Modern AI services must continually adapt to newly joined domains, yet delivering high-quality customized models is hampered by label sparsity, domain shifts, and tight budgets. We formulate this challenge as the learning system expansion problem and introduce HaT, an efficient heterogeneity-aware knowledge-transfer framework. HaT first selects a small set of high-quality source models with minimal overhead, and then fuses their imperfect predictions through a sample-wise attention mixer. Later, it adaptively distills the fused knowledge into target models via a knowledge dictionary. Extensive experiments on different tasks and modalities show that HaT outperforms state-of-the-art baselines by up to 16.5% accuracy, and saves 31.1% training time and up to 93.0% traffic.

The analog-to-digital converter (ADC) in a radio frequency (RF) front-end and digital signal processing (DSP) are significant sources of energy consumption, particularly in low-power systems like LoRa. To reliably demodulate weak signals, current systems rely on heavy oversampling - often 8x the signal bandwidth - which imposes a substantial and persistent oversampling tax on the analog front-end and DSP. This paper investigates if this tax can be mitigated by adapting techniques from image and video super resolution. We propose RF Super Resolution, a lightweight, real-time neural upscaler for RF signals. Our approach pairs an efficient digital interpolation algorithm with a shallow four-layer CNN. The neural network is trained to learn and correct the residual artifacts introduced by the digital upsampling and noise, effectively mimicking the output of a high-rate analog ADC and denoising filter. We validate our system on a large-scale, over-the-air LoRa study. Our results show that RF-SR, given a 2× Nyquist input (250 kHz), restores demodulation performance of a native 8× oversampled (2 MHz) system at half its sampling rate (1 MHz). This effectively removes the analog oversampling requirement, and provides an additional 1.25 dB SNR gain over the oversampled baseline, making it an efficient and effective signal enhancer suitable for gateway integration or post training quantized deployment at the end-node.

Dynamic Spectrum Sharing (DSS) is essential for optimizing spectrum utilization in modern wireless systems, but it requires high analog-to-digital converter (ADC) sampling rates, leading to increased costs and power consumption. Existing sub-Nyquist sampling techniques partially address these issues but struggle with dense spectrum adaptability, real-time performance, and efficiency. This paper presents RippleSense, a scalable and efficient wideband spectrum sensing approach capable of capturing GHz of densely occupied spectrum at sub-Nyquist ADC sampling rates. A novel sub-Nyquist sampling method is introduced by injecting distinct signatures into observed signals over different Nyquist zones before sampling, allowing programmatically reconstructing the full spectrum even after the Nyquist zones are folded to baseband due to inadequate ADC sampling rates. To showcase the scalability of this approach, a high-performance, multi-GHz spectrum sensing platform is implemented together with a highly parallelizable reconstruction algorithm that can process the data stream in real-time. Experimental evaluation has shown that the proposed approach supports operating configurations with signal-to-noise ratios as low as -10 dB and time resolutions down to 10 ns, enabling the capture of single radar pulses across bandwidths of up to 10 GHz using our prototype.

SparkLink Low Energy (SLE) is a new short-range wireless communication technology designed to satisfy the increasing demands of future short-range scenarios. While SLE promises fundamental improvements over traditional Bluetooth, a thorough real-world evaluation is lacking. This article provides a detailed evaluation framework that includes access layer benchmarking and end-to-end application validation, presenting the first comprehensive comparison between SLE and traditional Bluetooth technologies. Benchmarking reveals SLE offers a 4× increase in data rate, a 3.84× reduction in latency, and a 10 dB improvement in sensitivity. To evaluate SLE against actual application QoS requirements, we implement an end-to-end evaluation system for validation across three practical application scenarios. Based on experimentation and parameter analysis, we propose a design for QoS-centered optimization. These results underscore SLE’s fundamental advancements over Bluetooth and its potential in real-world applications, offering insights for further optimization of this communication technology.

The advent of Large Language Models (LLMs) has profoundly transformed our lives, revolutionizing interactions with AI and lowering the barrier to AI usage. While LLMs are primarily designed for natural language interaction, the extensive embedded knowledge empowers them to comprehend digital sensor data. This capability enables LLMs to engage with the physical world through IoT sensors and actuators, performing a myriad of AIoT tasks. Consequently, this evolution triggers a paradigm shift in conventional AIoT application development, democratizing its accessibility to all by facilitating the design and development of AIoT applications via natural language. However, some limitations need to be addressed to unlock the full potential of LLMs in AIoT application development. First, existing solutions often require transferring raw sensor data to LLM servers, which raises privacy concerns, incurs high query fees, and is limited by token size. Moreover, the reasoning processes of LLMs are opaque to users, making it difficult to verify the robustness and correctness of inference results. This paper introduces AutoIOT, an LLM-based automated program generator for AIoT applications. AutoIOT enables users to specify their requirements using natural language (input) and automatically synthesizes interpretable programs with documentation (output). AutoIOT automates the iterative optimization to enhance the quality of generated code with minimum user involvement. AutoIOT not only makes the execution of AIoT tasks more explainable but also mitigates privacy concerns and reduces token costs with local execution of synthesized programs. Extensive experiments and user studies demonstrate AutoIOT's remarkable capability in program synthesis for various AIoT tasks. The synthesized programs can match and even outperform some representative baselines.

We present AeroRelief, an autonomous UAV first-responder system for rapid delivery of critical medical supplies in hard-to-reach areas. Integrating AI-assisted dispatch, real-time path planning, and a winch-based payload mechanism, AeroRelief responds swiftly to emergencies with minimal human intervention. The system uses a long-range LoRa command link for robust communication and delivers supplies mid-air without landing. Our demonstration showcases the complete workflow—from emergency identification to UAV deployment—highlighting AeroRelief's potential to accelerate rescue missions and enhance survival outcomes in remote settings.

This paper presents the first nationwide deployment of human activity recognition (HAR) technology in the on-demand food delivery industry. We successfully adapted the state-of-the-art LIMU-BERT foundation model to the delivery platform. Spanning three phases over two years, the deployment progresses from a feasibility study in Yangzhou City to nationwide adoption involving 500,000 couriers across 367 cities in China. The adoption enables a series of downstream applications, and large-scale tests demonstrate its significant operational and economic benefits, showcasing the transformative potential of HAR technology in real-world applications. Additionally, we share lessons learned from this deployment and open-source our LIMU-BERT pretrained with millions of hours of sensor data.

Model splitting and offloading part of the DNN model from the mobile device to the cloud server, known as collaborative inference, improves end-to-end latency and server throughput in CNN-based models. However, current collaborative approaches do not apply to popular Transformer-based models, as these models generate large intermediate outputs with significant transmission latency and have a uniform block structure that makes it challenging to serve tail models efficiently on the server. We propose CollabTrans, a collaborative inference framework designed for Transformer-based models to enhance server scalability when handling requests from numerous end devices, taming the large output with truncated SVD and serving heterogeneous tail models by sharing a complete model. We demonstrate our framework with a real-time image classification mobile application together with background inference request traffic, showing the high inference throughput of our framework.

Due to the ubiquitous deployment of WiFi infrastructure, numerous studies have employed WiFi RSSI fingerprinting for indoor localization. However, fingerprinting methods necessitate labour-intensive site surveys for fingerprint collection and location annotation. To address these limitations, we propose WiMU, a real-time indoor localization system that integrates WiFi and inertial measurement unit (IMU) data to enhance the real-time performance and accuracy of localization. WiMU operates on commodity WiFi infrastructure without the need for additional hardware, leveraging crowd-sourced user trajectories to learn spatial representations of access points (APs). These representations can be fine-tuned with minimal labeled data to support effective localization. Extensive evaluations demonstrate that WiMU reduces the cost of building an indoor localization system while ensuring high positioning accuracy, paving the way for the large-scale deployment of real-time indoor localization systems.

This paper presents Babel, the expandable modality alignment model, specially designed for multi-modal sensing. While there has been considerable work on multi-modality alignment, they all struggle to effectively incorporate multiple sensing modalities due to the data scarcity constraints. How to utilize multi-modal data with partial pairings in sensing remains an unresolved challenge.

