the availability of Line-Of-Sight (LOS) signal propagation. Thus the awareness of LOS and Non-Line-Of-Sight (NLOS) plays as a key enabler for them. Realtime LOS identification on commodity WiFi devices, however, is challenging due to limited bandwidth of WiFi and resulting coarse multipath resolution. In this work, we explore and exploit the phase feature of PHY layer information, harnessing both space diversity with antenna elements and frequency diversity with OFDM subcarriers. On this basis, we propose PhaseU, a real-time LOS identification scheme that works in both static and mobile scenarios on commodity WiFi infrastructure. Experimental results in various indoor scenarios demonstrate that PhaseU consistently outperforms previous approaches, achieving overall LOS and NLOS detection rates of 94.35% and 94.19% in static cases and both higher than 80% in mobile contexts. Furthermore, PhaseU achieves real-time capability with millisecond-level delay for a connected AP and 1-second delay for unconnected APs, which is far beyond existing approaches.

RFID has been widely adopted as an effective method for anti-counterfeiting. Legacy systems based on security protocol are either too heavy to be affordable by passive tags or suffering from various protocol-layer attacks, e.g. reverse engineering, cloning, side-channel. In this work, we present a novel anti-counterfeiting system, TagPrint, using COTS RFID tags and readers. Achieving a low-cost and offline genuineness validation utilizing passive tags has been a daunting task. Our system achieves these three goals by leveraging a few of federated tags' fingerprints and geometric relationships. In TagPrint, we exploit a new kind of fingerprint, called phase fingerprint, extracted from the phase value of the backscattered signal, provided by the COTS RFID readers. To further solve the separation challenge, we devise a geometric solution to validate the genuineness. We have implemented a prototype of TagPrint using COTS RFID devices. The system has been tested extensively over 6,000 tags. The results show that our new fingerprint exhibits a good fitness of uniform distribution and the system achieves a surprising Equal Error Rate of 0.1% for anti-counterfeiting.

With the rapid development of mobile applications, there is an urgent need for highly efficient indoor localization service. Dedicated systems achieve good accuracy at the cost of deploying special hardware. Fingerprint-based methods avoid maintaining the expensive infrastructure but suffer from intensive labor for site-survey and poor robustness. In this paper, we present Per Loc, an infrastructure-free localization system which leverages rich vision features in indoor environment with high efficiency and accuracy. Per Loc makes use of binocular ranging technique to calculate the depth of a feature point and then figures out its geographical coordinates to build up reference point database, for which we design a filtering scheme to keep the database efficient in storage and search delay. During the localization stage, users simply take a photo of surroundings and feature points are extracted automatically as input for search scheme. Then a fast two-stage search scheme is proposed to find the nearest neighbors of query feature points in reference point database. Based on the perspective projection model, we inversely calculate users' geographical location in real time. We implement the proposed localization system on commercial smartphones as well as laptops and conduct extensive experiments. Per Loc achieves 1.76m of average error in office environment, and 2.2m of average error in shopping mall.

Mobile crowd sourcing (MCS) has grown to be a powerful computation paradigm to harness human power to solve real-world problems. Many commercial MCS platforms have arisen, enabling various novel applications. As crowd workers can be unreliable, a critical issue of these platforms is quality control. Many task assignment approaches have been proposed to increase the quality of crowd sourced tasks by matching workers and tasks in a bipartite graph. However, they fail to apply to MCS platforms where tasks are bound with locations. This paper considers the quality-aware online task assignment problem with location-based tasks. The goal is to optimize tasks' overall quality by assigning appropriate sets of tasks to workers in an online manner. To solve this problem, we propose a probabilistic quality measurement model and a hitchhiking model to characterize workers' behavior. Then we design a polynomial-time online assignment algorithm and prove that the proposed algorithm approximates the offline optimal solution with a competitive ratio of 10/7. Through extensive simulations, we demonstrate the efficiency and effectiveness of our solution.

Through-wall tracking has gained a lot of attentions in civilian applications recently. Many applications would benefit from such device-free tracking, e.g. elderly people surveillance, intruder detection, gaming, etc. In this work, we present a system, named Tadar, for tracking moving objects without instrumenting them us- ing COTS RFID readers and tags. It works even through walls and behind closed doors. It aims to enable a see-through-wall technology that is low-cost, compact, and accessible to civilian purpose. In traditional RFID systems, tags modulate their IDs on the backscatter signals, which is vulnerable to the interferences from the ambient reflections. Unlike past work, which considers such vulnerability as detrimental, our design exploits it to detect surrounding objects even through walls. Specifically, we attach a group of RFID tags on the outer wall and logically convert them into an antenna array, receiving the signals reflected off moving objects. This paper introduces two main innovations. First, it shows how to eliminate the flash (e.g. the stronger reflections off walls) and extract the reflections from the backscatter signals. Second, it shows how to track the moving object based on HMM (Hidden Markov Model) and its reflections. To the best of our knowledge, we are the first to implement a through-wall tracking using the COTS RFID systems. Empirical measurements with a prototype show that Tadar can detect objects behind 5" hollow wall and 8" concrete wall, and achieve median tracking errors of 7.8cm and 20cm in the X and Y dimensions.

Recently a number of systems have been developed to implement and improve the visual communication over screen-camera links. In this paper we study an opposite problem: how to prevent unauthorized users from videotaping a video played on a screen, such as in a theater, while do not affect the viewing experience of legitimate audiences. We propose and develop a light-weight hardware-free system, called Kaleido, that ensures these properties by taking advantage of the limited disparities between the screen-eye channel and the screen-camera channel. Kaleido does not require any extra hardware and is purely based on re-encoding the original video frame into multiple frames used for displaying. We extensively test our system Kaleido using a variety of smartphone cameras. Our experiments confirm that Kaleido preserves the high-quality screen-eye channel while reducing the secondary screen-camera channel quality significantly.

We introduce SmartGuide, a light-weighted and efficient approach to localize and recognize a distant unknown building. Our approach relies on shooting only a single photo of a target building via a smartphone and a local 2D Google map. SmartGuide first extracts a partial top view contour of a building from its side-view photo by applying vanishing point and the Manhattan World Assumption, and then fetches a candidate building set from a local 2D Google map based on smartphone's GPS readings. Partial top view shape, orientation and distance relative to the camera are used as input parameters in a probability model, which adversely recognizes the best candidate building in the local map. Our model is developed based on kernel density estimation that helps reduce noise in the smartphone sensors, such as GPS readings and camera ray direction reported by noisy accelerometer and compass. Experimental results demonstrate that our approach recognizes buildings ranging from 20m to 520m and achieves 92.7% accuracy in downtown areas where the Manhattan World Assumption is applicable. In addition, the processing time is no more than 6 seconds for 87% of cases. Compared with existing building localization schemes, SmartGuide offers numerous advantages. Our method avoids taking multiple photos, intricate 3D reconstruction or any initial deployment cost of database construction, making it faster and less labor-intensive than existing solutions.

Driven by the proliferation of mobile applications, the conflict between data communication requirement and limited battery capacity is becoming sharp on modern smartphones. Offloading mobile traffic from cellular to WiFi is widely recognized as a viable solution to improve the energy efficiency. However, through extensive field experiments, we find WiFi offloading is not always energy efficient and even consumes more energy than cellular network due to link quality variation. In addition, we also observe that practical data transmission deadline requirement and link utilization allows scheduling of data traffic to time periods with good link quality. Accordingly, we propose Q-offload, the first attempt towards energy efficient WiFi offloading with link dynamics. In Q-offload, we propose an iterative framework to achieve energy efficient WiFi offloading by exploiting good link quality while not affecting user experience. We evaluate the performance of Q-offload through both trace-driven analysis and real-world experiments. The results show that it can achieve 33.5%~55.7% energy efficiency improvement, compared with state-of-the-arts under different conditions.

Taxi-sharing allows occupied taxis to pick up new passengers on the fly, promising to reduce waiting time for taxi riders and increase productivity for drivers. However, if not carefully designed, taxi-sharing may cause more harm than benefit - it becomes harder to strike the balance between driver's profit and passenger's quality of service (e.g. travel time, number of strangers that share a taxi, etc.). In this paper, we propose a QoS-aware taxi-sharing system design - QA-Share - by addressing two important challenges. First, QA-Share aims to maximize driver profit and user experience at the same time. Second, QA-Share continuously optimizes these two metrics by dynamically adapting its schedule as new requests arrive, without entering an oscillation state. To address these two challenges, we have formulated the optimization problem using integer linear programming, and derived the optimal solution under a small system scale. When the number of requests and taxis becomes large, we have devised a heuristic algorithm that has a much faster execution time. We have also studied how to minimize oscillations caused by schedule re-calculations by dynamically tuning the update threshold. We have evaluated our approach with real-world dataset in a Chinese city - ZhenJiang - which contains the GPS traces recorded by over 3,000 taxis during a period of three months in 2013. Our results show that the QoS and profit is increased by 38% compared to earlier schemes.

Shopping behavior data are of great importance to understand the effectiveness of marketing and merchandising efforts. Online clothing stores are capable capturing customer shopping behavior by analyzing the click stream and customer shopping carts. Retailers with physical clothing stores, however, still lack effective methods to identify comprehensive shopping behaviors. In this paper, we show that backscatter signals of passive RFID tags can be exploited to detect and record how customers browse stores, which items of clothes they pay attention to, and which items of clothes they usually match with. The intuition is that the phase readings of tags attached on desired items will demonstrate distinct yet stable patterns in the time-series when customers look at, pick up or turn over desired items. We design ShopMiner,, a framework that harnesses these unique spatial-temporal correlations of time-series phase readings to detect comprehensive shopping behaviors. We have implemented a prototype of ShopMiner, with a COTS RFID reader and four antennas, and tested its effectiveness in two typical indoor environments. Empirical studies from two-week shopping-like data show that ShopMiner, could achieve high accuracy and efficiency in customer shopping behavior identification.

Non-invasive human sensing based on radio signals has attracted a great deal of research interest and fostered a broad range of innovative applications of localization, gesture recognition, smart health-care, etc., for which a primary primitive is to detect human presence. Previous works have studied the detection of moving humans via signal variations caused by human movements. For stationary people, however, existing approaches often employ a prerequisite scenario-tailored calibration of channel profile in human-free environments. Based on in-depth understanding of human motion induced signal attenuation reflected by PHY layer channel state information (CSI), we propose DeMan, a unified scheme for non-invasive detection of moving and stationary human on commodity WiFi devices. DeMan takes advantage of both amplitude and phase information of CSI to detect moving targets. In addition, DeMan considers human breathing as an intrinsic indicator of stationary human presence and adopts sophisticated mechanisms to detect particular signal patterns caused by minute chest motions, which could be destroyed by significant whole-body motion or hidden by environmental noises. By doing this, DeMan is capable of simultaneously detecting moving and stationary people with only a small number of prior measurements for model parameter determination, yet without the cumbersome scenario-specific calibration. Extensive experimental evaluation in typical indoor environments validates the great performance of DeMan in various human poses and locations and diverse channel conditions. Particularly, DeMan provides a detection rate of around 95% for both moving and stationary people, while identifies human-free scenarios by 96%, all of which outperforms existing methods by about 30%.

We propose an accurate acoustic direction finding scheme, Swadloon, according to the arbitrary pattern of phone shaking in a rough horizontal plane. Swadloon leverages sensors of the smartphone without the requirement of any specialized devices. Our Swadloon design exploits a key observation: the relative displacement and velocity of the phone-shaking movement corresponds to the subtle phase and frequency shift of the Doppler effects experienced in the received acoustic signal by the phone. Swadloon tracks the displacement of smartphone relative to the acoustic direction with the resolution less than 1 millimeter. The direction is then obtained by combining the velocity from the displacement with the one from the inertial sensors. Major challenges in implementing Swadloon are to measure the displacement precisely and to estimate the shaking velocity accurately when the speed of phone-shaking is low and changes arbitrarily. We propose rigorous methods to address these challenges, and apply Swadloon to several case studies: Phone-to-Phone direction finding, indoor localization and tracking. Our extensive experiments show that the mean error of direction finding is around 2.1 degree within the range of 32 m. For indoor localization, the 90-percentile errors are under 0.92 m. For real-time tracking, the errors are within 0.4 m for walks of 51 m.

Emergency navigation is an emerging application of wireless sensor networks with significant research and social value. In order to ensure the safe and timely navigation of the evacuees, most of the existing works model navigation as a path-planning problem or movement decision support problem and adopt different metrics, such as the shortest route, the minimum exposure path, and the maximum safe distance. Without sufficient consideration of the dynamics of danger, the existing approaches are likely to cause users to move back and forth during navigation, known as oscillation. Frequent oscillations inevitably result in the user remaining in danger for a longer period of time, amplification of the user's panic, and eventual decrease in the chances of survival. In this paper we take users' oscillations in the dynamic environments into account and quantify the local success rate of navigation using a metric called ENO (Expected Number of Oscillations). We then propose OPEN, an oscillation-free navigation approach that minimizes the probability of oscillation and guarantees the success rate of emergency navigation. We implement OPEN and evaluate its performance through the trace from our system and extensive simulations. The results demonstrate that OPEN outperforms the current state-of-the-art approaches with respect to user safety and navigation efficiency.

Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past decades. Most radio-based solutions require a process of site survey, in which radio signatures of an interested area are annotated with their real recorded locations. Site survey involves intensive costs on manpower and time, limiting the applicable buildings of wireless localization worldwide. In this study, we investigate novel sensors integrated in modern mobile phones and leverage user motions to construct the radio map of a floor plan, which is previously obtained only by site survey. Considering user movements in a building, originally separated RSS fingerprints are geographically connected by user moving paths of locations where they are recorded, and they consequently form a high dimension fingerprint space, in which the distances among fingerprints are preserved. The fingerprint space is then automatically mapped to the floor plan in a stress-free form, which results in fingerprints labeled with physical locations. On this basis, we design LiFS, an indoor localization system based on off-the-shelf WiFi infrastructure and mobile phones. LiFS is deployed in an office building covering over 1,600 m2, and its deployment is easy and rapid since little human intervention is needed. In LiFS, the calibration of fingerprints is crowdsourced and automatic. Experiment results show that LiFS achieves comparable location accuracy to previous approaches even without site survey.

Screen locking/unlocking is important for modern smart phones to avoid the unintentional operations and secure the personal stuff. Once the phone is locked, the user should take a specific action or provide some secret information to unlock the phone. The existing unlocking approaches can be categorized into four groups: motion, password, pattern, and fingerprint. Existing approaches do not support smart phones well due to the deficiency of security, high cost, and poor usability. We collect 200 users' handwaving actions with their smart phones and discover an appealing observation: the waving pattern of a person is kind of unique, stable and distinguishable. In this paper, we propose OpenSesame, which employs the users' waving patterns for locking/unlocking. The key feature of our system lies in using four fine-grained and statistic features of handwaving to verify users. Moreover, we utilize support vector machine (SVM) for accurate and fast classification. Our technique is robust compatible across different brands of smart phones, without the need of any specialized hardware. Results from comprehensive experiments show that the mean false positive rate of OpenSesame is around 15 percent, while the false negative rate is lower than 8 percent.

Existing methods in RFID systems often employ presence or absence fashion to detect the tags' motions, so they cannot meet motion detection requirement in many applications. Our recent observations suggest that the signal strength backscattered from the tag is hypersensitive to its position, inspiring us to perceive the tag motion through its radio signal strength changes. Motion perception is not trivial and challenged by weak stability of strength in that any other interference or noise may incur significant changes as well, resulting in high false positives. To tackle this issue, we propose to model the strength via the Mixture of Gaussian Model (MoG). The problem is thus converted to foreground segment in computer vision with the help of Strength Image, where the technique of MoG based background subtraction is employed. We then implement a prototype using commercial off-the-shelf products. The evaluation results show that the slightest tag motion (~10 cm) can be precisely perceived, and the accuracy is up to 92.34 percent while the false positive is suppressed under 0.5 percent.

Many proximity-based mobile social networks are developed to facilitate connections between any two people, or to help a user to find people with a matched profile within a certain distance. A challenging task in these applications is to protect the privacy of the participants’ profiles and communications. In this paper, we design novel mechanisms, when given a preference-profile submitted by a user, that search persons with matching-profile in decentralized mobile social networks. Meanwhile, our mechanisms establish a secure communication channel between the initiator and matching users at the time when a matching user is found. These techniques can also be applied to conduct privacy preserving keywords based search without any secure communication channel. Our analysis shows that our mechanism is privacy-preserving (no participants’ profile and the submitted preference-profile are exposed), verifiable (both the initiator and any unmatched user cannot cheat each other to pretend to be matched), and efficient in both communication and computation. Extensive evaluations using real social network data, and actual system implementation on smart phones show that our mechanisms are significantly more efficient than existing solutions.

Mobile sensing apps have proliferated rapidly over the recent years. Most of them rely on inference components heavily for detecting interesting activities or contexts. Existing work implements inference components using traditional models designed for balanced data sets, where the sizes of interesting (positive) and non-interesting (negative) data are comparable. Practically, however, the positive and negative sensing data are highly imbalanced. For example, a single daily activity such as bicycling or driving usually occupies a small portion of time, resulting in rare positive instances. Under this circumstance, the trained models based on imbalanced data tend to mislabel positive ones as negative. In this paper, we propose a new inference framework SLIM based on several machine learning techniques in order to accommodate the imbalanced nature of sensing data. Especially, guided under-sampling is employed to obtain balanced labelled subsets, followed by a similarity-based sampling that draws massive unlabelled data to enhance training. To the best of our knowledge, SLIM is the first model that considers data imbalance in mobile sensing. We prototype two sensing apps and the experimental results show that SLIM achieves higher recall (activity recognition rate) while maintaining the precision compared with five classical models. In terms of the overall recall and precision, SLIM is around 12 percent better than the compared solutions on average.

In order to simultaneously achieve good energy efficiency and high packet delivery performance, a multihop forwarding scheme should generally involve three design elements: media access mechanism, link estimation scheme, and routing strategy. Disregarding the low-duty-cycle nature of media access often leads to overestimation of link quality. Neglecting the bursty loss characteristic of wireless links inevitably consumes much more energy than necessary and underutilizes wireless channels. The routing strategy, if not well tailored to the above two factors, results in poor packet delivery performance. In this paper, we propose L2, a practical design of data forwarding in low-duty-cycle wireless sensor networks. L2addresses link burstiness by employing multivariate Bernoulli link model. Further incorporated with synchronized rendezvous, L2enables sensor nodes to work in a lazy mode, keep their radios off most of the time, and realize highly reliable forwarding by scheduling very limited packet transmissions. We implement L2on a real sensor network testbed. The results demonstrate that L2outperforms state-of-the-art approaches in terms of energy efficiency and network yield.

We present a comprehensive delay performance measurement and analysis in a large-scale wireless sensor network. We build a lightweight delay measurement system and present a robust method to calculate the per-packet delay. We show that the method can identify incorrect delays and recover them with a bounded error. Through analysis of delay and other system metrics, we seek to answer the following fundamental questions: What are the spatial and temporal characteristics of delay performance in a real network? What are the most important impacting factors, and is there any practical model to capture those factors? What are the implications to protocol designs? In this paper, we identify important factors from the data trace and show that the important factors are not necessarily the same with those in the Internet. Furthermore, we propose a delay model to capture those factors. We revisit several prevalent protocol designs such as Collection Tree Protocol, opportunistic routing, and Dynamic Switching-based Forwarding and show that our model and analysis are useful to practical protocol designs.

Network diagnosis is crucial in managing a wireless sensor network (WSN) since many network-related faults, such as node and link failures, can easily happen. Diagnosis tools usually consist of two key components, information collection and root-cause deduction, while in most cases information collection process is independent with root-cause deduction. This results in either redundant information which might pose high communication burden on WSNs, or incomplete information for root-cause inference that leads false judgments. To address the issue, we propose DID, a directional diagnosis approach, in which the diagnosis information acquirement is guided by the fault inference process. Through several rounds of incremental information probing and fault reasoning, root causes of the network abnormalities with high credibility are deduced. We employ a node tracing scheme to reconstruct the topical topology of faulty regions and build the inference model accordingly. We implement the DID approach in our forest monitoring sensor network system, GreenOrbs. Experimental results validate the scalability and effectiveness of this design.

How to shorten the time for channel waiting is critical to avoid network contention. Traditional MAC protocols with CSMA often assume that a transmission must be deferred if the channel is busy, so they focus more on the optimization of serial transmission performance. Recent advances in physical layer, however, allows a receiver to reengage onto a stronger incoming signal from an ongoing transmission or interference, and thus shows the potential of parallel transmissions. Indeed, even if the channel is busy, a node has opportunities to carry out a successful transmission. In this study, we propose opportunistic concurrency (OPC), a new MAC layer scheme, which enables sensor nodes to capture the opportunistic concurrency and carry out parallel transmissions instead of always waiting for a clear channel. Based on local concurrency map, which encodes the interactions among different links, OPC utilizes concurrency control algorithm to make transmission decision distributedly. Our experiments on a testbed consisting of 60 TelosB sensor motes identify the transmission opportunities in WSNs with OPC. Evaluation results show that OPC achieves a 17 percent reduction in packet latency, a 9.4 percent addition in throughput and a 10 percent reduction in power consumption compared with existing approaches.

Traditional approaches for wireless sensor network diagnosis are mainly sink-based. They actively collect global evidences from sensor nodes to the sink so as to conduct centralized analysis at the powerful back-end. On the one hand, long distance proactive information retrieval incurs huge transmission overhead; On the other hand, due to the coupling effect between diagnosis component and the application itself, sink often fails to obtain complete and precise evidences from the network, especially for the problematic or critical parts. To avoid large overhead in evidence collection process, self-diagnosis injects fault inference modules into sensor nodes and let them make local decisions. Diagnosis results from single nodes, however, are generally inaccurate due to the narrow scope of system performances. Besides, existing self-diagnosis methods usually lead to inconsistent results from different inference processes. How to balance the workload among the sensor nodes in a diagnosis task is a critical issue. In this work, we present a new in-network diagnosis approach named Local-Diagnosis (LD2), which conducts the diagnosis process in a local area. LD2 achieves diagnosis decision through distributed evidence fusion operations. Each sensor node provides its own judgements and the evidences are fused within a local area based on the Dempster-Shafer theory, resulting in the consensus diagnosis report. We implement LD2 on TinyOS 2.1 and examine the performance on a 50 nodes indoor testbed.

Constructive interference (CI) enables concurrent transmissions to interfere non-destructively, so as to enhance network concurrency. In this paper, we propose deliberate synchronized constructive interference (Disco), which ensures concurrent transmissions of an identical packet to synchronize more precisely than traditional CI. Disco envisions concurrent transmissions to positively interfere at the receiver, and potentially allows orders of magnitude reductions in energy consumption and improvements in link quality. We also theoretically introduce a sufficient condition to construct Disco with IEEE 802.15.4 radio for the first time. Moreover, we propose Triggercast, a distributed middleware service, and show it is feasible to generate Disco on real sensor network platforms like TMote Sky. To synchronize transmissions of multiple senders at the chip level, Triggercast effectively compensates propagation and radio processing delays, and has 95th percentile synchronization errors of at most 250 ns. Triggercast also intelligently decides which co-senders to participate in simultaneous transmissions, and aligns their transmission time to maximize the overall link Packet Reception Ratio (PRR), under the condition of maximal system robustness. Extensive experiments in real testbeds demonstrate that Triggercast significantly improves PRR from 5 to 70 percent with seven concurrent senders. We also demonstrate that Triggercast provides 1.3χ PRR performance gains in average, when it is integrated with existing data forwarding protocols.

Many wireless applications urgently demand an efficient neighbor discovery protocol to build up bridges connecting user themselves or to some service providers. However, due to intrinsic constraints of wireless devices, e.g., limited energy and error of clock synchronization, there is still absence of effective and efficient neighbor discovery protocols in the literature. In this work, we propose neighbor discovery protocols for the following two problems. First, we study Asynchronous Symmetry Neighbor Discovery problem, in which potential neighbor devices with asynchronous time clocks but the same duty cycle aim to find each other. Second, we propose an efficient protocol (utilizing Bouncing strategy) named BlindDatewith guaranteed worst-case performance 9/10 (1+δ)2x2where δ is a small fraction of the length of a time slot unit and 1/x is the duty cycle. Third, we extend this strategy to address Asynchronous Asymmetry Neighbor Discovery problem, in which both the time clock and the duty cycles of potential neighbors are considered to be heterogeneous. We conduct extensive experiments and simulations to examine the feasibility and efficiency of the proposed protocols, and results show that BlindDate greatly outperforms existing approaches in average-case. Compared with known protocols, BlindDate also achieves a better worst-case discovery latency bound (e.g., 10 percent performance gain comparing with Searchlight [1]).

Global positioning system (GPS) has enabled a number of geographical applications over many years. Quite a lot of location-based services, however, still suffer from considerable positioning errors of GPS (usually 1 to 20 m in practice). In this study, we design and implement a high-accuracy global positioning solution based on GPS and human mobility captured by mobile phones. Our key observation is that smartphone-enabled dead reckoning supports accurate but local coordinates of users' trajectories, while GPS provides global but inconsistent coordinates. Considering them simultaneously, we devise techniques to refine the global positioning results by fitting the global positions to the structure of locally measured ones, so the refined positioning results are more likely to elicit the ground truth. We develop a prototype system, named GloCal, and conduct comprehensive experiments in both crowded urban and spacious suburban areas. The evaluation results show that GloCal can achieve 30 percent improvement on average error with respect to GPS. GloCal uses merely mobile phones and requires no infrastructure or additional reference information. As an effective and light-weight augmentation to global positioning, GloCal holds promise in real-world feasibility.

With widespread usages of smart phones, participatory sensing becomes mainstream, especially for applications requiring pervasive deployments with massive sensors. However, the sensors on smart phones are prone to the unknown measurement errors, requiring automatic calibration among uncooperative participants. Current methods need either collaboration or explicit calibration process. However, due to the uncooperative and uncontrollable nature of the participants, these methods fail to calibrate sensor nodes effectively. We investigate sensor calibration in monitoring pollution sources, without explicit calibration process in uncooperative environment. We leverage the opportunity in sensing diversity, where a participant will sense multiple pollution sources when roaming in the area. Further, inspired by expectation maximization (EM) method, we propose a two-level iterative algorithm to estimate the source presences, source parameters and sensor noise iteratively. The key insight is that, only based on the participatory observations, we can “calibrate sensors without explicit or cooperative calibrating process”. Theoretical analysis proves that, our method can converge to the optimal estimation of sensor noise, where the likelihood of observations is maximized. Also, extensive simulations show that, ours improves the estimation accuracy of sensor bias up to 20 percent and that of sensor noise deviation up to 30 percent, compared with three baseline methods.

Prior work on anti-collision for radio frequency identification (RFID) systems usually schedule adjacent readers to exclusively interrogate tags for avoiding reader collisions. Although such a pattern can effectively deal with collisions, the lack of readers' collaboration wastes numerous time on the scheduling process and dramatically degrades the throughput of identification. Even worse, the tags within the overlapped interrogation regions of adjacent readers (termed as contentious tags), even if the number of such tags is very small, introduce a significant delay to the identification process. In this paper, we propose a new strategy for collision resolution. First, we shelve the collisions and identify the tags that do not involve reader collisions. Second, we perform a joint identification, in which adjacent readers collaboratively identify the contentious tags. In particular, we find that neighboring readers can cause a new type of tag collision, cross-tag-collision, which may impede the joint identification. We propose a protocol stack, named Season, to undertake the tasks in two phases and solve the cross-tag-collision. We conduct extensive simulations and preliminary implementation to demonstrate the efficiency of our scheme. The results show that our scheme can achieve above 6× improvement on the identification throughput in a large-scale dense reader environment.

In this work, we address the problem of distributed expert finding using chains of social referrals and profile matching with only local information in online social networks. By assuming that users are selfish, rational, and have privately known cost of participating in the referrals, we design a novel truthful efficient mechanism in which an expert-finding query will be relayed by intermediate users. When receiving a referral request, a participant will locally choose among her neighbors some user to relay the request. In our mechanism, several closely coupled methods are carefully designed to improve the performance of distributed search, including, profile matching, social acquaintance prediction, score function for locally choosing relay neighbors, and budget estimation. We conduct extensive experiments on several data sets of online social networks. The extensive study of our mechanism shows that the success rate of our mechanism is about 90 percent in finding closely matched experts using only local search and limited budget, which significantly improves the previously best rate 20 percent. The overall cost of finding an expert by our truthful mechanism is about 20 percent of the untruthful methods, e.g., the method that always selects high-degree neighbors. The median length of social referral chains is 6 using our localized search decision, which surprisingly matches the well-known small-world phenomenon of global social structures.

In large-scale wireless sensor networks, faulty link detection plays a critical role in network diagnosis and management. Most potential network bottlenecks such as network partition and routing errors can be detected by link scan. Since sequentially checking all potential links incurs high transmission and storage cost, we propose a passive scheme Link Scanner (LS) for monitoring wireless links. As we know, to maintain a sensor network running in a normal condition, many applications in flooding manner are necessary, such as time synchronization, reprogramming, protocol update, etc. During such regular flooding processes that for other purposes originally, LS passively collects hop counts of received probe messages at sensor nodes. Based on the observation that faulty links can result in mismatch between received hop counts and network topology, LS deduces all links' status with a probabilistic model. We evaluate our scheme by carrying out experiments on a testbed with 60 TelosB motes and conducting extensive simulation tests. A real outdoor system is also deployed to verify that LS can be reliably applied to surveillance networks.

Wireless LANs, particularly WiFi, have been pervasively deployed and have fostered myriad wireless communication services and ubiquitous computing applications. A primary concern in designing these applications is to combat harsh indoor propagation environments, particularly Non-Line-Of-Sight (NLOS) propagation. The ability to identify the existence of the Line-Of-Sight (LOS) path acts as a key enabler for adaptive communication, cognitive radios, and robust localization. Enabling such capability on commodity WiFi infrastructure, however, is prohibitive due to the coarse multipath resolution with MAC-layer received signal strength. In this paper, we propose two PHY-layer channel-statistics-based features from both the time and frequency domains. To further break away from the intrinsic bandwidth limit of WiFi, we extend to the spatial domain and harness natural mobility to magnify the randomness of NLOS paths while retaining the deterministic nature of the LOS component. We propose LiFi, a statistical LOS identification scheme with commodity WiFi infrastructure, and evaluate it in typical indoor environments covering an area of 1500 m2. Experimental results demonstrate that LiFi achieves an overall LOS detection rate of 90.42% with a false alarm rate of 9.34% for the temporal feature and an overall LOS detection rate of 93.09% with a false alarm rate of 7.29% for the spectral feature.

The emanations of electronic and mechanical devices have raised serious privacy concerns. It proves possible for an attacker to recover the keystrokes by acoustic signal emanations. Most existing malicious applications adopt context-based approaches, which assume that the typed texts are potentially correlated. Those approaches often incur a high cost during the context learning stage, and can be limited by randomly typed contents (e.g., passwords). Also, context correlations can increase the risk of successive false recognition. We present a context-free and geometry-based approach to recover keystrokes. Using off-the-shelf smartphones to record acoustic emanations from keystrokes, this design estimates keystrokes' physical positions based on the Time Difference of Arrival (TDoA) method. We conduct extensive experiments and the results show that more than 72.2\% of keystrokes can be successfully recovered.

Efficient tag identification is fundamentally required in large-scale RFID systems. Tag signal collision degrades identification efficiency as tag IDs involved in collision cannot be decoded. The situation becomes even worse in large-scale RFID systems when tag cardinality booms. Existing anti-collision protocols focus on either reducing collision probability or adopting spread spectrum techniques. Unfortunately, the former approach cannot resolve collision radically and the latter one occupies extra bandwidth resources. To address these issues, we propose to resolve tag collision using orthogonal Walsh code, in which tags map their IDs to a group of Walsh codes and transmit them sequentially. The reader can retrieve tag IDs by inverse mapping even under collision circumstances. We further design a new efficient tag identification protocol, Wonder, which reduces identification time without spreading the bandwidth. We conduct extensive simulations to examine its effectiveness and the results show that our protocol significantly improves identification efficiency over previous anti-collision protocols.