Self-supervised learning enables effective model pre-training on large-scale unlabeled data, which is crucial for user-specific fine-tuning in mobile sensing applications. However, pre-trained models often face significant domain shifts during fine-tuning due to user diversity, leading to performance degradation. To address this, we propose SelfReplay, an adaptive approach designed to align self-supervised models to different domains. SelfReplay consists of two stages: MetaSSL, which leverages meta-learning with self-supervised learning to pre-train domain-adaptive weights, and ReplaySSL, which further adapts the pre-trained model to each user's domain by replaying the meta-learned self-supervised task with a few user-specific samples. This produces a personalized model tailored to each user. Evaluations on mobile sensing benchmarks demonstrate that SelfReplay outperforms existing baselines, improving the F1-score by 9.4%p on average. On-device analyses on a commodity smartphone show the efficiency of SelfReplay's adaptation step, required just once after deployment, with SimCLR completing in only 10 seconds while using less than 100MB of memory.

The deployment of low-power wide-area networks (LPWAN) in urban environments faces a critical challenge with signal blockage caused by dense obstacles like buildings, resulting inblind spotswhere end nodes have difficulty reaching the gateway. This paper proposes LoRaMirror, a reflective smart surface design, to essentially eliminate these blind spots and improve overall communication in urban LoRaWAN. LoRaMirror is different from existing smart surface designs, as it addresses unprecedented challenges posed by LPWAN's unique application scenario of extremely long communication distances, extremely low data transmission rates, and extremely wide coverage. LoRaMirror is prototyped with a 16-antenna multi-layer array and the experimental results show significant performance gains in real world practice.

Rural Internet of Things (IoT) systems connect sensors and actuators in remote areas, serving crucial roles in agriculture and environmental monitoring. Given the absence of networking infrastructure for backhaul in these regions, satellite IoT techniques offer a cost-effective solution for connectivity. However, current satellite IoT architectures often struggle to deliver high performance due to temporal and spatial link challenges. This paper presents SateRIoT, a new network architecture with temporal link estimation and spatial link sharing that fully exploits the capability of space low-cost low-earth-orbit (LEO) IoT satellites and ground low-power wide area (LPWA) IoT techniques in rural areas. First, we introduce a bursty link model that predicts the number of transmittable packets within a transmission window, reducing energy waste from failed uplink transmissions. Moreover, we enhance the model by selecting informative features and optimizing the window length. Additionally, we develop a multi-hop flooding protocol that enables gateways to buffer and share data packets across the network while incorporating a priority data queue to avoid duplicate transmissions. We implement SateRIoT with commercial-off-the-shelf (COTS) IoT satellite and LoRa radios, then evaluate its performance based on real deployment and real-world collected traces. The results show that SateRIoT can consume 3.3X less energy consumption for an individual gateway. Moreover, SateRIoT offers up to a 5.6X reduction in latency for a single packet and a 1.9X enhancement in throughput.

The accuracy of vision-based face recognition suffers in challenging scenarios, such as foggy or smoky weather, poor lighting, and blockage by objects like facial masks. This paper proposes an acoustic-based facial recognition system based on acoustic facial spectrum - a novel acoustic representation of human faces in 3D space. Specifically, we divide the 3D space into cubes and profile the distribution of the acoustic signal reflected by the human face inside each cube. Generating such a per-cube acoustic profile is challenging in relating each reflected signal path back to the physical location of its reflecting cube. To address the challenge, we propose a novel multipath resolving algorithm that is capable of distinguishing signal reflection happened within different cube. Based on the facial spectrum, we propose a discriminator-recognizer network that can robustly recognize human faces under varying face-microphone distances or even in presence of facial mask blockage. Extensive experimental results demonstrate that the proposed system achieves over 95% average recognition accuracy for cases with and without mask blockage. The research artifacts accompanying this paper are available via DOI: 10.5281/zenodo.11094213.

Given the fact that WiFi-based sensing can be realized through the reuse of WiFi communication facilities and frequency bands, integrated sensing and communication (ISAC) emerges as a pivotal direction for future WiFi standards, such as IEEE 802.11bf. Traditional WiFi sensing systems extract channel state information (CSI) from exclusive WiFi packets to quantify the characteristics of the sensing target. This poses challenges for existing WiFi systems originally designed for communication purposes, as it demands high-quality and sufficient CSI measurements. In this paper, we propose SenCom as a step towards forward-compatible ISAC solution. SenCom extracts CSI from general WiFi packets, enabling CSI calibration across different WiFi communication modes and delivering quality CSI measurements for upper-layer sensing applications. A fitting-resampling scheme and an incentive strategy are also developed. The former one is to obtain evenly sampled CSI with consistent dimensionality and the latter one is to guarantee sufficient CSI measurements over time. We build a prototype of SenCom and conduct extensive experiments involving 15 participants. The results show that SenCom’s competence for a variety of sensing tasks while making minimal compromises to WiFi communication performance.

Device-free hand-writing systems identify the content that a user writes by hand movement in the air, thus providing an intuitive human computer interface. In this paper, we propose WiRITE, a Wi-Fi hand-writing recognition system built with commodity Wi-Fi APs. Unlike most existing machine learning based hand-writing recognition systems, which are often subject to severe limitations in generality, e.g., high training overhead when adapted across hand-writing alphabets, environments, and users, WiRITE is designed with unique consideration of its generality when applied to practice—being application-transferable, environment-agnostic, and user-independent. With little training overhead, WiRITE behaves inclusively to different users, environments, and applications, stemming from a comprehensive design of signal processing that is built into its core machine learning model. Extensive evaluation is conducted with five users for three applications, i.e., recognizing Digits, English letters, and Chinese characters, in realistic office environment. The experiment results demonstrate that WiRITE provides at least 0.9 accuracy in various combinations of users and applications with 0.93 accuracy in average.

Deep learning–based video analytics demands high network bandwidth to ferry the large volume of data when deployed on the cloud. When incorporated at the edge side, only lightweight deep neural network (DNN) models are affordable due to computational constraint. In this article, a cloud–edge collaborative architecture is proposed combining edge-based inference with cloud-assisted continuous learning. Lightweight DNN models are maintained at the edge servers and continuously retrained with a more comprehensive model on the cloud to achieve high video analytics performance while reducing the amount of data transmitted between edge servers and the cloud. The proposed design faces the challenge of constraints of both computation resources at the edge servers and network bandwidth of the edge–cloud links. An accuracy gradient-based resource allocation algorithm is proposed to allocate the limited computation and network resources across different video streams to achieve the maximum overall performance. A prototype system is implemented and experiment results demonstrate the effectiveness of our system with up to 28.6% absolute mean average precision gain compared with alternative designs.

CSI feedback is an important problem of massive multiple-input multiple-output (MIMO) technology because the feedback overhead is proportional to the number of sub-channels and the number of antennas, both of which scale with the size of the massive MIMO system. Deep learning-based CSI feedback methods have been widely adopted recently owing to their superior performance. Despite the success, current approaches have not fully exploited the relationship between the characteristics of CSI data and the deep learning framework. In this paper, we propose a jigsaw puzzles aided training strategy (JPTS) to enhance the deep learning-based massive MIMO CSI feedback approaches by maximizing mutual information between the original CSI and the compressed CSI. We apply JPTS on top of existing state-of-the-art methods. Experimental results show that by adopting this training strategy, the accuracy can be boosted by 12.07% and 7.01% on average in indoor and outdoor environments, respectively. The proposed method is ready to adopt to any existing deep learning frameworks of massive MIMO CSI feedback. Codes of JPTS are available on GitHub for use11https://github.com/SIJIEJI/JPTS.

Given the fact that WiFi-based sensing can be realized by reusing WiFi communication facilities and communication frequency bands, integrated sensing and communication (ISAC) is considered a crucial development direction for future WiFi standards, such as IEEE 802.11bf. Traditional WiFi sensing systems extract channel state information (CSI) from customized WiFi packets to quantify the characteristics of the sensing target. This poses challenges for existing WiFi systems originally designed for communication purposes, as it requires high-quality and sufficient CSI measurements. In this paper, we propose SenCom, which extracts CSI from general WiFi packets. SenCom enables CSI calibration across different WiFi communication modes and provides unified CSI measurements for upper-layer sensing applications. We also devise a fitting-resampling scheme to derive evenly sampled CSI with consistent dimensionality, and an incentive strategy to ensure sufficient CSI measurements over time. We build a prototype of SenCom and perform extensive experiments with 15 participants. The results show that SenCom is competent for a variety of sensing tasks, while incurring little compromise to the WiFi communication performance.