Sleep quality plays a significant role in personal health. A great deal of effort has been paid to design sleep quality monitoring systems, providing services ranging from bedtime monitoring to sleep activity detection. However, as sleep quality is closely related to the distribution of sleep duration over different sleep stages, neither the bedtime nor the intensity of sleep activities is able to reflect sleep quality precisely. To this end, we present Sleep Hunter, a mobile service that provides a fine-grained detection of sleep stage transition for sleep quality monitoring and intelligent wake-up call. The rationale is that each sleep stage is accompanied by specific yet distinguishable body movements and acoustic signals. Leveraging the built-in sensors on smartphones, Sleep Hunter integrates these physical activities with sleep environment, inherent temporal relation and personal factors by a statistical model for a fine-grained sleep stage detection. Based on the duration of each sleep stage, Sleep Hunter further provides sleep quality report and smart call service for users. Experimental results from over 30 sets of nocturnal sleep data show that our system is superior to existing actigraphy-based sleep quality monitoring systems, and achieves satisfying detection accuracy compared with dedicated polysomnography-based devices.

Crossroad is among the most dangerous parts outside for the visually impaired people. Numerous studies have exploited navigating systems for the visually impaired community, providing services ranging from block detection, route planning to realtime localization. However, none of them have addressed the safety issue in crossroad and integrated three key factors necessary for a practical crossroad navigation system: detecting the crossroad, locating zebra patterns, and guiding the user within zebra crossing when passing the road. Our CrossNavi application responds to these needs, providing an integrated crossroad navigation service that incorporates all the essential functionalities mentioned above. The overall service is fulfilled by the collaboration of built-in sensors on commodity phones, and requires minimal human participation. We describe the technical aspects of its design, implementation, interface, and further improvements to make the system practical on a wider basis. Experimental results from three visually impaired volunteers show that the system exhibits promising behavior in both urban and rural areas.

Being embedded in the physical world, wireless sensor networks (WSNs) present a wide range of failures, due to environment conditions, hardware limitations and software uncertainties, and so on. Once deployed, the interactivity of a WSN greatly decreases, which leads to limited visibility of network performance for managers to investigate sensor behaviors. Existing evidence-based approaches aim to explain particular network symptoms based on expert knowledge and heuristic experiences, which degrade diagnosis accuracy and perform unreliably. These diagnosis models define a limited group of network failures, emphasizing on expert knowledge too much, and thus fail to be adopted to different applications. In this work, we propose VN2, a novel tool to enhance the visibility of network performance. VN2 quantifies a node's state in terms of variation of 43 metrics, and trains a representative matrix of network exceptions with Non-negative Matrix Factorization (NMF) model. With this matrix, when a new network state coming up, VN2 automatically attributes abnormal symptoms to one or more root causes. We implement VN2 on test bed and real system traces. Experimental results show that VN2 models network exceptions involving small subsets of root causes, and the interpretation of root causes help us understand network behaviors in details.

Counting the number of RFID tags is a fundamental issue and has a wide range of applications in RFID systems. Most existing protocols, however, only apply to the scenario where a single reader counts the number of tags covered by its radio, or at most the union of tags covered by multiple readers. They are unable to achieve more complex counting objectives, i.e., counting the number of tags in a composite set expression such as (S_1 big cup S_2) - (S_3 big cap S_4). This type of counting has realistic significance since it provides more diversity than existing counting scenario, and can be applied in various applications. In this paper, we formally introduce the RFID composite counting problem, which aims at counting the tags in arbitrary set expression. We obtain strong lower bounds on the communication cost of composite counting. We then propose a generic Composite Counting Framework (CCF) that provides estimates for any set expression with desired accuracy. The communication cost of CCF is proved to be within a small factor from the optimal. We build a prototype system for CCF using USRP software defined radio and Intel WISP computational tags. Also, extensive simulations are conducted to evaluate the performance of CCF. The experimental results show that CCF is generic, accurate and time-efficient.

In a large-scale wireless sensor network, thousands of sensor nodes periodically generate and forward data back to the sink. In our recent outdoor deployment, we observe that some bottleneck nodes can greatly determine other nodes' data collection ratio, and thus affect the whole network performance. To figure out the importance of a node in data collection, the manager needs to understand the interactive behaviors among the parent and child nodes. To address this issue, we present a management tool BOND (Bottleneck Node Detector). We introduce the concept of Node Dependence to characterize how much a node relies on each of its parent nodes. BOND models the routing process as a Hidden Markov Model, and uses a machine learning approach to learn the state transition probabilities in this model based on the observed traces. BOND utilizes Node Dependence to explore the hidden bottleneck nodes in the network. Moreover, we can predict how adding or removing the sensor nodes would impact the data flow, thus avoid data loss and flow congestion in redeployment. We implement our tool on real hardware and deploy it in an outdoor system. Our extensive experiments show that BOND infers the Node Dependence with an average accuracy of more than 85%.

By attaching RFID tags to products, supply chain participants can identify products and create product data to record the product particulars in transit. Participants along the supply chain share their product data to enable information exchange and support critical decisions in production operations. Such an information sharing essentially requires a data access control mechanism when the product data relates to sensitive business issues. However, existing access control solutions are ill suited to the RFID-enabled supply chain, as they are not scalable in handling a huge number of tags, introduce vulnerability to the product data, and performs poorly to support privilege revocation of product data. We present a new scalable data access control system that addresses these limitations. Our system provides an item-level data access control mechanism that defines and enforces access policies based on both the participants' role attribute and the products' RFID tag attribute. Our system further provides an item-level privilege revocation mechanism by allowing the participants to delegate encryption updates in revocation operation without disclosing the underlying data contents. We design a new updatable encryption scheme and integrate it with Cipher text Policy-Attribute Based Encryption (CP-ABE) to implement the key components of our system.

Delay is an important metric to understand and improve system performance. While existing approaches focus on aggregate delay statistics in pre-programmed granularity, providing only statistical results such as averages and deviations, those approaches fail to provide fine-grained delay measurement at a flexible level and thus may miss important delay characteristics. For example, delay anomalies, which are critical system performance indicators, may not be captured by existing coarse grained approaches. In this work, we propose a fine-grained delay measurement approach based on a new measurement structure design called order preserving aggregator (OPA). OPA can efficiently encode the ordering and loss information by exploiting inherent data characteristics. Based on OPA, we propose a two layer design to convey both ordering and time stamp information, and then derive per-packet delay/loss measurement with a small overhead. We evaluate our approach both analytically and experimentally with widely used real-world data sets. The results show that our approach can achieve accurate per-packet delay measurement with an average of per-packet relative error at 2%, and an average of aggregated relative error at 10-5, while introducing less than 4 × 10-4additional overhead.

Time synchronization plays an important role in wireless sensor networks (WSNs). Due to the unique features of WSNs such as remote deployment, low-cost hardware, and restrictive energy supply, accurate and robust time synchronization is still a challenging task. Many approaches have been proposed to improve the performance and efficiency of time synchronization. Existing schemes, however, do not pay enough attention to the impacts of varying network topology properties, including network diameter, degree distribution, and the like. They can experience unexpected performance degradation in many real systems. To address these issues, we present a novel approach, which tries to improve the performance of existing time synchronization algorithms by introducing a virtual overlay. We propose an integrated model, called topo-refiner, which encodes the impact of different network topology features to the precision and robustness of time synchronization. Also, we design a novel optimization algorithm to build a virtual overlay from the underlying communication network. Our approach is orthogonal to existing clock synchronization methods, and can improve their performance by operating these methods atop the virtual layer. Finally, in order to verify the effectiveness of our approach, we conduct extensive simulations on synthetic and real system traces, as well as testbed experiments. Results show that topo-refiner is practical, and quickly adapts to varying time synchronization accuracy requirements for applications in WSNs.

Device-free passive detection is an emerging technology to detect whether there exists any moving entities in the area of interests without attaching any device to them. It is an essential primitive for a broad range of applications including intrusion detection for safety precautions, patient monitoring in hospitals, child and elder care at home, etc. Despite of the prevalent signal feature Received Signal Strength (RSS), most robust and reliable solutions resort to finer-grained channel descriptor at physical layer, e.g., the Channel State Information (CSI) in the 802.11n standard. Among a large body of emerging techniques, however, few of them have explored full potentials of CSI for human detection. Moreover, space diversity supported by nowadays popular multi-antenna systems are not investigated to the comparable extent as frequency diversity. In this paper, we propose a novel scheme for device-free PAssive Detection of moving humans with dynamic Speed (PADS). Both amplitude and phase information of CSI are extracted and shaped into sensitive metrics for target detection; and CSI across multi-antennas in MIMO systems are further exploited to improve the detection accuracy and robustness. We prototype PADS on commercial WiFi devices and experiment results in different scenarios demonstrate that PADS achieves great performance improvement in spite of dynamic human movements.

Smartphones nowadays are installed with diverse applications, each of which consumes energy and bandwidth. As more and more applications are crowded into a smart- phone, they cause serious problems with regard to battery life and bandwidth utilization. Existing proposals to tackle such challenges usually resort to two ways: avoiding energy- consuming network activities or improving communication efficiency in terms of power consumption. Those approaches either affect the smartphone users' experience, or offer little benefit in prolonging the battery life. Motivated by insightful understanding of users' habit, we in this paper propose a novel approach to orchestrate network activities of smartphone applications, based on user's habit. We implement our approach on smartphones as a middleware service called NetMaster. The performance evaluation with real traces shows that NetMaster reduces energy consumption of network activities by 77.8% in average and increases network bandwidth utilization by over 200%. The user experience is surprisingly well preserved. The chance of undesired interrupt during normal usage is less than 1%.

Cloud storage services such as Dropbox, Google Drive, and Microsoft OneDrive provide users with a convenient and reliable way to store and share data from anywhere, on any device, and at any time. The cornerstone of these services is the data synchronization (sync) operation which automatically maps the changes in users' local filesystems to the cloud via a series of network communications in a timely manner. If not designed properly, however, the tremendous amount of data sync traffic can potentially cause (financial) pains to both service providers and users.

Accurate maps are increasingly important with the growth of smart phones and the development of location-based services. Several crowdsourcing based map generation protocols have been proposed that rely on volunteers to provide their traces. Being creative, however, those methods pose a significant threat to user privacy as the traces can easily imply user behavior patterns. On the flip side, crowdsourcing-based map generation method does need individual locations. To address the issue, we present a systematic participatory-sensing-based high-quality map generation scheme, PMG, that meets the privacy demand of individual users. In this approach, individual users merely need to upload unorganized sparse location points so as to reduce the risk of exposing privacy, while the server generates accurate maps with unorganized points, instead of user traces. Experiments show that our solution is able to generate high-quality maps for a real environment that is robust to noisy data. The difference between the ground-truth map and the produced map is <; 10m, even when the collected locations are about 32m apart after clustering for the purpose of removing noise.

Indoor navigation has received much attention in academics and industry in recent years. Previous methods often attempt to locate users with various localization algorithms in combination with an indoor map, so they need expensive infrastructures deployed in advance. In this study, we propose to utilize existing indoor objects attached RFID tags and the reader to navigate the user to the destination, without need of any extra hardware. The key insight is that the personal movement takes an impact on the Doppler frequency shift values collected from the indoor objects when getting close to the tag. Such local human-item spatial relation is leveraged to infer the users position and further navigate user to destination step by step. We implement a prototype navigation system, called RollCaller, and conduct comprehensive experiments to examine its performance.

We propose an accurate acoustic direction finding scheme, Swadloon, according to the arbitrary pattern of phone shaking in rough horizontal plane. Swadloon tracks the displacement of smartphone relative to the acoustic direction with the resolution less than 1 millimeter. The direction is then obtained by combining the velocity from the displacement with the one from the inertial sensors. Major challenges in implementing Swadloon are to measure the displacement precisely and to estimate the shaking velocity accurately when the speed of phone-shaking is low and changes arbitrarily. We propose rigorous methods to address these challenges, and apply Swadloon to several case studies: Phone-to-Phone direction finding, indoor localization and tracking. Our extensive experiments show that the mean error of direction finding is around 2.1° within the range of 32 m. For indoor localization, the 90-percentile errors are under 0.92 m. For real-time tracking, the errors are within 0.4 m for walks of 51 m.

Collision resolution is a crucial issue in wireless sensor networks. The existing approaches of collision resolution have drawbacks with respect to energy efficiency and processing latency. In this paper, we propose ST AIRS, a time and energy efficient collision resolution mechanism for wireless sensor networks. STAIRS incorporates the constructive interference technique in its design and explicitly forms superimposed colliding signals. Through extensive observations and theoretical analysis, we show that the RSSI of the superimposed signals exhibit stairs-like phenomenon with different number of contenders. That principle offers an attractive feature to efficiently distinguish multiple contenders and in turn makes collision-free schedules for channel access. In the design and implementation of STAIRS, we address practical challenges such as contenders alignment, online detection of RSSI change points, and fast channel assignment. The experiments on real testbed show that STARIS realizes fast and effective collision resolution, which significantly improves the network performance in terms of both latency and throughput.

Recently, carrying control signals on passing data packets has emerged as a promising direction for efficient control information transmission. With control messages carried on data payload, the extra air time needed for control packets like RTS/CTS is eliminated and thus channel utilization is improved. However, carrying control signals on the data payload of a packet requires the data packet to have a sufficiently large SNR, otherwise both the data packet and the control messages are lost. In this paper, we proposeHitchhike, a technique that utilizes the preamble field to carry control messages. Hitchhike completely decouples the control messages from the payload and therefore the superposition of (multiple) control messages has little adverse effect on the operation of the payload decoding. We implement and evaluate Hitchhike in the USRP2 platform with 5 nodes. Evaluation results demonstrate the feasibility and effectiveness of Hitchhike. Compared with the state-of-the-art, e.g., Side-channel in 802.15.4, Hitchhike improves the detection accuracy of control messages by 40% and reduces the data loss caused by control messages by 15%.

RFID technology has been widely adopted in a variety of applications from logistics to access control. Many applications gain benefits from knowing the exact position of an RFID-tagged object. Existing localization algorithms in wireless network, however, can hardly be directly employed due to tag's limited capabilities in terms of energy and memory. For example, the RSS based methods are vulnerable to both distance and tag orientation, while AOA based methods put a strict constraint on the antennas' spacing that reader's directional antennas are too large to meet. In this paper, we propose BackPos, a fine-grained backscatter positioning technique using the COTS RFID products with detected phase. Our study shows that the phase is indeed a stable indicator highly related to tag's position and preserved over frequency or tag orientation, but challenged by its periodicity and tag's diversity. We attempt to infer the distance differences from phases detected by antennas under triangle constraint. Further, hyperbolic positioning using the distance differences is employed to shrink the tag's candidate positions until filtering out the real one. In combination with interrogation zone, we finally relax the triangle constraint and allow arbitrary deployment of antennas by sacrificing the feasible region. We implement a prototype of BackPos with COTS RFID products and evaluate this design in various scenarios. The results show that BackPos achieves the mean accuracy of 12.8cm with variance of 3.8cm.

Batch recall is a practically important problem for most industry manufacturers. The batches of products which contain flawed parts need to be recalled by manufacturers in time to prevent further economic and health loss. Accurate batch recall could be a challenging issue as flawed parts may have already been integrated into a large number of products and distributed to customers. The recent development of Radio Frequency Identification (RFID) provides us a promising opportunity to implement batch recall in an accurate and efficient way. RFID-enabled batch recall provides us the opportunity to further enhance the security of batch recall operation, allowing us to achieve recognition of problematic products, privacy preserving of production pattern, recall authentication and non-repudiation, etc. In this paper, we thoroughly study the security aspects and identify the unique requirements in RFID-enabled batch recall. We propose a practically secure protocol, COLLECTOR, to enable accurate, secure and efficient RFID batch recall.

Neighbor discovery is crucial for both wireless sensor networks and mobile computing applications. The crux of the problem is to achieve energy efficiency, which has been demonstrated to be difficult by prior work. In this paper we propose Hello, a generic flexible protocol for neighbor discovery. With an unrestricted parameter, it serves as a generic framework that incorporates existing deterministic protocols. Under the framework, we expose optimal parameters for either symmetric or asymmetric duty cycles, which is the first to our knowledge. As a result, it exhibits great flexibility by adjusting itself to any configuration that best meets application demands. Besides, several techniques are applied to removing redundant discoveries without sacrificing the worst-case latency. We evaluate Hello through extensive simulation and real-world sensor experiments. The results show that Hello is highly energy-efficient under symmetric and asymmetric duty cycles. In particular, it is two times better than the state of the art in terms of the worst-case latency under asymmetric duty cycles.

Duty cycling mode is widely adopted in wireless sensor networks to save energy. Existing duty-cycling protocols cannot well adapt to different data rates and dynamics, resulting in a high energy consumption in real networks. Improving those protocols may require global information or heavy computation and thus may not be practical, leading to empirical parameters in real protocols. To fill the gap between the application requirement and protocol performance, we design a light-weight adaptive duty-cycling protocol (LAD), which reduces the energy consumption under different data rates and protocol dynamics. We theoretically validate the performance improvement of the protocol. We implement the protocol in TinyOS and extensively evaluate it on 40 TelosB nodes. The evaluation results show the energy consumption can be reduced by 28.2%~40.1% compared with state-of-the-art protocols. Results based on data from a 1200-node operational network further show the effectiveness and scalability of the design.

In this work we design and develop Montage for real-time multi-user formation tracking and localization by off-the-shelf smartphones. Montage achieves submeter-level tracking accuracy by integrating temporal and spatial constraints from user movement vector estimation and distance measuring. In Montage we designed a suite of novel techniques to surmount a variety of challenges in real-time tracking, without infrastructure and fingerprints, and without any a priori user-specific (e.g., stride-length and phone-placement) or site-specific (e.g., digitalized map) knowledge. We implemented, deployed and evaluated Montage in both outdoor and indoor environment. Our experimental results (847 traces from 15 users) show that the stride-length estimated by Montage over all users has error within 9cm, and the moving-direction estimated by Montage is within 20o. For realtime tracking, Montage provides meter-second-level formation tracking accuracy with off-the-shelf mobile phones.

Wireless LANs, especially WiFi, have been pervasively deployed and have fostered myriad wireless communication services and ubiquitous computing applications. A primary concern in designing each scenario-tailored application is to combat harsh indoor propagation environments, particularly Non-Line-Of-Sight (NLOS) propagation. The ability to distinguish Line-Of-Sight (LOS) path from NLOS paths acts as a key enabler for adaptive communication, cognitive radios, robust localization, etc. Enabling such capability on commodity WiFi infrastructure, however, is prohibitive due to the coarse multipath resolution with mere MAC layer RSSI. In this work, we dive into the PHY layer and strive to eliminate irrelevant noise and NLOS paths with long delays from the multipath channel responses. To further break away from the intrinsic bandwidth limit of WiFi, we extend to the spatial domain and harness natural mobility to magnify the randomness of NLOS paths while retaining the deterministic nature of the LOS component. We prototype LiFi, a statistical LOS identification scheme for commodity WiFi infrastructure and evaluate it in typical indoor environments covering an area of 1500 m2. Experimental results demonstrate an overall LOS identification rate of 90.4% with a false alarm rate of 9.3%.

In many applications, we have to identify an object and then locate the object to within high precision (centimeter- or millimeter-level). Tracking mobile RFID tags in real time has been a daunting task, especially challenging for achieving high precision. We achieve these three goals by leveraging the phase value of the backscattered signal, provided by the COTS RFID readers, to estimate the location of the object. To illustrate the basic idea of our system, we firstly focus on a simple scenario where the tag is moving along a fixed track known to the system. We propose Differential Augmented Hologram (DAH) which will facilitate the instant tracking of the mobile RFID tag to a high precision. We then devise a comprehensive solution to accurately recover the tag's moving trajectory and its locations, relaxing the assumption of knowing tag's track function in advance.

In many applications, we have to identify an object and then locate the object to within high precision (centimeter- or millimeter-level). Legacy systems that can provide such accuracy are either expensive or suffering from performance degradation resulting from various impacts, e.g., occlusion for computer vision based approaches.

In this work, we explore a new networking mechanism with smart glasses, through which users can express their interest and connect to a target simply by a gaze. Doing this, we attempt to let wearable devices understand human attention and intention, and pair devices carried by users according to such attention and intention. To achieve this ambitious goal, we propose a proof-of-concept system iGaze, a visual attention driven networking suite: an iGaze glass (hardware), and a networking protocol VAN (software). Our glass, iGaze glass, is a low-cost head-mounted glass with a camera, orientation sensors, microphone and speakers, which are embedded with our software for visual attention capture and networking. A visual attention driven networking protocol (VAN) is carefully designed and implemented. In VAN, we design an energy efficient and highly accurate visual attention determination scheme using single camera to capture user's communication interest and a double-matching scheme based on visual direction detection and Doppler effect of acoustic signal to lock the target devices. Using our system, we conduct a series of trials for various application scenarios to demonstrate the effectiveness of our system.

In this demo, we propose a proof-of-concept networking system for smart glasses, through which users can express their interest and connect to a target simply by a gaze. Our system iGaze is a visual attention driven networking suite: an iGaze glass (hardware) and a networking protocol VAN (software). Our glass is a low-cost head-mounted glass with a camera, orientation sensors, microphone and speakers, which are embedded with our software for visual attention capture and networking. A visual attention driven networking protocol (VAN) is carefully designed and implemented. In VAN, we design an energy efficient and highly accurate visual attention determination scheme using single camera to capture user's communication interest and a double-matching scheme based on visual direction detection and Doppler effect of acoustic signal to lock the target devices. iGaze has separated and modularized hardware and software design. It can run on top of existing networking protocols, e.g., Wi-Fi.

Radio Frequency Identification technology (RFID) is widely used in many applications, such as asset monitoring, e-passport and electronic payment, and is becoming one of the most effective solutions in cyber physical system. Since the identification alone does not provide any guarantee that tag corresponds to genuine identity, authentication of tag information is needed in most RFID systems. Meanwhile, as the number of tags is rapidly growing in recent years, per-tag based methods suffer from severely low efficiency and thus give way to probabilistic batch authentication. Most previous methods, however, share a common drawback from statistical perspective: they fail to explore correlation information, i.e., they do not comprehensively utilize all the information in authentication data structures. In addition, those schemes are not scalable well when multiple tag sets need to be verified simultaneously. In this paper, we propose a fast and efficient batch authentication scheme, Wise Counting (WIC), for large-scale RFID systems. We are the first to formally introduce the general batch authentication problem with multiple tag sets and give counterfeits estimation scheme with high efficiency. By employing a novel hierarchical authentication structure, we show that WIC is able to fast and efficiently authenticate both a single tag set and multiple tag sets in an easy, intuitive way. Through detailed theoretical analysis and extensive simulations, we validate the design of WIC and demonstrate its large superiority over state-of-the art approaches.

To save energy, wireless sensor networks often run in a low duty cycle mode, where the radios of sensor nodes are scheduled between ON and OFF states. For nodes to communicate with each other, Low Power Listening (LPL) and Low Power Probing (LPP) are two types of rendezvous mechanisms. Nodes with LPL or LPP rely on signal strength or probe packets to detect potential transmissions, and then keep the radio-on for communications. Unfortunately, in many cases, signal strength and probe packets are susceptible to interference, resulting in undesirable radio on time when the signal strength of interference is above a threshold or a probe packet is interfered. To address the issue, we propose ZiSense, an energy efficient rendezvous mechanism which is resilient to interference. Instead of checking the signal strength or decoding the probe packets, ZiSense detects the existence of ZigBee transmissions and wakes up nodes accordingly. On sensor nodes with limited information and resource, we carefully study and extract short-term features purely from the time-domain RSSI sequence, and design a rule-based approach to efficiently identify the existence of ZigBee. We theoretically analyze the benefit of ZiSense in different environments and implement a prototype in TinyOS with TelosB motes. We examine ZiSense performance under controlled interference and office environments. The evaluation results show that, compared with state-of-the-art rendezvous mechanisms, ZiSense significantly reduces the energy consumption.

The seven papers in this special section explores issues relating to network and computer security as the Internet is becoming more ubiquitous. One central element of this trend is the existence of a massive network of interconnected wired/wireless physical objects, things, sensors, and devices, which can interact in a rich set of manners through a worldwide communication and information infrastructure to provide value added services. These papers present the most recent advances in IoT security.

A critical function of wireless sensor networks (WSNs) is data gathering. One is often only interested in collecting a specific function of the sensor measurements at a sink node, rather than downloading all the raw data from all the sensors. In this paper, we study the capacity of computing and transporting the specific functions of sensor measurements to the sink node, called aggregation capacity, for WSNs. We focus on random WSNs that can be classified into two types: random extended WSN and random dense WSN. All existing results about aggregation capacity are studied for dense WSNs, including random cases and arbitrary cases, under the protocol model (ProM) or physical model (PhyM). In this paper, we propose the first aggregation capacity scaling laws for random extended WSNs. We point out that unlike random dense WSNs, for random extended WSNs, the assumption made in ProM and PhyM that each successful transmission can sustain a constant rate is over-optimistic and unpractical due to transmit power limitation. We derive the first result on aggregation capacity for random extended WSNs under the generalized physical model. Particularly, we prove that, for the type-sensitive divisible perfectly compressible functions and type-threshold divisible perfectly compressible functions, the aggregation capacities for random extended WSNs with${\mbi {n}}$nodes are of order$\Thetab ({{{({\bf log} {\mbi {n}})}^{ - {\alphab \over {\bf 2}} - {\bf 1}}}})$and$\Theta ({{{{{({\bf log} {\mbi {n}})}^{ - \alphab /{\bf 2}}}} \over {{\bf log}{\bf log} {\mbi {n}}}}})$, respectively, where$\alphab \gt {\bf 2}$denotes the power attenuation exponent in the generalized physical model. Furthermore, we improve the aggregation throughput for general divisible perfectly compressible functions to$\Omegab ({{{({\bf log} {\mbi {n}})}^{ - {\alphab \over {\bf 2}}}}})$by choosing$\Thetab ({\bf log}\; {\mbi {n}})$sensors from a small region (relative to the whole region) as sink nodes.

We present a new mechanism called Elon for enabling efficient and long-term reprogramming in wireless sensor networks. Elon reduces the transferred code size significantly by introducing the concept of replaceable component. It avoids the cost of hardware reboot with a novel software reboot mechanism. Moreover, it significantly prolongs the reprogrammable lifetime (i.e., the time period during which the sensor nodes can be reprogrammed) by avoiding flash writes for TelosB nodes. Experimental results show that Elon transfers up to 120--389 times less information than Deluge, and 18--42 times less information than Stream. The software reboot mechanism that Elon applies reduces the rebooting cost by 50.4%--53.87% in terms of beacon packets, and 56.83% in terms of unsynchronized nodes. In addition, Elon prolongs the reprogrammable lifetime by a factor of 3.3.

In this paper, we propose a novel clock calibration approach called FLIGHT, which leverages the fact that the fluorescent light intensity changes with a stable period that equals half of the alternating current's. By tuning to the light emitted from indoor fluorescent lamps, FLIGHT can intelligently extract the light period information and achieve network wide time calibration by referring to such a common time reference. The light period can be also viewed as an indoor context indicator. As sampling the light sensor consumes substantially less energy, FLIGHT provides us a lightweight clock calibration and time synchronization solution. In addition, FLIGHT suits various mobility-enabled scenarios and it can work well even when the network is temporarily disconnected. We address a series of practical challenges and implement FLIGHT in TelosB motes. We conduct comprehensive experiments using a 12-node test-bed in both static and mobile environments. Over one-week measurement suggests that compared with existing technologies, FLIGHT can achieve tightly synchronized time with low energy consumption. We further leverage the periodical pattern and upgrade FLIGHT to recognize the ambient indoor/outdoor context, based on which the on/off states of a variety of location-based services can be controlled automatically for mobile devices.

For cognitive wireless networks, one challenge is that the status and statistics of the channels' availability are difficult to predict. Numerous learning based online channel sensing and accessing strategies have been proposed to address such challenge. In this work, we propose a novel channel sensing and accessing strategy that carefully balances the channel statistics exploration and multichannel diversity exploitation. Unlike traditional MAB-based approaches, in our scheme, a secondary cognitive radio user will sequentially sense the status of multiple channels in a carefully designed order. We formulate the online sequential channel sensing and accessing problem as a sequencing multi-armed bandit problem, and propose a novel policy whose regret is in optimal logarithmic rate in time and polynomial in the number of channels. We conduct extensive simulations to compare the performance of our method with traditional MAB-based approach. Simulation results show that the proposed scheme improves the throughput by more than 30% and speeds up the learning process by more than 100%.

Understanding the packet delivery performance of a wireless sensor network (WSN) is critical for improving system performance and exploring future developments and applications of WSN techniques. In spite of many empirical measurements in the literature, we still lack in-depth understanding on how and to what extent different factors contribute to the overall packet losses for a complete stack of protocols at large scale. Specifically, very little is known about: 1) when, where, and under what kind of circumstances packet losses occur; 2) why packets are lost. As a step toward addressing those issues, we deploy a large-scale WSN and design a measurement system for retrieving important system metrics. We propose MAP, a step-by-step methodology to identify the losses, extract system events, and perform spatial-temporal correlation analysis by employing a carefully examined causal graph. MAP enables us to get a closer look at the root causes of packet losses in a low-power ad hoc network. This study validates some earlier conjectures on WSNs and reveals some new findings. The quantitative results also shed lights for future large-scale WSN deployments .

In this article, we investigate the problem of controlling node sleep intervals so as to achieve the min-max energy fairness in asynchronous duty-cycling sensor networks. We propose a mathematical model to describe the energy efficiency of such networks and observe that traditional sleep interval setting strategies, for example, operating sensor nodes with an identical sleep interval, or intuitive control heuristics, for example, greedily increasing sleep intervals of sensor nodes with high energy consumption rates, hardly perform well in practice. There is an urgent need to develop an efficient sleep interval control strategy for achieving fair and high energy efficiency. To this end, we theoretically formulate the Sleep Interval Control (SIC) problem and find out that it is a convex optimization problem. By utilizing the convex property, we decompose the original problem and propose a distributed algorithm, called GDSIC. In GDSIC, sensor nodes can tune sleep intervals through a local information exchange such that the maximum energy consumption rate of the network approaches to be minimized. The algorithm is self-adjustable to the traffic load variance and is able to serve as a unified framework for a variety of asynchronous duty-cycling MAC protocols. We implement our approach in a prototype system and test its feasibility and applicability on a 50-node testbed. We further conduct extensive trace-driven simulations to examine the efficiency and scalability of our algorithm with various settings.

Localization is an enabling technique for many sensor network applications. Real-world deployments demonstrate that, in practice, a network is not always entirely localizable, leaving a certain number of theoretically nonlocalizable nodes. Previous studies mainly focus on how to tune network settings to make a network localizable. However, the existing methods are considered to be coarse-grained, since they equally deal with localizable and nonlocalizable nodes. Ignoring localizability induces unnecessary adjustments and accompanying costs. In this study, we propose a fine-grained approach, localizability-aided localization (LAL), which basically consists of three phases: node localizability testing, structure analysis, and network adjustment. LAL triggers a single round adjustment, after which some popular localization methods can be successfully carried out. Being aware of node localizability, all network adjustments made by LAL are purposefully selected. Experiment and simulation results show that LAL effectively guides the adjustment while makes it efficient in terms of the number of added edges and affected nodes.

Wireless reprogramming is a crucial technique for software deployment in wireless sensor networks (WSNs). Code dissemination is a basic building block to enable wireless reprogramming. We present ECD, an Efficient Code Dissemination protocol leveraging 1-hop link quality information based on the TinyOS platform. Compared to prior works, ECD has three salient features. First, it supports dynamically configurable packet sizes. By increasing the packet size for high PHY rate radios, it significantly improves the transmission efficiency. Second, it employs an accurate sender selection algorithm to mitigate transmission collisions and transmissions over poor links. Third, it employs a simple impact-based backoff timer design to shorten the time spent in coordinating multiple eligible senders so that the largest impact sender is most likely to transmit. We implement ECD based on TinyOS and evaluate its performance extensively via testbed experiments and simulations. Results show that ECD outperforms state-of-the-art protocols, Deluge and MNP, in terms of completion time and data traffic (e.g., about 20 percent less traffic and 20-30 percent shorter completion time compared to Deluge).