LoRaWAN suffers dramatic performance degradation over a long communication range due to signal attenuation and blockages. To ensure reliable data transfer, LoRaWAN adopts retransmission mechanism where an unacknowledged packet is retransmitted multiple times in the hope of successfully delivering the packet at least once over harsh wireless channels. This retransmission mechanism is ill-suited for LoRa: 1) unsuccessful retransmissions lead to high power consumption for battery-powered LoRa nodes, and 2) a retransmission at another time does not necessarily improve the signal strength over harsh wireless channels.

Existing inertial measurement unit (IMU) based human activity recognition (HAR) approaches still face a major challenge when adopted across users in practice. The severe heterogeneity in IMU data significantly undermines model generalizability in wild adoption. This paper presents UniHAR, a universal HAR framework for mobile devices. To address the challenge of data heterogeneity, we thoroughly study augmenting data with the physics of the IMU sensing process and present a novel adoption of data augmentations for exploiting both unlabeled and labeled data. We consider two application scenarios of UniHAR, which can further integrate federated learning and adversarial training for improved generalization. UniHAR is fully prototyped on the mobile platform and introduces low overhead to mobile devices. Extensive experiments demonstrate its superior performance in adapting HAR models across four open datasets.

This paper presents HandFi, which constructs hand skeletons with practical WiFi devices. Unlike previous WiFi hand sensing systems that primarily employ predefined gestures for pattern matching, by constructing the hand skeleton, HandFi can enable a variety of downstream WiFi-based hand sensing applications in gaming, healthcare, and smart homes. Deriving the skeleton from WiFi signals is challenging, especially because the palm is a dominant reflector compared with fingers. HandFi develops a novel multi-task learning neural network with a series of customized loss functions to capture the low-level hand information from WiFi signals. During offline training, HandFi takes raw WiFi signals as input and uses the leap motion to provide supervision. During online use, only with commercial WiFi, HandFi is capable of producing 2D hand masks as well as 3D hand poses. We demonstrate that HandFi can serve as a foundation model to enable developers to build various applications such as finger tracking and sign language recognition, and outperform existing WiFi-based solutions. Artifacts can be found: https://github.com/SIJIEJI/HandFi

Data scientists and researchers utilize enormous spatio-temporal data and build machine learning models to solve practical problems in diverse domains including intelligent transportation, urban planning, epidemic prediction, and many more. Extracting application-specific features from big spatio-temporal data poses system requirements of heterogeneous data support, efficient and scalable computing over spatial and temporal dimensions, as well as a user-friendly programming interface. This paper presents ST4ML, a distributed spatio-temporal data processing system to support scalable machine-learning-oriented applications. We propose a three-stage pipelining computing framework, namely "selection-conversion-extraction" to abstract the distributed computing flow and implement it based on Apache Spark. To the best of our knowledge, ST4ML is the first of its kind to realize our design considerations. Extensive experiments with real-world datasets evidence that ST4ML outperforms straightforward extensions of existing ST data processing systems by up to an order of magnitude. ST4ML is open-sourced at https://github.com/Panrong/st4ml.

ACM TIOT launched its first special issue on the theme of wireless sensing for IoT. As an important component of the special issue and a novel practice of the journal, an online virtual workshop will be held, with presentations for each of the accepted articles. Welcome to join us for online discussion! Free registration is required for an attendee of the workshop. The zoom link will be shared to registered attendees before the workshop.

While urban rail transit systems are playing an increasingly important role in meeting the transportation demands of people, precise awareness of how the human crowd is distributed within such a system is highly necessary, which serves a range of important applications including emergency response, transit recommendation, and commercial valuation. Most rail transit systems are closed systems where once entered the passengers are free to move around all stations and are difficult to track. In this article, we attempt to estimate the crowd distribution based only on the tap-in and tap-out records of all the rail riders. Specifically, we study Singapore MRT (Mass Rapid Transit) as a vehicle and leverage EZ-Link transit card records to estimate the crowd distribution. Guided by a key observation that the passenger inflows and arrival flows at different MRT stations and time are spatio-temporally correlated due to behavioral consistency of MRT riders, we design and implement a machine learning-based solution, CrowdAtlas, that captures MRT riders’ transition probabilities among stations and across time, and based on that accurately estimates the crowd distribution within the MRT system. Our comprehensive performance evaluations with both trace-driven studies and real-world experiments in MRT disruption cases demonstrate the effectiveness of CrowdAtlas.

This article investigates how the expansion of array size may improve the spatial diversity of state-of-the-art Wi-Fi system and increase its throughput. With comprehensive Wi-Fi measurement studies with augmented antennas, we identify the potential performance gain atop spatial diversity gains from existing technologies like MIMO and beamforming. We propose WINAS, a general Wi-Fi intelligent antenna selection scheme with full system implementation that can be easily integrated with commodity Wi-Fi AP. WINAS provides substantially improved throughput for downlink traffics. Our experimental evaluation suggests that WINAS improves Wi-Fi throughput up to 1.56x, and 1.47x in average, in real user-based evaluation.

Current LoRa networks including those following the LoRaWAN specification use the primitive ALOHA mechanism for media access control due to LoRa’s lack of carrier sense capability. From our extensive measurements, the channel activity detection feature that was recently introduced to LoRa for energy-efficiently detecting preamble chirps can also detect payload chirps reliably. This sheds light on an efficient carrier-sense multiple access protocol that we refer to as LMAC for LoRa networks. This article presents the designs of three advancing versions of LMAC that respectively implement carrier-sense multiple access, and balance the communication loads among the channels defined by frequencies and spreading factors based on the end nodes’ local information and then additionally the gateway’s global information. Experiments on a 50-node lab testbed and a 16-node university deployment show that, compared with ALOHA, LMAC brings up to 2.2× goodput improvement and 2.4× reduction of radio energy per successfully delivered frame. Thus, should LoRaWAN’s ALOHA be replaced with LMAC, network performance boosts can be realized.

Service disruptions of rail transit systems have become more frequent in the past decade in urban cities, due to various reasons, such as power failures, signal errors, and so on. Smart transit cards provide detailed tapping records of commuters, which enable us to infer their trajectories under both normal and disruptive circumstances. In this article, we study and predict the impact of disruptions on commuters and further evaluate the vulnerability of the rail system. Specifically, we define two metrics, stay ratio and travel delay, to quantify the impact, and we derive the predictor of each metric based on the inferred alternative route choices of commuters under disruptive circumstances. We demonstrate that the alternative route choices contribute to more similar feature distribution among different disruptions, which is crucial to tackling the main challenge of abnormal data scarcity and is beneficial for obtaining more reliable predictors for future disruptions. We evaluate our approach with a real-world transit card dataset. The result demonstrates the effectiveness of our method. Based on the predictors, we further analyse the vulnerability of the rail system. An evaluation with cross validation from taxi GPS trajectory data indicates its efficacy in discovering vulnerable rail stations as well as Origin-Destination pairs.

This paper presents our experience in adopting recent research results of mobile phone based indoor/outdoor detection (IODetector) to support the real world business of on-demand food delivery. The real world deployment of the adopted IODetector involves three phases spanning 20 months, during which the deployment scales from a feasibility study across a few areas of interest to a city-wide trial in Shanghai, and eventually to nationwide deployment over 367 cities in China. Iterative development has been performed throughout different deployment phases to excel the IODetector. Large scale evaluation and comparative A/B testing suggest key value of adopting indoor/outdoor detection in the real world business. We also present the lessons learned from the deployment experience including real world know-hows, practical limits and constraints, as well as discussions on design alternatives. We believe this paper provides insights to guide future efforts in translating research results to industry adoptions.