Gradient-based routing using Bloom filters is an effective mechanism to enable data-centric queries in multihop networks. A node compressively describes its data items as a Bloom filter, which is then diffused away to the other nodes with information decay. The Bloom filters form an information potential that eventually navigates queries to the source node by ascending the potential field. The existing designs of Bloom filters, however, have critical limitations with respect to the feasibility of gradient-based routing. The compressed routing entries appear to be noisy. Noise in unrelated routing entries is very likely to equal to even outweigh information in right routing entries, thus blinding a query to its desired destination. This work addresses the root cause of the mismatch between the ideal and the practical performance of gradient-based routing using Bloom filters. We first investigate the impact of decaying model on the effectiveness of routing entries, and then evaluate the negative impact of noise on routing decisions. Based on such analytical results, we derive the necessary and sufficient condition of feasible gradient-based routing using Bloom filters. Accordingly, we propose a receiver-oriented design of Bloom filters, called Wader, which satisfies the necessary and sufficient condition. The evaluation results demonstrate that Wader guarantees the correctness and efficiency of gradient-based routing with high probability.

In many logistics applications of RFID technology, luggage attached with tags are placed on moving conveyor belts for processing. It is important to figure out the order of goods on the belts so that further actions like sorting can be accurately taken on proper goods. Due to arbitrary goods placement or the irregularity of wireless signal propagation, neither of the order of tag identification nor the received signal strength provides sufficient evidence on their relative positions on the belts. In this study, we observe, from experiments, a critical region of reading rate when a tag gets close enough to a reader. This phenomenon, as well as other signal attributes, yields the stable indication of tag order. We establish a probabilistic model for recognizing the transient critical region and propose the OTrack protocol to continuously monitor the order of tags. To validate the protocol, we evaluate the accuracy and effectiveness through a one-month experiment conducted through a working conveyor at Beijing Capital International Airport.

In this study, we explore the possibility of locating remote objects via cameras together with built-in inertial sensors of off-the-shelf smartphones. Our solution, CamLoc, enables a user taking two photos of an object using a smartphone at a fixed location and immediately knowing the location of the object in global coordinates, thus facilitating myriad location-based services. Such usage is user-friendly but error prone. We devise several techniques to mitigate the errors caused by cheap and noisy sensors, upgrading the positioning accuracy to an applicable level. We prototype CamLoc on Android OS, and evaluate its performance across different scenarios with various building densities. Experiment results show that our system achieves 89 percent and 72 percent physical location mapping accuracy in rural and downtown areas, respectively, which is competitive with existing solutions.

Due to its large scale and constrained communication radius, a wireless sensor network mostly relies on multi-hop transmissions to deliver a data packet along a sequence of nodes. It is of essential importance to measure the forwarding quality of multi-hop paths and such information shall be utilized in designing efficient routing strategies. Existing metrics like ETX, ETF mainly focus on quantifying the link performance in between the nodes while overlooking the forwarding capabilities inside the sensor nodes. The experience on manipulating GreenOrbs, a large-scale sensor network with 330 nodes, reveals that the quality of forwarding inside each sensor node is at the least an equally important factor that contributes to the path quality in data delivery. In this paper we propose QoF, Quality of Forwarding, a new metric which explores the performance in the gray zone inside a node left unattended in previous studies. By combining the QoF measurements within a node and over a link, we are able to comprehensively measure the intact path quality in designing efficient multi-hop routing protocols. We implement QoF and build a modified Collection Tree Protocol (CTP). We evaluate the data collection performance in a testbed consisting of 50 TelosB nodes, and compare it with the original CTP protocol. The experimental results show that our approach takes both transmission cost and forwarding reliability into consideration, thus achieving a high throughput for data collection.

Accurate localization is crucial for wireless ad-hoc and sensor networks. Among the localization schemes, component-based approaches specialize in localization performance. By grouping nodes into increasingly large rigid components, component-based localization algorithms can properly conquer network sparseness and anchor sparseness. However, such design is sensitive to measurement errors. Existing robust localization methods focus on eliminating the positioning error of a single node. Indeed, a single node has two dimensions of freedom in 2D space and only suffers from one type of transformation: translation. As a rigid 2D structure, a component suffers from three possible transformations: translation, rotation, and reflection. A high degree of freedom brings about complicated cases of error productions and difficulties on error controlling. This study is the first work addressing how to deal with ranging noises for component-based methods. By exploiting a set of robust patterns, we present an Error-TOlerant Component-based algorithm (ETOC) that not only inherits the high-performance characteristic of component-based methods, but also achieves robustness of the result. We evaluate ETOC through a real-world sensor network consisting of 120 TelosB motes as well as extensive large-scale simulations. Experiment results show that, comparing with the-state-of-the-art designs, ETOC can work properly in sparse networks and provide more accurate localization results.

Context-awareness is getting increasingly important for a range of mobile and pervasive applications on nowadays smartphones. Whereas human-centric contexts (e.g., indoor/ outdoor, at home/in office, driving/walking) have been extensively researched, few attempts have studied from phones' perspective (e.g., on table/sofa, in pocket/bag/hand). We refer to such immediate surroundings as micro-environment, usually several to a dozen of centimeters, around a phone. In this study, we design and implement Sherlock, a micro-environment sensing platform that automatically records sensor hints and characterizes the micro-environment of smartphones. The platform runs as a daemon process on a smartphone and provides finer-grained environment information to upper layer applications via programming interfaces. Sherlock is a unified framework covering the major cases of phone usage, placement, attitude, and interaction in practical uses with complicated user habits. As a long-term running middleware, Sherlock considers both energy consumption and user friendship. We prototype Sherlock on Android OS and systematically evaluate its performance with data collected on fifteen scenarios during three weeks. The preliminary results show that Sherlock achieves low energy cost, rapid system deployment, and competitive sensing accuracy.

Crowdsourcing-based mobile applications are becoming more and more prevalent in recent years, as smartphones equipped with various built-in sensors are proliferating rapidly. The large quantity of crowdsourced sensing data stimulates researchers to accomplish some tasks that used to be costly or impossible, yet the quality of the crowdsourced data, which is of great importance, has not received sufficient attention. In reality, the low-quality crowdsourced data are prone to containing outliers that may severely impair the crowdsourcing applications. Thus in this work, we conduct pioneer investigation considering crowdsourced data quality. Specifically, we focus on estimating user motion trajectory information, which plays an essential role in multiple crowdsourcing applications, such as indoor localization, context recognition, indoor navigation, etc. We resort to the family of robust statistics and design a robust trajectory estimation scheme, name TrMCD, which is capable of alleviating the negative influence of abnormal crowdsourced user trajectories, differentiating normal users from abnormal users, and overcoming the challenge brought by spatial unbalance of crowdsourced trajectories. Two real field experiments are conducted and the results show that TrMCD is robust and effective in estimating user motion trajectories and mapping fingerprints to physical locations.

Device-free Passive (DfP) human detection acts as a key enabler for emerging location-based services such as smart space, human-computer interaction, and asset security. A primary concern in devising scenario-tailored detecting systems is coverage of their monitoring units. While disk-like coverage facilitates topology control, simplifies deployment analysis, and is crucial for proximity-based applications, conventional monitoring units demonstrate directional coverage due to the underlying transmitter-receiver link architecture. To achieve omnidirectional coverage under such link-centric architecture, we propose the concept of omnidirectional passive human detection. The rationale is to exploit the rich multipath effect to blur the directional coverage. We harness PHY layer features to robustly capture the fine-grained multipath characteristics and virtually tune the shape of the coverage of the monitoring unit, which is previously prohibited with mere MAC layer RSSI. We design a fingerprinting scheme and a threshold-based scheme with off-the-shelf WiFi infrastructure and evaluate both schemes in typical clustered indoor scenarios. Experimental results demonstrate an average false positive of 8 percent and an average false negative of 7 percent for fingerprinting in detecting human presence in 4 directions. And both average false positive and false negative remain around 10 percent even with threshold-based methods.

Previous packet length optimizations for sensor networks often employ a fixed optimal length scheme, while in this study we present DPLC, a Dynamic Packet Length Control scheme. To make DPLC more efficient in terms of channel utilization, we incorporate a lightweight and accurate link estimation method. We further provide two easy-to-use services, i.e., small message aggregation and large message fragmentation, to facilitate upper-layer application programming. The implementation of DPLC based on TinyOS 2.1 is lightweight, with respect to computation, memory, and header overhead. Our experiments using a real indoor testbed running CTP show that DPLC achieves the best performance compared with previous works.

Existing approaches to diagnosing sensor networks are generally sink based, which rely on actively pulling state information from sensor nodes so as to conduct centralized analysis. First, sink-based tools incur huge communication overhead to the traffic-sensitive sensor networks. Second, due to the unreliable wireless communications, sink often obtains incomplete and suspicious information, leading to inaccurate judgments. Even worse, it is always more difficult to obtain state information from problematic or critical regions. To address the given issues, we present a novel self-diagnosis approach, which encourages each single sensor to join the fault decision process. We design a series of fault detectors through which multiple nodes can cooperate with each other in a diagnosis task. Fault detectors encode the diagnosis process to state transitions. Each sensor can participate in the diagnosis by transiting the detector's current state to a new state based on local evidences and then passing the detector to other nodes. Having sufficient evidences, the fault detector achieves the Accept state and outputs a final diagnosis report. We examine the performance of our self-diagnosis tool called TinyD2 on a 100-node indoor testbed and conduct field studies in the GreenOrbs system, which is an operational sensor network with 330 nodes outdoor.

Indoor localization has enabled a great number of mobile and pervasive applications, attracting attentions from researchers worldwide. Most of current solutions rely on Received Signal Strength (RSS) of wireless signals as location fingerprint, to discriminate locations of interest. Fingerprint uniqueness with respect to locations is a basic requirement in these fingerprinting-based solutions. However, due to insufficient number of signal sources, temporal variations of wireless signals, and rich multipath effects, such requirement is not always met in complex indoor environments, which we refer to as fingerprint ambiguity. In this work, we explore the potential of leveraging user motion against fingerprint ambiguity. Our basic idea is that user motion patterns collected by built-in sensors of mobile phones add to the diversity built by RSS fingerprints. On this basis, we propose MoLoc, a motion-assisted localization scheme implemented on mobile phones. MoLoc can easily be integrated in existing localization systems by simply adding a motion database that is constructed automatically by crowdsourcing. We conducted experiments in a large office hall. The experiment results show that MoLoc doubles the localization accuracy achieved by the fingerprinting method, and limits the mean localization error to less than 1m.

Many proximity-based mobile social networks are developed to facilitate connections between any two people, or to help a user to find people with a matched profile within a certain distance. A challenging task in these applications is to protect the privacy of the participants' profiles and personal interests. In this paper, we design novel mechanisms, when given a preference-profile submitted by a user, that search persons with matching-profile in decentralized multi-hop mobile social networks. Our mechanisms also establish a secure communication channel between the initiator and matching users at the time when the matching user is found. Our rigorous analysis shows that our mechanism is privacy-preserving (no participants' profile and the submitted preference-profile are exposed), verifiable (both the initiator and the unmatched user cannot cheat each other to pretend to be matched), and efficient in both communication and computation. Extensive evaluations using real social network data, and actual system implementation on smart phones show that our mechanisms are significantly more efficient than existing solutions.

Packet corruption caused by collision is a critical problem that hurts the performance of wireless networks. Conventional medium access control (MAC) protocols resort to collision avoidance to maintain acceptable efficiency of channel utilization. According to our investigation and observation, however, collision avoidance comes at the cost of miscellaneous overhead, which oppositely hurts channel utilization, not to mention the poor resiliency and performance of those protocols in face of dense networks or intensive traffic. Discovering the ability to tolerate collisions at the physical layer implementations of wireless networks, we in this paper propose Coco, a MAC protocol that advocates simultaneous accesses from multiple senders to a shared channel, i.e., optimistically allowing collisions instead of simply avoiding them. With a simple but effective design, Coco addresses the key challenges in achieving collision tolerance, such as precise sender alignment and fine control of the transmission concurrency. We implement Coco in 802.15.4 networks and evaluate its performance through extensive experiments with 21 TelosB nodes. The results demonstrate that Coco is light-weight and enhances channel utilization by at least 20% in general cases, compared with state-of-the-arts protocols.

Opportunistic routing, offering relatively efficient and adaptive forwarding in low-duty-cycled sensor networks, generally allows multiple nodes to forward the same packet simultaneously, especially in networks with intensive traffic. Uncoordinated transmissions often incur a number of duplicate packets, which are further forwarded in the network, occupy the limited network resource, and hinder the packet delivery performance. Existing solutions to this issue, e.g. overhearing or coordination based approaches, either cannot scale up with the system size, or suffers high control overhead. We present Duplicate-Detectable Opportunistic Forwarding (DOF), a duplicate free opportunistic forwarding protocol for low-duty-cycled wireless sensor networks. DOF enables senders to obtain the information of all potential forwarders via a slotted acknowledgement scheme, so the data packets can be sent to the deterministic next-hop forwarder. Based on light-weight coordination, DOF explores the opportunities as many as possible and removes duplicate packets from the forwarding process. We implement DOF and evaluate its performance on an indoor test-bed with 20 TelosB nodes. The experimental results show that DOF reduces the average duplicate ratio by 90%, compared to state-of-the-art opportunistic protocols, and achieves 61.5% enhancement in network yield and 51.4% saving in energy consumption.

Many wireless applications urgently demand an efficient neighbor discovery protocol to build up a bridge connecting users to service providers or to other users. However, due to intrinsic constraints of wireless devices, e.g., limited energy and error of clock synchronization, there is still absence of effective and efficient neighbor discovery protocols in the literature. In this work, we propose neighbor discovery protocols for the following two problems. We first study Asynchronous Symmetry Neighbor Discovery problem, in which potential neighbor devices with asynchronous time clock but the same duty cycle aim to find each other. We further propose an efficient protocol (using Bouncing strategy) named Blind Date with guaranteed worst-case performance 9/10(1 + δ)2x2where δ is a small fraction of the length of time slot unit and 1/x is the duty cycle. Next, we extend this design to address Asynchronous Asymmetry Neighbor Discovery problem, in which both time clock and the duty cycles of potential neighbors are considered to be heterogeneous. We conduct extensive simulations to examine the feasibility and efficiency of the proposed protocols. Results show that Blind Date protocol outperforms existing approaches in average-case. We conclude that, compared with known protocols, Blind Date achieves a better worst-case discovery latency bound (e.g., 10% performance gain comparing with Searchlight [1]).

Understanding the packet delivery performance of a wireless sensor network (WSN) is critical for improving system performance and exploring further development and applications of WSN techniques. In spite of many empirical measurements in the literature, we still lack in-depth understanding on how and to what extent different factors contribute to the overall packet losses with respect to a complete stack of protocols at large scales. Specifically, very little is known about (1) When, where, and under what kind of circumstances packet losses occur. (2) Why packets are lost. As a step towards addressing those issues, we deploy a large-scale WSN and design a measurement system for retrieving important system metrics. We propose MAP, a step-by-step methodology to identify the losses, extract system events, and perform spatial-temporal correlation analysis by employing a carefully examined casual graph. MAP enables us to get a closer look at the root causes of packet losses in a low-power adhoc network. This study validates some earlier conjectures on WSNs and reveals some new findings. The quantitative results also shed lights for future large-scale WSN deployments.

We present a holistic reprogramming system called R3. R3 has two salient features. First, the binary differencing algorithm within R3 (R3diff) ensures an optimal result in terms of the delta size under a configurable cost measure. Second, the similarity preserving method within R3 (R3sim) optimizes the binary code format for achieving a large similarity with a small metadata overhead. Overall, R3 achieves the smallest delta size compared to other incremental approaches such as Rsync [11], RMTD [9], Zephyr/Hermes [17], [18], and R2 [2], e.g., 50%–99% reduction compared to Stream and about 20%–40% reduction compared to R2. R3's implementation on TelosB/TinyOS is lightweight and efficient. We release our code at http://code.google.com/p/r3-dongw.

Screen locking/unlocking is important for modern smart phones to avoid the unintentional operations and secure the personal stuff. Once the phone is locked, the user should take a specific action or provide some secret information to unlock the phone. Existing approaches do not support smart phones well due to the deficiency of security, high cost, and poor usability. We collect 200 users' handshaking actions with their smart phones and discover an appealing observation: the shaking pattern of a person is kind of unique, stable and distinguishable. In this paper, we propose OpenSesame, which employs the users' shaking patterns for locking/unlocking. The key feature of our system lies in using four fine-grained and statistic features of handshaking to verify users. Moreover, we utilize support vector machine (SVM) for accurate classification. Results from comprehensive experiments show that our technique is robust compatible across different brands of smart phones, without the need of any specialized hardware.

In large-scale wireless sensor networks, it proves very difficult to dynamically monitor system degradation and detect bad links. Faulty link detection plays a critical role in network diagnosis. Indeed, a destructive node impacts its links' performances including transmitting and receiving. Similarly, other potential network bottlenecks such as network partition and routing errors can be detected by link scan. Since sequentially checking all potential links incurs high transmission and storage cost, existing approaches often focus on links currently in use, while overlook those unused yet ones, thus fail to offer more insights to guide following operations. We propose a novel scheme Link Scanner (LS) for monitoring wireless links at real time. LS issues one probe message in the network and collects hop counts of the received probe messages at sensor nodes. Based on the observation that faulty links can result in mismatch between the received hop counts and the network topology, we are able to deduce all links' status with a probabilistic model. We evaluate our scheme by carrying out experiments on a testbed with 60 TelosB motes and conducting extensive simulation tests. A real outdoor system is also deployed to verify that LS can be reliably applied to surveillance networks.

In many logistics applications of RFID technology, goods attached with tags are placed on moving conveyor belts for processing. It is important to figure out the order of goods on the belts so that further actions like sorting can be accurately taken on proper goods. Due to arbitrary goods placement or the irregularity of wireless signal propagation, neither of the order of tag identification nor the received signal strength provides sufficient evidence on their relative positions on the belts. In this study, we observe, from experiments, a critical region of reading rate when a tag gets close enough to a reader. This phenomenon, as well as other signal attributes, yields the stable indication of tag order. We establish a probabilistic model for recognizing the transient critical region and propose the OTrack protocol to continuously monitor the order of tags. To validate the protocol, we evaluate the accuracy and effectiveness through a one-month experiment conducted through a working conveyor at Beijing Capital International Airport.

Constructive Interference (CI) proposed in the existing work (e.g., A-MAC [1], Glossy [2]) may degrade the packet reception performance in terms of Packet Reception Ratio (PRR) and Received Signal Strength Indication (RSSI). The packet reception performance of a set of nodes transmitting simultaneously might be no better than that of any single node transmitting individually. In this paper, we redefine CI and propose TriggerCast, a practical wireless architecture which ensures concurrent transmissions of an identical packet to interfere constructively rather than to interfere non-destructively. CI potentially allows orders of magnitude reductions in energy consumption and improvements in link quality. Moreover, we for the first time present a theoretical sufficient condition for generating CI with IEEE 802.15.4 radio: concurrent transmissions with an identical packet should be synchronized at chip level. Meanwhile, co-senders participating in concurrent transmissions should be carefully selected, and the starting instants for the concurrent transmissions should be aligned. Based on the sufficient condition, we propose practical techniques to effectively compensate propagation and radio processing delays. TriggerCast has 95thpercentile synchronization errors of at most 250ns. Extensive experiments in practical testbeds reveal that TriggerCast significantly improves PRR (from 5% to 70% with 7 concurrent senders, from 50% to 98.3% with 6 senders) and RSSI (about 6dB with 5 senders).

Global Positioning System (GPS) has enabled a number of geographical applications over many years. Quite a lot of location-based services, however, still suffer from considerable positioning errors of GPS (usually 1m to 20m in practice). In this study, we design and implement a high-accuracy global positioning solution based on GPS and human mobility captured by mobile phones. Our key observation is that smart phone-enabled dead reckoning supports accurate but local coordinates of users' trajectories, while GPS provides global but inconsistent coordinates. Considering them simultaneously, we devise techniques to refine the global positioning results by fitting the global positions to the structure of locally measured ones, so the refined positioning results are more likely to elicit the ground truth. We develop a prototype system, named GloCal, and conduct comprehensive experiments in both crowded urban and spacious suburban areas. The evaluation results show that GloCal can achieve 30% improvement on average error with respect to GPS.

Though there exist ready-made DSRC/WiFi/3G/4G cellular systems for roadway communications, there are common defects in these systems for roadway safety oriented applications and the corresponding challenges remain unsolved for years, i.e., WiFi cannot work well in vehicular networks due to the high probability of packet loss caused by burst communications, which is a common phenomenon in roadway networks; 3G/4G cannot well support real-time communications due to the nature of their designs; DSRC lacks the support to roadway safety oriented applications with hard realtime and reliability requirements [1]. To solve the conflict between the capability limitations of existing systems and the ever-growing demands of roadway safety oriented communication applications, we propose a novel system design and implementation for realtime reliable roadway communications, aiming at providing safety messages to users in a realtime and reliable manner. In our extensive experimental study, the latency is well controlled within the hard realtime requirement (100ms) for roadway safety applications given by NHTSA [2], and the reliability is proved to be improved by two orders of magnitude compared with existing experimental results [1]. Our experiments show that the proposed system for roadway safety communications can provide guaranteed highly reliable packet delivery ratio (PDR) of 99% within the hard realtime requirement 100ms under various scenarios, e.g., highways, city areas, rural areas, tunnels, bridges. Our design can be widely applied for roadway communications and facilitate the current research in both hardware and software design and further provide an opportunity to consolidate the existing work on a practical and easy-configurable low-cost roadway communication platform.

The existing work on distributed secure multi-party computation, e.g., set operations, dot product, ranking, focus on the privacy protection aspects, while the verifiability of user inputs and outcomes are neglected. Most of the existing works assume that the involved parties will follow the protocol honestly. In practice, a malicious adversary can easily forge his/her input values to achieve incorrect outcomes or simply lie about the computation results to cheat other parities. In this work, we focus on the problem of verifiable privacy preserving multiparty computation. We thoroughly analyze the attacks on existing privacy preserving multi-party computation approaches and design a series of protocols for dot product, ranging and ranking, which are proved to be privacy preserving and verifiable. We implement our protocols on laptops and mobile phones. The results show that our verifiable private computation protocols are efficient both in computation and communication.

Passive human detection and localization serve as key enablers for various pervasive applications such as smart space, human-computer interaction and asset security. The primary concern in devising scenario-tailored detecting systems is the coverage of their monitoring units. In conventional radio-based schemes, the basic unit tends to demonstrate a directional coverage, even if the underlying devices are all equipped with omnidirectional antennas. Such an inconsistency stems from the link-centric architecture, creating an anisotropic wireless propagating environment. To achieve an omnidirectional coverage while retaining the link-centric architecture, we propose the concept of Omnidirectional Passive Human Detection, and investigate to harness the PHY layer features to virtually tune the shape of the unit coverage by fingerprinting approaches, which is previously prohibited with mere MAC layer RSSI. We design the scheme with ubiquitously deployed WiFi infrastructure and evaluate it in typical multipath-rich indoor scenarios. Experimental results show that our scheme achieves an average false positive of 8% and an average false negative of 7% in detecting human presence in 4 directions.

Leveraging a wireless multi-path channel as a source of common randomness, a number of key generation methods have been proposed according to information-theory security. However, by taking the advantages of node's mobility, existing schemes usually have low generation rate or low entropy. To overcome this limitation, we present a key generation protocol with known Artificial Interference, named Smoke Grenade, a new physical-layer approach for secret key generations in a narrowband fading channel. Our scheme utilizes artificial interference to contribute to the change of the measured values on channel states. The theoretical analysis shows that the key generation rate rises with the increment of the interference power. Particularly, the achievable key rate of Smoke Grenade achieves at least four times better than that of traditional key generation schemes when the average interference power is normalized to 1. Simulation results also show that Smoke Grenade has a higher generation rate and entropy compared with some known state-of-the-art approaches.

The deployment of large scale Wireless Sensor Networks generally needs network management and measurement solutions. End-to-end delay is one of the most important metrics in assessing the network performance. Many efforts have been devoted to measuring the end-to-end delay efficiently and precisely. Unfortunately, existing approaches often require sensor nodes to be tightly time synchronized which is costly in resource limited sensor network. We propose a novel scheme that can measure the end-to-end delay for each packet to the granularity of tens of microseconds without clock synchronization. Through extensive experiments on our testbed, we examine the effectiveness of our approach. The results show that our scheme achieves high performance with low overhead. We also present observations about the network states by tracking and analyzing the end-to-end delay data.

Channel utilization for wireless sensor networks is far from efficient, especially for convergecast in which multiple nodes are sending packets to a receiver. In this paper, we analyze the channel utilization when multiple nodes contend for the channel in convergecast and show that channel utilization can be improved by accumulating packets on each node. However, the number of accumulated packets should be carefully determined. Otherwise, the system performance may not be improved or even be degraded, e.g., incurring additional packet delay. Based on the analysis result, we present STAGGER to achieve channel utilization improvement while guarantee the worst case performance. We implement STAGGER in TinyOS 2.1 and evaluate its performance on TelosB nodes. STAGGER only uses local information to determine the number of accumulated packets without incurring additional overhead. It adopts CSMA at the low level and preserves its nice properties, e.g., fairness. The experimental results show that the design can significantly improve the per-hop throughput and reduce packet loss ratio under high traffic rate.

Data dissemination is a building block of wireless sensor networks (WSNs). In order to guarantee the reliability, many existing works rely on a negotiation scheme, making senders and receivers negotiate the schedule of transmissions through a three-way handshake procedure. According to our observation, however, negotiation incurs long dissemination time and seriously defers the network wide convergence. On the other hand, the flooding approach, which is conventionally considered to be inefficient and energy-consuming, may facilitate data dissemination if appropriately designed. This motivates us to pursue a delicate tradeoff between negotiation and flooding in the data dissemination process. In this paper, we propose SurF (Survival of the Fittest), a data dissemination protocol which selectively adopts negotiation and leverages flooding opportunistically. How to capture and utilize the opportunities when negotiation should be used is a challenging issue. SurF incorporates a time-reliability model to estimate the time efficiencies of the two schemes (flooding vs. negotiation) and dynamically selects the fittest one to facilitate the dissemination process. We implement SurF based on TinyOS 2.1.1 and evaluate its performance with 40 TelosB nodes. The results show that SurF, while retaining the dissemination reliability, reduces the dissemination time by 40% in average, compared with the state-of-the-art protocols.

Routing dynamics are intrinsic characteristics of operational wireless sensor networks (WSNs). We present the measurement and analysis results for routing dynamics in a large-scale WSN. We seek to answer several fundamental questions: How dynamically are current routing protocols performing? What causes routing dynamics? What is the impact of routing dynamics? Answers to the above questions are critical to understanding the interactions among multiple network elements, evaluating protocol design strategies, and improving system performances. However, measurements in large-scale WSNs are challenging due to the lack of dedicated log information (be analogous to configuration files, syslog messages used in Internet). We propose an approach to identify the routing dynamics based on limited information and correlate them with system events to find out the root causes. The key findings of our study include: parent change events mainly affect local nodes, i.e. They do not cause routing instability on far-away nodes, environment dynamics and routing loops have large impact on routing, small portion of parent changes might not be necessary, while a large portion of parent changes are effective in improving network performance.

Many algorithms have been introduced to deterministically authenticate Radio Frequency Identification (RFID) tags, while little work has been done to address the scalability issue in batch authentications. Deterministic approaches verify them one by one, and the communication overhead and time cost grow linearly with increasing size of tags. We design a fine-grained batch authentication scheme, INformative Counting (INC), which achieves sublinear authentication time and communication cost in batch verifications. INC also provides authentication results with accurate estimates of the number of counterfeiting tags and genuine tags, while previous batch authentication methods merely provide 0/1 results indicating the existence of counterfeits. We conduct detailed theoretical analysis and extensive experiments to examine this design and the results show that INC significantly outperforms previous work in terms of effectiveness and efficiency.

Detecting and diagnosing anomalies in networked embedded systems like sensor networks is a very difficult task, due to the variable workloads and severe resource constraints. We notice that most node-level debugging tools can provide detailed program information inside the node but fail to detect when and where a problem occurs in the network. On the other hand, most network-level diagnosis tools can effectively detect a problem from the network but fail to narrow down the problem within the node because they lack detailed program information. To close the gap, we propose D2, a new anomaly detection and diagnosis method by combining program profiling and symptom mining. D2 employs binary instrumentation to perform lightweight function count profiling. Based on the statistics, D2 uses PCA (Principal Component Analysis) based approach for automatically detecting network anomalies. Compared to previous methods, D2 is able to point programmers closer to the most likely causes by a novel approach combining statistical tests and program call graph analysis. We implement our method based on TinyOS 2.1.1 and evaluate its effectiveness by case studies in the development of a working sensor network. Results show that our method is effective for detecting and diagnosing problems in real-world sensor network systems, and at the same time, incurs an acceptable overhead.

The networked application environment has motivated the development of multitasking operating systems for sensor networks and other low-power electronic devices, but their multitasking capability is severely limited because traditional stack management techniques perform poorly on small-memory systems without virtual memory support. In this paper, we show that combining binary translation and a new kernel runtime can lead to efficient OS designs on resource constrained platforms. We introduce SenSmart, a multitasking OS for sensor networks, and present new OS design techniques for supporting preemptive multitask scheduling, memory isolation, and adaptive stack management. Our solution provides memory isolation and automatic stack relocation on usual sensornet platforms. The adaptive stack management frees programmers from the burden of estimating tasks' stack usage, yet it enables SenSmart to schedule and run more tasks than other multitasking OSes for sensor networks. We have implemented SenSmart on MICA2/MICAz motes. Evaluation shows that SenSmart has a significantly better capability in managing concurrent tasks than other sensornet operating systems.

We present R2, an incremental reprogramming approach using relocatable code, to improve program similarity for efficient incremental reprogramming in networked embedded systems. R2 achieves a higher degree of similarity than existing approaches by mitigating effects of both function shifts and data shifts. R2 adopts a content-aware differencing algorithm to generate small delta files for efficient dissemination. Besides, it makes efficient use of memory and does not degrade program quality. We implement R2 based on TinyOS 2.1 and demonstrate its advantages through detailed analysis of TinyOS examples. We also present case studies on the software programs of a large-scale sensor system GreenOrbs. Results show that R2 reduces the dissemination cost by approximately 65 percent compared to state-of-the-art network reprogramming approach Deluge, and reduces the dissemination cost by approximately 20 percent compared to Zephyr and Hermes the latest works on incremental reprogramming.

A fundamental goal of data center networking is to efficiently interconnect a large number of servers with the low equipment cost. Several server-centric network structures for data centers have been proposed. They, however, are not truly expandable and suffer a low degree of regularity and symmetry. Inspired by the commodity servers in today's data centers that come with dual port, we consider how to build expandable and cost-effective structures without expensive high-end switches and additional hardware on servers except the two NIC ports. In this paper, two such network structures, called HCN and BCN, are designed, both of which are of server degree 2. We also develop the low overhead and robust routing mechanisms for HCN and BCN. Although the server degree is only 2, HCN can be expanded very easily to encompass hundreds of thousands servers with the low diameter and high bisection width. Additionally, HCN offers a high degree of regularity, scalability, and symmetry, which conform to the modular designs of data centers. BCN is the largest known network structure for data centers with the server degree 2 and network diameter 7. Furthermore, BCN has many attractive features, including the low diameter, high bisection width, large number of node-disjoint paths for the one-to-one traffic, and good fault-tolerant ability. Mathematical analysis and comprehensive simulations show that HCN and BCN possess excellent topological properties and are viable network structures for data centers.

Extracting planar graph from network topologies is of great importance for efficient protocol design in wireless ad hoc and sensor networks. Previous techniques of planar topology extraction are often based on ideal assumptions, such as UDG communication model and accurate node location measurements. To make these protocols work effectively in practice, we need extract a planar topology in a location-free and distributed manner with small stretch factors. The planar topologies constructed by current location-free methods often have large stretch factors. In this paper, we present a fine-grained and location-free network planarization method under ρ-quasi-UDG communication model with ρ≥1/√2. Compared with existing location-free planarization approaches, our method can extract a provably connected planar graph, called topological planar simplification (TPS), from the connectivity graph in a fine-grained manner using local connectivity information. We evaluate our design through extensive simulations and compare with the state-of-the-art approaches. The simulation results show that our method produces high-quality planar graphs with a small stretch factor in practical large-scale networks.