Falls are one of the leading causes of death in the elderly people aged 65 and above. In order to prevent death by sending prompt fall detection alarms, non-invasive radio-frequency (RF) based fall detection has attracted significant attention, due to its wide coverage and privacy preserving nature. Existing RF-based fall detection systems process fall as an activity classification problem and assume that human falls introduce reproducible patterns to the RF signals. We, however, argue that the fall is essentially an accident, hence, its impact is uncontrollable and unforeseeable. We propose to solve the fall detection problem in a fundamentally different manner. Instead of directly identifying the human falls which are difficult to quantify, we recognize the normal repeatable human activities and then identify the fall as abnormal activities out of the normal activity distribution. We implement our idea and build a prototype based on commercial Wi-Fi. We conduct extensive experiments with 16 human subjects. The experiment results show that our system can achieve high fall detection accuracy and adapt to different environments for real-time fall detection.

Mobile cloud offloading is indispensable for inference tasks based on large-scale deep models. However, transmitting privacy-rich inference data to the cloud incurs concerns. This paper presents the design of a system called PriMask, in which the mobile device uses a secret small-scale neural network called MaskNet to mask the data before transmission. PriMask significantly weakens the cloud's capability to recover the data or extract certain private attributes. The MaskNet is cascadable in that the mobile can opt in to or out of its use seamlessly without any modifications to the cloud's inference service. Moreover, the mobiles use different MaskNets, such that the collusion between the cloud and some mobiles does not weaken the protection for other mobiles. We devise a split adversarial learning method to train a neural network that generates a new MaskNet quickly (within two seconds) at run time. We apply PriMask to three mobile sensing applications with diverse modalities and complexities, i.e., human activity recognition, urban environment crowdsensing, and driver behavior recognition. Results show PriMask's effectiveness in all the three applications.

Low-power wide-area network technologies such as long-range wide-area network (LoRaWAN) are promising for collecting low-rate monitoring data from geographically distributed sensors, in which timestamping the sensor data is a critical system function. This article considers a synchronization-free approach to timestamping LoRaWAN uplink data based on signal arrival time at the gateway, which well matches LoRaWAN’s one-hop star topology and releases bandwidth from transmitting timestamps and synchronizing end devices’ clocks at all times. However, we show that this approach is susceptible to a frame delay attack consisting of malicious frame collision and delayed replay. Real experiments show that the attack can affect the end devices in large areas up to about 50,000, m 2 . In a broader sense, the attack threatens any system functions requiring timely deliveries of LoRaWAN frames. To address this threat, we propose a LoRaTS gateway design that integrates a commodity LoRaWAN gateway and a low-power software-defined radio receiver to track the inherent frequency biases of the end devices. Based on an analytic model of LoRa’s chirp spread spectrum modulation, we develop signal processing algorithms to estimate the frequency biases with high accuracy beyond that achieved by LoRa’s default demodulation. The accurate frequency bias tracking capability enables the detection of the attack that introduces additional frequency biases. We also investigate and implement a more crafty attack that uses advanced radio apparatuses to eliminate the frequency biases. To address this crafty attack, we propose a pseudorandom interval hopping scheme to enhance our frequency bias tracking approach. Extensive experiments show the effectiveness of our approach in deployments with real affecting factors such as temperature variations.

No abstract available.

This paper proposes a spatiotemporal deep learning framework, Trajectory-based Graph Neural Network (TrGNN), that mines the underlying causality of flows from historical vehicle trajectories and incorporates that into road traffic prediction. The vehicle trajectory transition patterns are studied to explicitly model the spatial traffic demand via graph propagation along the road network; an attention mechanism is designed to learn the temporal dependencies based on neighborhood traffic status; and finally, a fusion of multi-step prediction is integrated into the graph neural network design. The proposed approach is evaluated with a real-world trajectory dataset. Experiment results show that the proposed TrGNN model achieves over 5% error reduction when compared with the state-of-the-art approaches across all metrics for normal traffic, and up to 14% for atypical traffic during peak hours or abnormal events. The advantage of trajectory transitions especially manifest itself in inferring high fluctuation of flows as well as non-recurrent flow patterns.

While the urban rail transit systems are playing an increasingly important role in meeting the transportation demands of people, the precise awareness of how the human crowd is distributed within the urban rail transit system is highly necessary, which serves to a range of important applications including emergency response, transit recommendation, commercial valuation, etc. Most urban rail transit systems are closed systems where once entered the travelers are free to move around all stations that are connected into the system and are difficult to track. In this paper, we attempt to estimate the crowd distribution within the urban rail transit system based only on the entrance and exit records of all the rail riders. Specifically, we study Singapore MRT (Mass Rapid Transit) as a vehicle and leverage the tap-in and tap-out records of the EZ-Link transit cards to estimate the crowd distribution. Guided by a key observation that the passenger inflows and arrival flows at various MRT stations are spatio-temporally correlated due to behavioral consistence of MRT riders, we design and implement a machine learning based solution, CrowdAtlas, that accurately estimates the crowd distribution within the MRT system. Our trace-driven performance evaluation demonstrates the effectiveness of CrowdAtlas.

Service disruptions of rail transit systems become more frequent in the past decades in urban cities like Singapore, due to various reasons such as power failures, signal errors, etc. We study and predict the impact of disruptions to transit systems and commuters. This benefits service providers in making both short and long term plans to improve their services. Specifically, we define two metrics, stay ratio and travel delay, to quantify the impact. To tackle the main challenge of abnormal data scarcity, i.e., only 6 observed disruptions in our one-year data records, we propose to format the problem into a training problem on a feature space relevant to alternative route choices of the commuters. We demonstrate the new feature space corresponds to more similar data distribution among different disruptions, which is beneficial for training more generalisable predictors for future disruptions. We implement and evaluate our approach with a real-world transit card dataset. The result clearly shows that our method outperforms a range of baseline methods.

This paper presents TrajGen, an approach to generate artificial datasets of mobility trajectories based on an original trajectory dataset while retaining the utility of the original data in supporting various mobility applications. The generated mobility data is disentangled with the original data and can be shared without compromising the data privacy. TrajGen leverages Generative Adversarial Nets combined with a Seq2Seq model to generate the spatial-temporal trajectory data. TrajGen is implemented and evaluated with real-world taxi trajectory data in Singapore. The extensive experimental results demonstrate that TrajGen is able to generate artificial trajectory data that retain key statistical characteristics of the original data. Two case studies, i.e. road map updating and Origin-Destination demand estimation are performed with the generated artificial data, and the results show that the artificial trajectories generated by TrajGen retain the utility of original data in supporting the two applications.

Vehicle trajectories provide essential information to understand the urban mobility and benefit a wide range of urban applications. State-of-the-art solutions for vehicle sensing may not build accurate and complete knowledge of all vehicle trajectories. In order to fill the gap, this paper proposes VeTrac, a comprehensive system that employs widely deployed traffic cameras as a sensing network to trace vehicle movements and reconstruct their trajectories in a large scale. VeTrac fuses mobility correlation and vision-based analysis to reduce uncertainties in identifying vehicles. A graph convolution process is employed to maintain the identity consistency across different camera observations, and a self-training process is invoked when aligning with the urban road network to reconstruct vehicle trajectories with confidence. Extensive experiments with real-world data input of over 7 million vehicle snapshots from over one thousand traffic cameras demonstrate that VeTrac achieves 98% accuracy for simple expressway scenario and 89% accuracy for complex urban environment. The achieved accuracy outperforms alternative solutions by 32% for expressway scenario and by 59% for complex urban environment.

Deep learning greatly empowers Inertial Measurement Unit (IMU) sensors for various mobile sensing applications, including human activity recognition, human-computer interaction, localization and tracking, and many more. Most existing works require substantial amounts of well-curated labeled data to train IMU-based sensing models, which incurs high annotation and training costs. Compared with labeled data, unlabeled IMU data are abundant and easily accessible. In this work, we present LIMU-BERT, a novel representation learning model that can make use of unlabeled IMU data and extract generalized rather than task-specific features. LIMU-BERT adopts the principle of self-supervised training of the natural language model BERT to effectively capture temporal relations and feature distributions in IMU sensor measurements. However, the original BERT is not adaptive to mobile IMU data. By meticulously observing the characteristics of IMU sensors, we propose a series of techniques and accordingly adapt LIMU-BERT to IMU sensing tasks. The designed models are lightweight and easily deployable on mobile devices. With the representations learned via LIMU-BERT, task-specific models trained with limited labeled samples can achieve superior performances. We extensively evaluate LIMU-BERT with four open datasets. The results show that the LIMU-BERT enhanced models significantly outperform existing approaches in two typical IMU sensing applications.