Recent advances in the application field increasingly demand the use of wireless camera sensor networks (WCSNs), for which localization is a crucial task to enable various location-based services. Most of the existing localization approaches for WCSNs are essentially interactive, i.e., require the interaction among the nodes throughout the localization process. As a result, they are costly to realize in practice, vulnerable to sniffer attacks, inefficient in energy consumption and computation. In this paper, we propose LISTEN, a noninteractive localization approach. Using LISTEN, every camera sensor node only needs to silently listen to the beacon signals from a mobile beacon node and capture a few images until determining its own location. We design the movement trajectory of the mobile beacon node, which guarantees to locate all the nodes successfully. We have implemented LISTEN and evaluated it through extensive experiments. Both the analytical and experimental results demonstrate that it is accurate, cost-efficient, and especially suitable for WCSNs that consist of low-end camera sensors.

Constructive interference-based flooding (CIBF) is a latency-optimal flooding protocol, which can realize millisecond network flooding latency and submicrosecond time synchronization accuracy, require no network state information, and be adapted to topology changes. However, constructive interference (CI) has a precondition to function, i.e., the maximum temporal displacement Δ of concurrent packet transmissions should be less than a given hardware constrained threshold (e.g., 0.5 μs, for the IEEE 802.15.4 radio). In this paper, we derive the closed-form packet reception ratio (PRR) formula for CIBF and theoretically disclose that CIBF suffers the scalability problem. The packet reception performance of intermediate nodes degrades significantly as the density or the size of the network increases. We analytically show that CIBF has a PRR lower bound (94.5%) in the grid topology. Based on this observation, we propose the spine constructive interference-based flooding (SCIF) protocol for an arbitrary uniformly distributed topology. Extensive simulations show that SCIF floods the entire network much more reliably than the state-of- the-art Glossy protocol does in high-density or large-scale networks. We further explain the root cause of CI with waveform analysis, which is mainly examined in simulations and experiments.

Localization is a fundamental issue of wireless sensor networks that has been extensively studied in the literature. Our real-world experience from GreenOrbs, a sensor network system deployed in a forest, shows that localization in the wild remains very challenging due to various interfering factors. In this paper, we propose CDL, a Combined and Differentiated Localization approach for localization that exploits the strength of range-free approaches and range-based approaches using received signal strength indicator (RSSI). A critical observation is that ranging quality greatly impacts the overall localization accuracy. To achieve a better ranging quality, our method CDL incorporates virtual-hop localization, local filtration, and ranging-quality aware calibration. We have implemented and evaluated CDL by extensive real-world experiments in GreenOrbs and large-scale simulations. Our experimental and simulation results demonstrate that CDL outperforms current state-of-art localization approaches with a more accurate and consistent performance. For example, the average location error using CDL in GreenOrbs system is 2.9 m, while the previous best method SISR has an average error of 4.6 m.

Knowing accurate positions of nodes in wireless ad hoc and sensor networks is essential for a wide range of pervasive and mobile applications. However, errors are inevitable in distance measurements and we observe that a small number of outliers can degrade localization accuracy drastically. To deal with noisy and outlier ranging results, triangle inequality, is often employed in existing approaches. Our study shows that triangle inequality has many limitations, which make it far from accurate and reliable. In this study, we formally define the outlier detection problem for network localization and build a theoretical foundation to identify outliers based on graph embeddability and rigidity theory. Our analysis shows that the redundancy of distance measurements plays an important role. We then design a bilateration generic cycles-based outlier detection algorithm, and examine its effectiveness and efficiency through a network prototype implementation of MicaZ motes as well as extensive simulations. The results show that our design significantly improves the localization accuracy by wisely rejecting outliers.

Multiple-Input-Multiple-Output (MIMO) has great potential for enhancing the throughput of multihop wireless networks via spatial multiplexing or spatial reuse. Spatial reuse with Stream Control (SC) provides a considerable improvement of the network throughput over spatial multiplexing. The gain of spatial reuse, however, is still not fully exploited. There exist large numbers of additional data streams, which could be transmitted concurrently with those data streams scheduled by stream control at certain time slots and vicinities. In this paper, we address the issue of MIMO link scheduling to maximize the gain of spatial reuse and thus network throughput. We propose a Receiver-Oriented Interference Suppression model (ROIS), based on which we design both centralized and distributed link scheduling algorithms to fully exploit the gain of spatial reuse in multihop MIMO networks. Further, we address the traffic-aware link scheduling problem by injecting nonuniform traffic load into the network. Through theoretical analysis and comprehensive performance evaluation, we achieve the following results: 1) link scheduling based on ROIS achieves significant higher network throughput than that based on stream control, with any interference range, number of antennas, and average hop length of data flows. 2) The traffic-aware scheduling is enticingly complementary to the link scheduling based on ROIS model. Accordingly, the two scheduling schemes can be combined to further enhance the network throughput.

Content distribution on today's Internet operates primarily in two modes: server-based and peer-to-peer (P2P). To leverage the advantages of both modes while circumventing their key limitations, a third mode: peer-to-server/peer (P2SP) has emerged in recent years. Although P2SP can provide efficient hybrid server-P2P content distribution, P2SP generally works in a closed manner by only utilizing its private owned servers to accelerate its private organized peer swarms. Consequently, P2SP still has its limitations in both content abundance and server bandwidth. To this end, the fourth mode (or says a generalized mode of P2SP) has appeared as "open-P2SP" that integrates various third-party servers, contents, and data transfer protocols all over the Internet into a large, open, and federated P2SP platform. In this paper, based on a large-scale commercial open-P2SP system named "QQXuanfeng" , we investigate the key challenging problems, practical designs and real-world performances of open-P2SP. Such "white-box" study of open-P2SP provides solid experiences and helpful heuristics to the designers of similar systems.

We study the ubiquitous data collection for mobile users in wireless sensor networks. People with handheld devices can easily interact with the network and collect data. We propose a novel approach for mobile users to collect the network-wide data. The routing structure of data collection is additively updated with the movement of the mobile user. With this approach, we only perform a limited modification to update the routing structure while the routing performance is bounded and controlled compared to the optimal performance. The proposed protocol is easy to implement. Our analysis shows that the proposed approach is scalable in maintenance overheads, performs efficiently in the routing performance, and provides continuous data delivery during the user movement. We implement the proposed protocol in a prototype system and test its feasibility and applicability by a 49-node testbed. We further conduct extensive simulations to examine the efficiency and scalability of our protocol with varied network settings.

Sensor networks are deemed suitable for large-scale deployments in the wild for a variety of applications. In spite of the remarkable efforts the community put to build the sensor systems, an essential question still remains unclear at the system level, motivating us to explore the answer from a point of real-world deployment view. Does the wireless sensor network really scale? We present findings from a large-scale operating sensor network system, GreenOrbs, with up to 330 nodes deployed in the forest. We instrument such an operating network throughout the protocol stack and present observations across layers in the network. Based on our findings from the system measurement, we propose and make initial efforts to validate three conjectures that give potential guidelines for future designs of large-scale sensor networks. 1) A small portion of nodes bottlenecks the entire network, and most of the existing network indicators may not accurately capture them. 2) The network dynamics mainly come from the inherent concurrency of network operations instead of environment changes. 3) The environment, although the dynamics are not as significant as we assumed, has an unpredictable impact on the sensor network. We suggest that an event-based routing structure can be trained and thus better adapted to the wild environment when building a large-scale sensor network.

Coverage is a fundamental problem in wireless sensor networks (WSNs). From both economic and applicable concerns, designers always would like to provide guaranteed QoS of coverage of WSNs. In this paper, we address two path-coverage problems in WSNs, maximum k-support path coverage (a.k.a. best case coverage) and minimum k-breach path coverage (a.k.a. worst case coverage), in which every point on the desired resultant path is covered by at least k sensors simultaneously while optimizing certain objectives. We present two polynomial-time approaches to find optimal solutions for both maximum k-support coverage problem and minimum k-breach coverage problem. The time complexity of both algorithms are O(k2n log n), where n is the number of deployed sensor nodes and k is the coverage degree. In addition, a number of properties of kth-nearest point Voronoi diagram are presented, which is new to the literature.

We propose a pervasive usage of the sensor network infrastructure as a cyber-physical system for navigating internal users in locations of potential danger. Our proposed application differs from previous work in that they typically treat the sensor network as a media of data acquisition while in our navigation application, in-situ interactions between users and sensors become ubiquitous. In addition, human safety and time factors are critical to the success of our objective. Without any preknowledge of user and sensor locations, the design of an effective and efficient navigation protocol faces nontrivial challenges. We propose to embed a road map system in the sensor network without location information so as to provide users navigating routes with guaranteed safety. We accordingly design efficient road map updating mechanisms to rebuild the road map in the event of changes in dangerous areas. In this navigation system, each user only issues local queries to obtain their navigation route. The system is highly scalable for supporting multiple users simultaneously. We implement a prototype system with 36 TelosB motes to validate the effectiveness of this design. We further conduct comprehensive and large-scale simulations to examine the efficiency and scalability of the proposed approach under various environmental dynamics.

Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past two decades. Most radio-based solutions require a process of site survey, in which radio signatures are collected and stored for further comparison and matching. Site survey involves intensive costs on manpower and time. In this work, we study unexploited RF signal characteristics and leverage user motions to construct radio floor plan that is previously obtained by site survey. On this basis, we design WILL, an indoor localization approach based on off-the-shelf WiFi infrastructure and mobile phones. WILL is deployed in a real building covering over 1600 m2, and its deployment is easy and rapid since site survey is no longer needed. The experiment results show that WILL achieves competitive performance comparing with traditional approaches.

In wireless sensor networks (WSNs), diagnosis is a crucial and challenging task due to the distributed nature and stringent resources. Most previous approaches are supervised, relying on a-priori knowledge of network faults. Our experience with GreenOrbs, a long-term large-scale WSN system, reveals the need of diagnosis in an agnostic manner. Specifically, in addition to predefined faults (i.e., with known types and symptoms), silent failures that are unknown beforehand, account for a large fraction of network performance degradation. Currently, there is no effective solution for silent failures because they are often diverse and highly system-related. In this paper, we propose Agnostic Diagnosis (AD), an online lightweight failure detection approach. AD is motivated by the fact that the system metrics (e.g., radio-on time, number of packets transmitted) of sensor nodes usually exhibit certain correlation patterns. Violations of such patterns indicate potential silent failures. We implement AD on a working WSN consisting of 330 nodes. Our experimental results demonstrate the advantages of AD to discover silent failures, effectively expanding the capacity and scope of WSN diagnosis.

Location-based Service (LBS) becomes increasingly important for wireless and mobile networks. In current LBS schemes, Service Providers (SPs) require users report their accurate locations, which can be illegally used by adversaries to infer sensitive information of users. Privacy disclosure raises serious concerns and limits the application of LBS. Previous solutions either rely on pre-installed centralized intermediary or assume cooperative users. Other distributed solutions, lacking of user obligation, only provide relatively poor anonymity. In this study, we propose a new solution of pseudonym change, which works for non-cooperative users and in the absence of intermediary. The basic idea behind our solution is ad-hoc anonymity. The proposed solution allows users to decide whether or not participating according to their own wills. In addition, artificially generated dummies mix up all the users who have participated in pseudonym changes at different times. Theoretical analysis demonstrates that asynchronous pseudonym change and dummy participation notably enhance privacy protection. We implement our solution on a real-world dataset of mobile phone users, which is collected at Boston, MA, and by the Reality Mining, MIT. The simulation results show that our approach significantly outperforms existing solutions.

Wireless Sensor Networks (WSN) become increasingly popular to collect data over a large area. Given the collected data set, the network manager can extract various kinds of aggregate statistics from the set to characterize the physical space. On the collection of the data, three requirements should be imposed: (1) Privacy: as sensor nodes are source limited and often deployed in an open environment, the sensed data suffer from privacy vulnerabilities. Secure mechanism should be provided to protect data privacy, (2) Communication efficiency: collecting data from large-scale sensor networks often involves large-volume data generation and transmission, which may quickly consume the energy of the WSN. To prolong the lifetimes of the sensor nodes, the sensed data should be transmitted in lightweight manner, (3) Accuracy: the sensed data should be recovered accurately at the base station (BS) so that the manager can manipulate them freely to achieve any precise aggregate statistic he prefers. To satisfy these requirements, we propose two novel privacy-preserving data collection schemes based on compressive sensing techniques. Our schemes address the privacy, communication efficiency and accuracy issues simultaneously. Detailed theoretical analysis and simulation results confirm the high performance of the proposed schemes.

As a large scale real sensor network system, GreenOrbs reveals that locating sensor nodes in the forest still faces great challenges because of volatile and fluctuating environmental factors. In this paper, we present a novel localization scheme, EARL, which provides accurate reference nodes and good ranging quality. We exam the range quality along routing paths by taking complex environmental factors into account, such as forest density, temperature and humidity. To improve localization accuracy, we use power scanning technique to judge the accuracy reference nodes and further calibrate the bad nodes through reverse-localization. To overcome the error propagation, we assign different weights to the range measurement according to the ranging quality. We implemented our localization scheme in GreenOrbs testbed, and evaluate through extensive experiments. The results demonstrate that EARL outperforms the current localization approaches with better accuracy. The localization accuracy achieved by our method is around 20% higher than best existing methods.

Energy-constrained wireless sensor networks are duty-cycled, relaying on multi-hop forwarding to collect data packets. A forwarding scheme generally involves three design elements: media access, link estimation, and routing strategy. Most existing studies, however, focus only on a subset of those three. Disregarding the low duty cycle nature of media access often leads to overestimate of link quality. Neglecting the characteristic of bursty loss over wireless links inevitably consumes much more energy than necessary and underutilizes wireless channels. The routing strategy, if not well tailored to the above factors, results in poor packet delivery performance. In this paper, we propose L2, a practical design of data forwarding in low duty cycle wireless sensor networks. L2addresses link burstiness using multivariate Bernoulli link model. Further incorporated with synchronized rendezvous, L2enables sensor nodes to work in a lazy mode, keep their radios off as long as they can, and allocates the precious energy for only a limited number of promising transmissions. We implement L2on real sensor network testbeds. The results show that L2outperforms state-of-the-art approaches in terms of energy efficiency and network yield.

In this paper, we investigate the problem of controlling node sleep intervals so as to achieve the min-max energy fairness in asynchronous duty-cycling sensor networks. We propose a mathematical model to describe the energy efficiency of such networks and observe that traditional sleep interval setting strategy, i.e., operating sensor nodes with identical sleep intervals, or intuitive control heuristics, i.e., greedily increasing sleep intervals of sensor nodes with high energy consumption rates, hardly perform well in practice. There is an urgent need to develop an efficient sleep interval control strategy for achieving fair and high energy efficiency. To this end, we theoretically formulate the Sleep Interval Control (SIC) problem and find it a convex optimization problem. By utilizing the convex property, we decompose the original problem and propose a distributed algorithm, called GDSIC. In GDSIC, sensor nodes can tune sleep intervals through a local information exchange such that the maximum energy consumption rate in the network approaches to be minimized. The algorithm is self-adjustable to the traffic load variance and is able to serve as a unified framework for a variety of asynchronous duty-cycling MAC protocols. We implement our approach in a prototype system and test its feasibility and applicability on a 50-node testbed. We further conduct extensive trace-driven simulations to examine the efficiency and scalability of our algorithm with various settings.

For cognitive wireless networks, one challenge is that the status of the channels' availability and quality is difficult to predict and quantify. Numerous learning based online channel sensing and accessing strategies have been proposed to address such challenge. In this work, we propose a novel channel sensing and accessing strategy that carefully balances the channel statistics exploration and multichannel diversity exploitation. Unlike traditional MAB-based approaches, in our scheme, a secondary cognitive radio user will sequentially sense the status of multiple channels in a carefully designed ordering. We formulate the online sequential channel sensing and accessing problem as a sequencing multi-armed bandit problem, and propose a novel policy whose regret is in optimal logarithmic rate in time and polynomial in the number of channels. We conducted extensive simulations to compare the performance of our method with traditional MAB-based approach. Our simulation results show that our scheme improves the throughput by more than 30% and speed up the learning process by more than 100%.