By processing a large dataset composed of daily trajectories of thousands of students in Singapore, we find that, instead of simply picking up students from their homes, an optimal school shuttle planning system needs to learn the real transportation usage and plan across all potential pickup locations for every student to generate need-satisfying routes. It is challenging, however, to perform route planning over a large number of students each having multiple potential pickup locations. We develop a graph-based data structure that embeds potential pickup locations of all students with the awareness of real-world constraints and existing public transits. Based on the graph structure, we prove that the optimal last-mile school shuttle planning problem is NP-hard and thereafter design a Tabu-based expansion algorithm to solve the problem, which strikes at a proper balance between the savings of students' commute time and the total cost of operating the shuttle buses. Extensive experiments with large-scale real-world crowdsensed trajectory data demonstrate that our last-mile school shuttles can save the traveling time for most students by over 20% and the savings can be up to 65% for 10% of the students.

Current localization schemes on mobile devices are experiencing great diversity that is mainly shown in two aspects: the large number of available localization schemes and their diverse performance. This paper presents UniLoc, a unified framework that gains improved performance from multiple localization schemes by exploiting their diversity. UniLoc predicts the localization error of each scheme online based on an error model and real-time context. It further combines the results of all available schemes based on the error prediction results and an ensemble learning algorithm. The combined result is more accurate than any individual schemes. With the flexible design of error modeling and ensemble learning, UniLoc can easily integrate a new localization scheme. The energy consumption of UniLoc is low, since its computation, including both error prediction and ensemble learning, only involves simple linear calculation. Our experience with extensive experiments tells that such easy aggregation incurs little overhead in integrating and training a localization scheme, but gains substantially from the scheme diversity. UniLoc outperforms individual localization schemes by 1.6× in a variety of environments, including > 89% new places where we did not train the error models.

Recently, wearable devices have become increasingly popular in our lives because of their neat features and stylish appearance. However, their tiny sizes bring about new challenges to human-device interaction such as texts input. Although some novel methods have been put forward, they possess different defects and are not applicable to deal with the problem. As a result, we propose an acoustic-based texts-entry system, i.e., EchoWrite, by which texts can be entered with a finger writing in the air without wearing any additional device. More importantly, different from many previous works, EchoWrite runs in a training-free style which reduces the training overhead and improves system scalability. We implement EchoWrite with commercial devices and conduct comprehensive experiments to evaluate its texts-entry performance. Experimental results show that EchoWrite enables users to enter texts at a speed of 7.5 WPM without practice, and 16.6 WPM after about 30-minute practice. This speed is better than touch screen-based method on smartwatches, and comparable with previous related works. Moreover, EchoWrite provides favorable user experience of entering texts.

Low-power wide-area network technologies such as LoRaWAN are promising for collecting low-rate monitoring data from geographically distributed sensors, in which timestamping the sensor data is a critical system function. This paper considers a synchronization-free approach to timestamping LoRaWAN uplink data based on signal arrival time at the gateway, which well matches LoRaWAN’s one-hop star topology and releases bandwidth from transmitting timestamps and synchronizing end devices’ clocks at all times. However, we show that this approach is susceptible to a frame delay attack consisting of malicious frame collision and delayed replay. Real experiments show that the attack can affect the end devices in large areas up to about 50, 000 m2. In a broader sense, the attack threatens any system functions requiring timely deliveries of LoRaWAN frames. To address this threat, we propose a LoRaTS gateway design that integrates a commodity LoRaWAN gateway and a low-power software-defined radio receiver to track the inherent frequency biases of the end devices. Based on an analytic model of LoRa’s chirp spread spectrum modulation, we develop signal processing algorithms to estimate the frequency biases with high accuracy beyond that achieved by LoRa’s default demodulation. The accurate frequency bias tracking capability enables the detection of the attack that introduces additional frequency biases. Extensive experiments show the effectiveness of our approach.

Current LoRa networks including those following the LoRaWAN specification use the primitive ALOHA mechanism for media access control due to LoRa's lack of carrier sense capability. From our extensive measurements, the Channel Activity Detection (CAD) feature that is recently introduced to LoRa for energy-efficiently detecting preamble chirps, can also detect payload chirps reliably. This sheds light on an efficient carrier-sense multiple access (CSMA) protocol that we call LMAC for LoRa networks. This paper presents the designs of three advancing versions of LMAC that respectively implements CSMA, balances the communication loads among the channels defined by frequencies and spreading factors based on the end nodes' local information and then additionally the gateway's global information. Experiments on a 50-node lab testbed and a 16-node university deployment show that, compared with ALOHA, LMAC brings up to 2.2× goodput improvement and 2.4× reduction of radio energy per successfully delivered frame. Thus, should the LoRaWAN's ALOHA be replaced with LMAC, network performance boosts can be realized.

Energy consumption of Internet-of-Things end devices is a major constraint that limits their long-term and large-scale deployment. Conventionally, the radios and processors used in these end devices are major power consumption that drains at the level of milliwatts (mWs). However, in recent decades, backscatter communication has dramatically reduced the power consumed by the radios in end devices to microwatts (μWs), and thus the processor remains the major bottleneck for energy optimization.

Recently, wearable devices have become increasingly popular in our lives because of their neat features and stylish appearance. However, their tiny sizes bring about new challenges to human-device interaction such as texts input. Although some novel methods have been put forward, they possess different defects and are not applicable to deal with the problem. As a result, we propose an acoustic-based texts-entry system, i.e., EchoWrite, by which texts can be entered with a finger writing in the air without wearing any additional device. More importantly, different from many previous works, EchoWrite runs in a training-free style which reduces the training overhead and improves system scalability. We implement EchoWrite with commercial devices and conduct comprehensive experiments to evaluate its texts-entry performance. Experimental results show that EchoWrite enables users to enter texts at a speed of 7.5 WPM without practice, and 16.6 WPM after about 30- minute practice. This speed is better than touch screen-based method on smartwatches, and comparable with previous related works.

Respiration rate monitoring is beneficial for the diagnosis of a variety of diseases, such as heart failure and sleep disorders. Radio Frequency (RF) based respiration rate monitoring systems, namely ultra-wideband radar and COTS device, have been proposed without requiring any direct contact with the detected person. However, existing RF based systems either require expensive UWB radio (radar based) or work only in stationary environments (COTS device based). To address the limitations of both radar based and COTS device based systems, in this paper, we propose RespiRadio, a system that can detect a person's respiration rate in dynamic ambient environments via a single TX-RX pair of WiFi cards. The key novelty of RespiRadio is that it overcomes the limit of existing COTS device based respiration rate systems by synthesizing a wider-bandwidth WiFi radio. With the synthesized WiFi radio, we can identify the path reflected by the breathing person and then analyze the periodicity of the signal power measurements only from this path to infer the respiration rate. We experimentally evaluate the performance of RespiRadio in non-static indoor environments and the results demonstrate that the overall estimation error is 0.152 breaths per minute (bpm).

Wi-Fi localization and tracking face accuracy limitations dictated by antenna count (for angle-of-arrival methods) and frequency bandwidth (for time-of-arrival methods). This paper presents mD-Track, a device-free Wi-Fi tracking system capable of jointly fusing information from as many dimensions as possible to overcome the resolution limit of each individual dimension. Through a novel path separation algorithm, mD-Track can resolve multipath at a much finer-grained resolution, isolating signals reflected off targets of interest. mD-Track can localize human passively at a high accuracy with just a single Wi-Fi transceiver pair. mD-Track also introduces novel methods to greatly streamline its estimation algorithms, achieving real-time operation. We implement mD-Track on both WARP and cheap off-the-shelf commodity Wi-Fi hardware, and evaluate its performance in different indoor environments.

Automated Fare Collection (AFC) systems have been globally deployed for decades, particularly in the public transportation network where the transit fee is calculated based on the length of the trip (a.k.a., distance-based pricing AFC systems). Although most messages of AFC systems are insecurely transferred in plaintext, system operators did not pay much attention to this vulnerability, since the AFC network is basically isolated from the public network (e.g., the Internet)-there is no way of exploiting such a vulnerability from the outside of the AFC network. Nevertheless, in recent years, the advent of Near Field Communication (NFC)-equipped smartphones has opened up a channel to invade into the AFC network from the mobile Internet, i.e., by Host-based Card Emulation (HCE) over NFC-equipped smartphones. In this paper, we identify a novel paradigm of attacks, called LessPay, against modern distance-based pricing AFC systems, enabling users to pay much less than what they are supposed to be charged. The identified attack has two important properties: 1) it is invisible to AFC system operators because the attack never causes any inconsistency in the back-end database of the operators; and 2) it can be scalable to affect a large number of users (e.g., 10,000) by only requiring a moderate-sized AFC card pool (e.g., containing 150 cards). To evaluate the efficacy of the attack, we developed an HCE app to launch the LessPay attack; and the real-world experiments demonstrate not only the feasibility of the LessPay attack (with 97.6 percent success rate) but also its low cost in terms of bandwidth and computation. Finally, we propose, implement and evaluate four types of countermeasures, and present security analysis and comparison of these countermeasures on defending against the LessPay attack.

This paper presents a graph processing based traffic estimation system, GPTE, which is able to achieve high accuracy and high scalability to support city scale traffic estimation. GPTE benefits from its non-linear traffic correlation modeling and the graph-parallel processing framework built on clustered machines. By representing the road network as a property graph, GPTE decomposes the numerous computations involved in non-linear models to vertices and performs traffic estimation via neural network modeling and iterative information propagation. This paper presents our experiences in designing and implementing GPTE on top of the Spark, an emerging cluster computing framework. Extensive experiments are performed with real-world data input from Singapore's transport authority. Experimental results show that GPTE achieves as high as 88 percent accuracy in traffic estimation and up to 8× performance gain in computation efficiency with the optimization techniques applied. Comparison study demonstrates that GPTE outperforms the baseline solutions by 34 percent on accuracy and 46 percent on processing time.

Power delay profiles characterize multipath channel features, which are widely used in motion- or localization-based applications. The performance of power delay profile obtained using commodity Wi-Fi devices is limited by two dominating factors. The resolution of the derived power delay profile is determined by the channel bandwidth, which is however limited on commodity WiFi. The collected CSI reflects the signal distortions due to both the channel attenuation and the hardware imperfection. A direct derivation of power delay profiles using raw CSI measures, as has been done in the literature, results in significant inaccuracy. In this paper, we present Splicer, a software-based system that derives high-resolution power delay profiles by splicing the CSI measurements from multiple WiFi frequency bands. We propose a set of key techniques to separate the mixed hardware errors from the collected CSI measurements. Splicer adapts its computations within stringent channel coherence time and thus can perform well in the presence of mobility. Our experiments with commodity WiFi NICs show that Splicer substantially improves the accuracy in profiling multipath characteristics, reducing the errors of multipath distance estimation to be less than 2 m. Splicer can immediately benefit upper-layer applications. Our case study with recent single-AP localization achieves a median localization error of 0.95 m.

This paper presents StrLight, the first practical VLC system that leverages widely-deployed string lights to transmit data. The data transmission is imperceptible to human eyes. Users can decode the data by mobile devices (e.g., smartphones) equipped with cameras. StrLight primarily differs from existing VLC systems in using string lights which are composed of a large number of small LEDs and thus the unique design to address practical issues including a special data modulation/encoding scheme, a data representation with unstructured/unknown topologies of LEDs in the string light, and a fault tolerance against broken and blocked LEDs. To the best of our knowledge, StrLight is the first practical VLC system of its kind. We build several prototypes of string light transmitters and test with different smartphone models and a customized mobile device as receivers. The experiment results show that StrLight provides an efficient and robust data broadcasting. A string light of 100 LEDs working in 450 Hz and a camera with a capture rate of 30 Hz and an image resolution of as low as 320 × 240 pixels, delivers data rate of ~1 kbps, without observable light flickers.

Due to the increasing popularity of mobile devices, the usage of lifelogging has dramatically expanded. People collect their daily memorial moments and share with friends on the social network, which is an emerging lifestyle. We see great potential of lifelogging applications along with rapid recent growth of the wearables market, where more sensors are introduced to wearables, i.e., electroencephalogram (EEG) sensors, that can further sense the user’s mental activities, e.g., emotions. In this article, we present the design and implementation of Memento, an emotion-driven lifelogging system on wearables. Memento integrates EEG sensors with smart glasses. Since memorable moments usually coincides with the user’s emotional changes, Memento leverages the knowledge from the brain-computer-interface domain to analyze the EEG signals to infer emotions and automatically launch lifelogging based on that. Towards building Memento on Commercial off-the-shelf wearable devices, we study EEG signals in mobility cases and propose a multiple sensor fusion based approach to estimate signal quality. We present a customized two-phase emotion recognition architecture, considering both the affordability and efficiency of wearable-class devices. We also discuss the optimization framework to automatically choose and configure the suitable lifelogging method (video, audio, or image) by analyzing the environment and system context. Finally, our experimental evaluation shows that Memento is responsive, efficient, and user-friendly on wearables.

Long Range (LoRa) is a Low-power Wide-area Network technology designed for the Internet of Things. In recent years, it has gained significant momentum among industrial and research communities. Patented by Semtech, LoRa makes use of chirp spread spectrum modulation to deliver data with promises of long battery life, far-reaching communication distances, and a high node density at the cost of data rate. In this article, we conduct a series of experiments to verify the claims made by Semtech on LoRa technology. Our results show that LoRa is capable of communicating over 10km under line-of-sight environments. However, under non-line-of-sight environments, LoRa’s performance is severely affected by obstructions such as buildings and vegetations. Moreover, the promise of prolonged battery life requires extreme tuning of parameters. Last, a LoRa gateway supports up to 6,000 nodes with PRR requirement of >70%. This study also explores the relationship between LoRa transmission parameters and proposes an algorithm to determine optimal settings in terms of coverage and power consumption under non-line-of-sight environments. It further investigates the impact of LoRa Wide-area Networks on energy consumption and network capacity along with implementation of a LoRa medium access mechanism and possible gains brought forth by implementing such a mechanism.

Current localization schemes on mobile devices are experiencing great diversity that is mainly shown in two aspects: the large number of available localization schemes and their diverse performance. This paper presents UniLoc, a unified framework that gains improved performance from multiple localization schemes by exploiting their diversity. UniLoc predicts the localization error of each scheme online based on an error model and real-time context. It further combines the results of all available schemes based on the error prediction results and an ensemble learning algorithm. The combined result is more accurate than any individual schemes. With the flexible design of error modeling and ensemble learning, UniLoc can easily integrate a new localization scheme. The energy consumption of UniLoc is low, since its computation, including both error prediction and ensemble learning, only involves simple linear calculation. Our experience with extensive experiments tells that such easy aggregation incurs little overhead in integrating and training a localization scheme, but gains substantially from the scheme diversity. UniLoc outperforms individual localization schemes by 1.6X in a variety of environments, including >89% new places where we did not train the error models.

Most digital maps are designed for vehicles and miss a great number of walkways that can facilitate people's daily mobility as pedestrians. Despite of such a fact, most existing map updating approaches only focus on the motorways. To fill the gap, this paper presents VitalAlley, a walkway discovery and verification approach with mobility data from large scale crowdsensing. VitalAlley aims to identify the uncharted walkways from the big but noisy personal mobility data and incorporate these findings into existing incomplete road maps. The implementation of VitalAlley faces the major challenges due to the unstructured nature of the walkways themselves and the noise from crowdsensing data. VitalAlley leverages different aspects of individual mobility to model and estimate the walkable areas, based on which representative walkways that connect known road segments or points of interest are extracted. To verify the new-found walkways, we further propose image based auto-verification with the help of publicly accessible street image database from Google Street View. VitalAlley is implemented and evaluated with real world crowdsensing data from the Singapore National Science Experiment. As a result, 736 walkways (totaling 161 km in distance) are identified from the mobility dataset collected from 108,337 students in Singapore. We manually verify 224 walkways totaling 32.4 km over a 9 km^2 district through on-site inspection. The results suggest over 96% accuracy of VitalAlley in discovering the walkways.