Traditional approaches for wireless sensor network diagnosis are mainly sink-based. They actively collect global evidences from sensor nodes to the sink so as to conduct centralized analysis at the powerful back-end. On the one hand, long distance proactive information retrieval incurs huge transmission overhead; On the other hand, due to the coupling effect between diagnosis component and the application itself, sink often fails to obtain complete and precise evidences from the network, especially for the problematic or critical parts. To avoid large overhead in evidence collection process, self-diagnosis injects fault inference modules into sensor nodes and let them make local decisions. Diagnosis results from single nodes, however, are generally inaccurate due to the narrow scope of system performances. Besides, existing self-diagnosis methods usually lead to inconsistent results from different inference processes. How to balance the workload among the sensor nodes in a diagnosis task is a critical issue. In this work, we present a new in-network diagnosis approach named Local-Diagnosis (LD2), which conducts the diagnosis process in a local area. LD2 achieves diagnosis decision through distributed evidence fusion operations. Each sensor node provides its own judgements and the evidences are fused within a local area based on the Dempster-Shafer theory, resulting in the consensus diagnosis report. We implement LD2 on TinyOS 2.1 and examine the performance on a 50 nodes indoor testbed.

Motivated by the needs of precise carbon emission measurement and real-time surveillance for CO2management in cities, we present CitySee, a real-time CO2-monitoring system using sensor networks for an urban area (around 100 square kilometers). In order to conduct environment monitoring in a real-time and long-term manner, CitySee has to address the following challenges, including sensor deployment, data collection, data processing, and network management. In this discussion, we mainly focus on the sensor deployment problem so that necessary requirements like connectivity, coverage, data representability are satisfied. We also briefly go through the solutions for the remaining challenges. In CitySee, the sensor deployment problem can be abstracted as a relay node placement problem under hole-constraint. By carefully taking all constraints and real deployment situations into account, we propose efficient and effective approaches and prove that our scheme uses additional relay nodes at most twice of the minimum. We evaluate the performance of our approach through extensive simulations resembling realistic deployment. The results show that our approach outperforms previous strategies. We successfully apply this design into CitySee, a large-scale wireless sensor network consisting of 1096 relay nodes and 100 sensor nodes in Wuxi City, China.

Traffic analysis presents a serious threat to wireless network privacy due to the open nature of wireless medium. Traditional solutions are mainly based on the mix mechanism proposed by David Chaum, but the main drawback is its low network performance due to mixing and cryptographic operations. We propose a novel privacy preserving scheme based on network coding called Priv-Code to counter against traffic analysis attacks for wireless communications. Priv-Code is able to provide strong privacy protection for wireless networks as the mix system because of its intrinsic mixing feature, and moreover, it can achieve better network performance owing to the advantage of network coding. We first construct a hypergraph-based network coding model for wireless networks, under which we formalize an optimization problem whose objective function is to make each node have identical transmission rate. Then we provide a decentralized algorithm for this optimization problem. After that we develop an information theoretic metric for privacy measurement using entropy, and based on this metric we demonstrate that Priv-Code achieves stronger privacy protection than the mix system while achieving better network performance.

Exploiting constructive interference in wireless networks is an emerging trend for it allows multiple senders transmit an identical packet simultaneously. Constructive interference based flooding can realize millisecond network flooding latency and sub-microsecond time synchronization accuracy, require no network state information and adapt to topology changes. However, constructive interference has a precondition to function, namely, the maximum temporal displacement Δ of concurrent packet transmissions should be less than a given hardware constrained threshold. We disclose that constructive interference based flooding suffers the scalability problem. The packet reception performances of intermediate nodes degrade significantly as the density or the size of the network increases. We theoretically show that constructive interference based flooding has a packet reception ratio (PRR) lower bound (95:4%) in the grid topology. For a general topology, we propose the spine constructive interference based flooding (SCIF) protocol. With little overhead, SCIF floods the entire network much more reliably than Glossy [1] in high density or large-scale networks. Extensive simulations illustrate that the PRR of SCIF keeps stable above 96% as the network size grows from 400 to 4000 while the PRR of Glossy is only 26% when the size of the network is 4000. We also propose to use waveform analysis to explain the root cause of constructive interference, which is mainly examined in simulations and experiments. We further derive the closed-form PRR formula and define interference gain factor (IGF) to quantitatively measure constructive interference.

Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past two decades. Most radio-based solutions require a process of site survey, in which radio signatures are collected and stored for further comparison and matching. Site survey involves intensive costs on manpower and time. In this work, we study unexploited RF signal characteristics and leverage user motions to construct radio floor plan that is previously obtained by site survey. On this basis, we design WILL, an indoor localization approach based on off-the-shelf WiFi infrastructure and mobile phones. WILL is deployed in a real building covering over 1600m2, and its deployment is easy and rapid since site survey is no longer needed. The experiment results show that WILL achieves competitive performance comparing with traditional approaches.

In this work, we address the problem of finding experts using chains of social referrals and profile matching with only local information in online social networks. By assuming that users are selfish, rational, and have privately known cost of participating in the referrals, we design a novel truthful efficient mechanism in which an expert-finding query will be relayed by intermediate users. When receiving a referral request, a participant will locally choose among her neighbors some user to relay the request. In our mechanism, several closely coupled methods are carefully designed to improve the search performance, including, profile matching, social acquaintance prediction, score function for locally choosing relay neighbors, and budget estimation. We conduct extensive experiments on several datasets of online social networks. The extensive study of our mechanism shows that the success rate of our mechanism is about 90% in finding closely matched experts using only local search and limited budget, which significantly improves the previously best rate 20%. The overall cost of finding an expert by our truthful mechanism is about 20% of the untruthful method and only about 2% of the method that always selects high-degree neighbors. The median length of social referral chains is 6 using our localized search decision, which surprisingly matches the well-known small-world phenomenon of global social structures.

We investigate efficient channel learning and opportunity utilization problem in cognitive radio networks (CRN). We find that the sensing order of multiple channels and channel accessing policy play a critical role in designing effective and efficient scheme to maximize the throughput. Leveraging this important finding, we propose a near optimal online channel access policy. We prove that, our policy can converge to an optimal point in a guaranteed probability. Further, we design a computational efficient channel access policy, integrating optimal stopping theory and multi-armed bandit policy effectively. The computational complexity is reduced from O(K NK) to O(K), where N is the number of channels, and K is the maximum number of sensing/probing times in each procedure. Our simulation results validate our policy, showing at least 40% performance improvement over statistically optimal but fixed policy.

In this paper, we propose a novel clock calibration approach called FLIGHT, which leverages the fact that the fluorescent light intensity changes with a stable period that equals half of the alternating current's. By tuning to the light emitted from indoor fluorescent lamps, FLIGHT can intelligently extract the light period information and achieve network wide time calibration by referring to such a common time reference. We address a series of practical challenges and implement FLIGHT in TelosB motes. We conduct comprehensive experiments using a 12-node test-bed in both static and mobile environments. Over one-week measurement suggests that compared with existing technologies, FLIGHT can achieve tightly synchronized time with low energy consumption.

In this demo, we propose a novel clock calibration approach called FLIGHT, which leverages the fact that the fluorescent light intensity changes with a stable period that equals half of the alternating current's. By tuning to the light emitted from indoor fluorescent lamps, FLIGHT can intelligently extract the light period information and achieve network wide time calibration by referring to such a common time reference. We address a series of practical challenges and implement FLIGHT in TelosB motes. In this demonstration, we will show that by taking advantage of the stability of the AC frequency, the detected light intensity, even from different lamps, exhibits a consistent and stable period. FLIGHT can achieve tightly synchronized time with low energy consumption. In addition, since FLIGHT is independent to the network message exchange, time synchronization can be retained even when the network is temporarily disconnected. Such characteristics particularly suit various mobility-enabled scenarios.

Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past decades. Most radio-based solutions require a process of site survey, in which radio signatures of an interested area are annotated with their real recorded locations. Site survey involves intensive costs on manpower and time, limiting the applicable buildings of wireless localization worldwide. In this study, we investigate novel sensors integrated in modern mobile phones and leverage user motions to construct the radio map of a floor plan, which is previously obtained only by site survey. On this basis, we design LiFS, an indoor localization system based on off-the-shelf WiFi infrastructure and mobile phones. LiFS is deployed in an office building covering over 1600m2, and its deployment is easy and rapid since little human intervention is needed. In LiFS, the calibration of fingerprints is crowdsourced and automatic. Experiment results show that LiFS achieves comparable location accuracy to previous approaches even without site survey.

We present a comprehensive delay performance measurement and analysis in an operational large-scale urban wireless sensor network. We build a light-weight delay measurement system in such a network and present a robust method to calculate per-packet delay. Through analysis of delay and system metrics, we seek to answer the following fundamental questions: what are the spatial and temporal characteristics of delay performance in a real network? what are the most important impacting factors and is there any practical model to capture those factors? what are the implications to protocol design? In this paper, we explore the important factors from the data in presence of various metrics and randomness, and show that the important factors are not necessarily the same with that in Internet. Further, we propose a delay model to capture those factors and validate it in the network. We revisit several prevalent protocol designs such as Collection Tree Protocol, opportunistic routing and Dynamic Switching based Forwarding, and show the implications to protocol designs.

Emergency navigation is an emerging application of wireless sensor networks with significant research and social values. In order to ensure the safety and timeliness of navigation for the users, most of the existing works model navigation as a path-planning problem and adopt different metrics, such as the shortest route, the minimum exposure path, and the maximum safe distance. Without sufficient consideration of the dynamics of danger, the existing approaches are likely to cause users to move back and forth during navigation, known as oscillation. Frequent oscillations inevitably result in the user remaining in danger for a longer period of time, amplification the user's panic, and eventual decrease in the chances of survival. In this paper we take users' oscillations in the dynamic environments into account and quantify the local success rate of navigation using a metric called ENO (Expected Number of Oscillations). We then propose OPEN, an oscillation-free navigation approach that minimizes the probability of oscillation and guarantees the success rate of emergency navigation. We implement OPEN and evaluate its performance through test-bed experiments and extensive simulations. The results demonstrate that OPEN outperforms the current state-of-the-arts approaches with respect to user safety and navigation efficiency.

Coverage problem is a fundamental issue in wireless ad hoc and sensor networks. Previous techniques for coverage scheduling often require accurate location information or range measurements, which cannot be easily obtained in resource-limited ad hoc and sensor networks. Recently, a method based on algebraic topology is proposed to achieve coverage verification using only connectivity information. The topological method sheds some light on the issue of location-free coverage. Unfortunately, the needs of centralized computation and rigorous restriction on sensing and communication ranges greatly limit the applicability in practical large-scale distributed sensor networks. In this work, we make the first attempt toward establishing a graph theoretical framework for connectivity-based coverage with configurable coverage granularity. We propose a novel coverage criterion and scheduling method based on cycle partition. Our method is able to construct a sparse coverage set in a distributed manner, using purely connectivity information. Compared with existing methods, our design has a particular advantage, which permits us to configure or adjust the quality of coverage by adequately exploiting diverse sensing ranges and specific requirements of different applications. We formally prove the correctness and evaluate the effectiveness of our approach through extensive simulations and comparisons with the state-of-the-art approaches.

We study the asymptotic networking-theoretic multicast capacity bounds for random extended networks (REN) under Gaussian channel model, in which all wireless nodes are individually power-constrained. During the transmission, the power decays along path with attenuation exponent \alpha > 2. In REN, n nodes are randomly distributed in the square region of side length \sqrt{n}. There are n_s randomly and independently chosen multicast sessions. Each multicast session has n_d+1 randomly chosen terminals, including one source and n_d destinations. By effectively combining two types of routing and scheduling strategies, we analyze the asymptotic achievable throughput for all n_s=\omega (1) and n_d. As a special case of our results, we show that for n_s=\Theta (n), the per-session multicast capacity for REN is of order \Theta ({1\over \sqrt{n_d n}}) when n_d=O({n\over ({\log n})^{\alpha +1}} ) and is of order \Theta ({1\over n_d} \cdot (\log n)^{-{\alpha \over 2} }) when n_d=\Omega ({n\over \log n} ).

Peer-to-Peer multikeyword searching requires distributed intersection/union operations across wide area networks, raising a large amount of traffic cost. Existing schemes commonly utilize Bloom Filters (BFs) encoding to effectively reduce the traffic cost during the intersection/union operations. In this paper, we address the problem of optimizing the settings of a BF. We show, through mathematical proof, that the optimal setting of BF in terms of traffic cost is determined by the statistical information of the involved inverted lists, not the minimized false positive rate as claimed by previous studies. Through numerical analysis, we demonstrate how to obtain optimal settings. To better evaluate the performance of this design, we conduct comprehensive simulations on TREC WT10G test collection and query logs of a major commercial web search engine. Results show that our design significantly reduces the search traffic and latency of the existing approaches.

Reverse skyline query plays an important role in many sensing applications, such as environmental monitoring, habitat monitoring, and battlefield monitoring. Due to the limited power supplies of wireless sensor nodes, the existing centralized approaches, which do not consider energy efficiency, cannot be directly applied to the distributed sensor environment. In this paper, we investigate how to process reverse skyline queries energy efficiently in wireless sensor networks. Initially, we theoretically analyzed the properties of reverse skyline query and proposed a skyband-based approach to tackle the problem of reverse skyline query answering over wireless sensor networks. Then, an energy-efficient approach is proposed to minimize the communication cost among sensor nodes of evaluating range reverse skyline query. Moreover, optimization mechanisms to improve the performance of multiple reverse skylines are also discussed. Extensive experiments on both real-world data and synthetic data have demonstrated the efficiency and effectiveness of our proposed approaches with various experimental settings.

Location awareness is highly critical for wireless ad-hoc and sensor networks. Many efforts have been made to solve the problem of whether or not a network can be localized. Nevertheless, based on the data collected from a working sensor network, it is observed that the network is not always entirely localizable. Theoretical analyses also suggest that, in most cases, it is unlikely that all nodes in a network are localizable, although a (large) portion of the nodes can be uniquely located. Existing studies merely examine whether or not a network is localizable as a whole; yet two fundamental questions remain unaddressed: First, given a network configuration, whether or not a specific node is localizable? Second, how many nodes in a network can be located and which are them? In this study, we analyze the limitation of previous works and propose a novel concept of node localizability. By deriving the necessary and sufficient conditions for node localizability, for the first time, it is possible to analyze how many nodes one can expect to locate in sparsely or moderately connected networks. To validate this design, we implement our solution on a real-world system and the experimental results show that node localizability provides useful guidelines for network deployment and other location-based services.

In order to improve scalability and to reduce the maintenance overhead for structured peer-to-peer (P2P) networks, researchers have proposed architectures based on several interconnection networks with a fixed-degree and a logarithmical diameter. Among existing fixed-degree interconnection networks, the Kautz digraph has many distinctive topological properties compared to others. It, however, requires that the number of peers have the some given values, determined by peer degree and network diameter. In practice, we cannot guarantee how many peers will join a P2P network at a given time, since a P2P network is typically dynamic with peers frequently entering and leaving. To address such an issue, we propose the balanced Kautz tree and Kautz ring structures. We further design a novel structured P2P system, called BAKE, based on the two structures that has the logarithmical diameter and constant degree, even the number of peers is an arbitrary value. By keeping a total ordering of peers and employing a robust locality-preserved resource placement strategy, resources that are similar in a single or multidimensional attributes space are stored on the same peer or neighboring peers. Through analysis and simulation, we show that BAKE achieves the optimal diameter and as good a connectivity as the Kautz digraph does (almost achieves the Moore bound), and supports the exact as well as the range queries efficiently. Indeed, the structures of balanced Kautz tree and Kautz ring we propose can also be applied to other interconnection networks after minimal modifications, for example, the de Bruijn digraph.

Efficient and effective full-text retrieval in unstructured peer-to-peer networks remains a challenge in the research community. First, it is difficult, if not impossible, for unstructured P2P systems to effectively locate items with guaranteed recall. Second, existing schemes to improve search success rate often rely on replicating a large number of item replicas across the wide area network, incurring a large amount of communication and storage costs. In this paper, we propose BloomCast, an efficient and effective full-text retrieval scheme, in unstructured P2P networks. By leveraging a hybrid P2P protocol, BloomCast replicates the items uniformly at random across the P2P networks, achieving a guaranteed recall at a communication cost of O(√N), where N is the size of the network. Furthermore, by casting Bloom Filters instead of the raw documents across the network, BloomCast significantly reduces the communication and storage costs for replication. We demonstrate the power of BloomCast design through both mathematical proof and comprehensive simulations based on the query logs from a major commercial search engine and NIST TREC WT10G data collection. Results show that BloomCast achieves an average query recall of 91 percent, which outperforms the existing WP algorithm by 18 percent, while BloomCast greatly reduces the search latency for query processing by 57 percent.