Existing digital maps mainly focus on motorways and miss many walkways facilitating people's daily mobility especially for pedestrians. Based on in-depth analysis of massive human mobility trajectories collected from the National Science Experiment (NSE) in Singapore, we propose a system, discovering walkways from the large scale crowdsensing mobility data. In this demo, we show the new-found walkways discovered by our system in Singapore through a custom visualization platform.

This paper presents our experience in designing, implementing, testing, and applying a general-purpose antenna extension solution with commodity Wi-Fi. The proposed solution, SWAN, builds an array of stitched antennas extended from the radio chains of commodity Wi-Fi. SWAN has low hardware cost and provides easy-to-use interfaces embedded in the Linux kernel. Two application cases for wireless sensing and communication are presented that proves the usefulness of the solution. SWAN is able to provide over 3× performance improvement on Wi-Fi azimuth estimation and localization and over 30% improvement on Wi-Fi throughput over original Wi-Fi AP with three fixed antennas.

Computational Radio Frequency Identification (CRFID) tags operate solely on harvested energy and have emerged as viable platforms for a variety of ubiquitous sensing and computation applications. Due to their battery-less nature, these tags can be permanently deployed in hard-to-reach places where the possibility of tag access is eliminated. In such scenarios, maintaining and upgrading the tag’s firmware becomes infeasible because programming tools, including wired interface and PC-based software, are required to erase, modify, or reprogram the microcontroller unit’s memory. Such limitations necessitate the demand for an over-the-air (OTA) scheme, which can wirelessly reprogram or upgrade the firmware in CRFID tags. In this article, we present R 3 —a reliable OTA reprogramming scheme that is compliant with EPC protocol and requires no hardware upgrade to RFID reader or CRFID tag. We demonstrate our scheme on three platforms, which include both software-defined as well as chip-based CRFID tags, that is, WISP5.1 and Optimized WISP (Opt-WISP), and Spider tag, respectively. The selection also includes both the FLASH- and FRAM-based microcontrollers. We extensively evaluate our scheme in terms of several metrics, including overall system delay, time and energy overhead, and success rate in line with interrogation range. We foresee our endeavor to offer the viability of OTA reprogramming and firmware upgrade for CRFID tokens under practical situations.

This paper presents DopEnc, an acoustic-based encounter profiling system on commercial off-the-shelf smartphones. DopEnc automatically identifies the persons that users interact with in the context of encountering. DopEnc performs encounter profiling in two major steps: (1) Doppler profiling to detect that two persons approach and stop in front of each other via an effective trajectory, and (2) voice profiling to confirm that they are thereafter engaged in an interactive conversation. DopEnc is further extended to support parallel acoustic exploration of many users by incorporating a unique multiple access scheme within the limited inaudible acoustic frequency band. All implementation of DopEnc is based on commodity sensors like speakers, microphones, and accelerometers integrated on mainstream smartphones. We evaluate DopEnc with detailed experiments and a real use-case study of 11 participants. Overall DopEnc achieves an accuracy of 6.9 percent false positive and 9.7 percent false negative in real usage.

Tracking the location of items on the warehouse storage rack automatically is required by manufacturing companies to improve operational efficiency, productivity, and resource utilization. Due to the long reading range and faster data transfer rate, passive ultra-high frequency (UHF) RFID system is explored to localize the items on the rack in this study. A tag is incorporated into an item on the rack for localization using radio signals. The received signal strength indication (RSSI) and radio frequency (RF) phase are utilized for localization. A supervised clustering approach is developed based on the measurements of location tag at the different zone of the rack. The localization accuracy is improved by using the kernel as a similarity measure. If the desired granularity of item location is not small, the lower price point per tag makes passive RFID systems more economical than active RFID systems.

Due to the increasing popularity of mobile devices, the usage of lifelogging has been dramatically expanded. People collect their daily memorial moments and share with friends on the social network, which has been an emerging lifestyle. We see great potential of lifelogging applications along with rapid growth of recent wearable market, where more sensors are introduced to wearables, i.e., electroencephalogram (EEG) sensors, that can further sense the user's mental activities, e.g., emotions. In this paper, we present the design and implementation of Memento, an emotion driven lifelogging system on wearables. Memento integrates EEG sensors with smart glasses. Since memorable moments usually coincides with the user's emotional changes, Memento leverages the knowledge from the brain-computer-interface (BCI) domain to analyze the EEG signals to infer emotions and automatically launch lifelogging based on that. Towards building Memento on COTS wearable devices, we study EEG signals in mobility cases and propose a multiple sensor fusion based approach to estimate signal quality. We also present a customized two-phase emotion recognition architecture, considering both the affordability and efficiency of wearable-class devices. Our experimental evaluation shows that Memento is responsive, efficient and user-friendly on wearables.

Automated Fare Collection (AFC) systems have been globally deployed for decades, particularly in public transportation. Although the transaction messages of AFC systems are mostly transferred in plaintext, which is obviously insecure, system operators do not need to pay much attention to this issue, since the AFC network is well isolated from public network (e.g., the Internet). Nevertheless, in recent years, the advent of Near Field Communication (NFC)-equipped smartphones has bridged the gap between the AFC network and the Internet through Host-based Card Emulation (HCE). Motivated by this fact, we design and practice a novel paradigm of attack on modern distance-based pricing AFC systems, enabling users to pay much less than actually required. Our constructed attack has two important properties: 1) it is invisible to AFC system operators because the attack never causes any inconsistency in the backend database of the operators; and 2) it can be scalable to large number of users (e.g., 10,000) by maintaining a moderate-sized AFC card pool (e.g., containing 150 cards). Based upon this constructed attack, we developed an HCE app, named LessPay. Our real-world experiments on LessPay demonstrate not only the feasibility of our attack (with 97.6% success rate), but also its low-overhead in terms of bandwidth and computation.

This paper presents iType, a system that uses eye gaze for typing private information on commodity mobile platforms. The design combats three primary challenges: 1) relatively low accuracy of mobile gaze tracking; 2) difficulties in correcting input errors due to lacking the comparison with the true text-entry value; and 3) device motions and other noises that may interfere gaze tracking accuracy and thus the iType performance. We devise a set of effective techniques, including leveraging a collective behavior of the gaze tracking results, unique correlation of the typing error spatial distributions, and motion sensor hints from mobile devices, to address above challenges. A set of enhancement techniques are applied to further improve iType's robustness and reliability. We consolidate above designs and implement iType on iOS platform. Evaluations show that iType achieves high keystroke detection accuracy for the secure typing within a reasonable short latency.

The papers in this special section focus on the Internet of Things for use in smart cities and urban informatics. CITIES around the world are currently under quick transition toward a low-carbon environment, high quality of living, and resource-efficient economy. Smart systems are crucial components for building sustainable cities and aggregating comprehensive urban informatics. Internet-of-Things (IoT) is an emerging technology evolved from the convergence of wireless technologies, embedded systems, and the Internet. Extensive research on IoT is taking place on sensing and automatic control, network infrastructure and communication, and big data analytics. This facilitates a multidisciplinary approach for developing integrated solutions and creating novel applications to build a sustainable society. Combinations of IoT with autonomous systems, machine intelligence, sensor and actuator networks provide smart functions in wide application domains, including environmental monitoring, smart transportation, industrial production, and security surveillance. This special section on Internet-of-Things for smart cities and urban informatics is focused on the development, adoption, and application of IoT technology to improve the efficiency of cities and enhance urban services in society. It covers topics including smart transportation, smart buildings, data transmission and analytics, system designs, and experiments of IoT for smart cities.

This paper presents a participatory sensing-based urban traffic monitoring system. Different from existing works that heavily rely on intrusive sensing or full cooperation from probe vehicles, our system exploits the power of participatory sensing and crowdsources the traffic sensing tasks to bus riders' mobile phones. The bus riders are information source providers and, meanwhile, major consumers of the final traffic output. The system takes public buses as dummy probes to detect road traffic conditions, and collects the minimum set of cellular data together with some lightweight sensing hints from the bus riders' mobile phones. Based on the crowdsourced data from participants, the system recovers the bus travel information and further derives the instant traffic conditions of roads covered by bus routes. The real-world experiments with a prototype implementation demonstrate the feasibility of our system, which achieves accurate and fine-grained traffic estimation with modest sensing and computation overhead at the crowd.

Screen-camera links for Visible Light Communication (VLC) are diverse, as the link quality varies according to many factors, such as ambient light and camera's performance. This paper presents SoftLight, a channel coding approach that considers the unique channel characteristics of VLC links and automatically adapts the transmission data rate to the link qualities of various scenarios. SoftLight incorporates two new ideas: (1) an expanded color modulation interface that provides soft hint about its confidence in each demodulated bit and establishes a bit-level VLC erasure channel, and (2) a rateless coding scheme that achieves bit-level rateless transmissions with low computation complexity and tolerates the false positive of bits provided by the soft hint enabled erasure channel. SoftLight is orthogonal to the visual coding schemes and can be applied atop any barcode layouts. We implement SoftLight on Android smartphones and evaluate its performance under a variety of environments. The experiment results show that SoftLight can correctly transmit a 22-KByte photo between two smartphones within 0.6 second and improves the average goodput of the state-of-the-art screen-camera VLC solution by 2.2×.

This paper presents Pando, a completely contention-free data dissemination protocol for wireless sensor networks. Pando encodes data by Fountain codes and disseminates the rateless stream of encoded packets along the fast and parallel pipelines built on constructive interference and channel diversity. Since every encoded packet contains innovative information to the original data object, Pando avoids duplicate retransmissions and fully exploits the wireless broadcast effect in data dissemination. To transform Pando into a practical system, we devise several techniques, including the integration of Fountain coding with the timing-critical operations of constructive interference and pipelining, a silence-based feedback scheme for the one-way pipelined dissemination, and packet-level adaptation of network density and channel diversity. Based on these techniques, Pando can accomplish data dissemination entirely over the fast and parallel pipelines. We implement Pando in Contiki and for TelosB motes. We evaluate Pando with various settings on two large-scale open test beds, Indriya and Flocklab. Our experimental results show that Pando can provide 100% reliability and reduce the dissemination time of state of the art by 3.5×.

This paper presents a hopless networking paradigm. Incorporating recent techniques of rateless codes, senders break packets into rateless information streams and each single stream automatically adapts to diverse channel qualities at all potential receivers, regardless of their hop distances. The receivers are capable of accumulating rateless information pieces from different senders and jointly decoding the packet, largely improving throughput. We develop a practical protocol, called HOPE, which instantiates the hopless networking paradigm. Compared with the existing opportunistic routing protocol family, HOPE best exploits the wireless channel diversity and takes full advantage of the wireless broadcast effect. HOPE incurs minimum protocol overhead and serves general networking applications. We extensively evaluate the performance of HOPE with indoor network traces collected from USRP N210s and Intel 5300 NICs. The results show that HOPE achieves 1.7× and 1.3× goodput gain over EXOR and MIXIT, respectively. We further implement HOPE on a sensor network testbed, achieving the goodput gains over CTP.

Current commodity RFID systems incur high communication overhead due to severe tag-to-tag collisions. Although some recent works have been proposed to support parallel decoding for concurrent tag transmissions, they require accurate channel measurements, tight tag synchronization, or modifications to standard RFID tag operations. In this paper, we present BiGroup, a novel RFID communication paradigm that allows the reader to decode the collision from multiple commodity-off-the-shelf (COTS) RFID tags in one communication round. In BiGroup, COTS tags can directly join ongoing communication sessions and get decoded in parallel. The collision resolution intelligence is solely put at the reader side. To this end, BiGroup examines the tag collisions at RFID physical layer from constellation domain as well as time domain, exploits the under-utilized channel capacity due to low tag transmission rate, and leverages tag diversities. We implement BiGroup with USRP N210 software radio that is able to read and decode multiple concurrent transmissions from COTS passive tags. Our experimental study gives encouraging results that BiGroup greatly improves RFID communication efficiency, i.e., 11 times performance improvement compared with the alternative decoding scheme for COTS tags.

We present Travi-Navi-a vision-guided navigation system that enables a self-motivated user to easily bootstrap and deploy indoor navigation services, without comprehensive indoor localization systems or even the availability of floor maps. Travi-Navi records high-quality images during the course of a guider's walk on the navigation paths, collects a rich set of sensor readings, and packs them into a navigation trace. The followers track the navigation trace, get prompt visual instructions and image tips, and receive alerts when they deviate from the correct paths. Travi-Navi also finds shortcuts whenever possible. In this paper, we describe the key techniques to solve several practical challenges, including robust tracking, shortcut identification, and high-quality image capture while walking. We implement Travi-Navi and conduct extensive experiments. The evaluation results show that Travi-Navi can track and navigate users with timely instructions, typically within a four-step offset, and detect deviation events within nine steps. We also characterize the power consumption of Travi-Navi on various mobile phones.

Screen-camera links for Visible Light Communication (VLC) are diverse, as the link quality varies according to many factors, such as ambient light and camera's performance. This paper presents SoftLight, a channel coding approach that considers the unique channel characteristics of VLC links and automatically adapts the transmission data rate to the link qualities of various scenarios. SoftLight incorporates two new ideas: (1) an expanded color modulation interface that provides soft hint about its confidence in each demodulated bit and establishes a bit-level VLC erasure channel, and (2) a rateless coding scheme that achieves bit-level rateless transmissions with low computation complexity and tolerates the false positive of bits provided by the soft hint enabled erasure channel. SoftLight is orthogonal to the visual coding schemes and can be applied atop any barcode layouts. We implement SoftLight on Android smartphones and evaluate its performance under a variety of environments. The experiment results show that SoftLight can correctly transmit a 22-KByte photo between two smartphones within 0.6 second and improves the average goodput of the state-of-the-art screen-camera VLC solution by 2.2.

Due to frequency selective fading, modern wideband 802.11 transmissions have unevenly distributed bit BERs in a packet. In this paper, we propose to unequally protect packet bits according to their BERs. By doing so, we can best match the effective transmission rate of each bit to channel condition, and improve throughput. The major design challenge lies in deriving an accurate relationship between the frequency selective channel condition and the decoded packet bit BERs, all the way through the complex 802.11 PHY layer. Based on our study, we find that the decoding error of a packet bit corresponds to dense errors in the underlying codeword bits, and the BER can be truthfully approximated by the codeword bit error density. With above observation, we propose UnPKT, scheme that protects packet bits using different MAC-layer FEC redundancies based on bit-wise BER estimation to augment wide-band 802.11 transmissions. UnPKT is software-implementable and compatible with the existing 802.11 architecture. Extensive evaluations based on Atheros 9580 NICs and GNU-Radio platforms show the effectiveness of our design. UnPKT can achieve a significant goodput improvement over state-of-the-art approaches.

This paper presents DopEnc, an acoustic-based encounter profiling system on smartphones. DopEnc can automatically identify the persons that users interact with in the context of encountering. DopEnc performs encounter profiling in two major steps: (1) Doppler profiling to detect that two persons approach and stop in front of each other via an effective trajectory, and (2) voice profiling to confirm that they are thereafter engaged in an interactive conversation. DopEnc is further extended to support parallel acoustic exploration of many users by incorporating a unique multiple access scheme within the limited inaudible acoustic frequency band. All implementation of DopEnc is based on commodity sensors like speakers, microphones and accelerometers integrated on commercial-off-the-shelf smartphones. We evaluate DopEnc with detailed experiments and a real use-case study of 11 participants. Overall DopEnc achieves an accuracy of 6.9% false positive and 9.7% false negative in real usage.

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.

This paper presents a transport traffic estimation method which leverages road network correlation and sparse traffic sampling via the compressive sensing technique. Through the investigation on a traffic data set of more than 4400 taxis from Shanghai city, China, we observe nontrivial traffic correlations among the traffic conditions of different road segments and derive a mathematical model to capture such relations. After mathematical manipulation, the models can be used to construct representation bases to sparsely represent the traffic conditions of all road segments in a road network. With the trait of sparse representation, we propose a traffic estimation approach that applies the compressive sensing technique to achieve a city-scale traffic estimation with only a small number of probe vehicles, largely reducing the system operating cost. To validate the traffic correlation model and estimation method, we do extensive trace-driven experiments with real-world traffic data. The results show that the model effectively reveals the hidden structure of traffic correlations. The proposed estimation method derives accurate traffic conditions with the average accuracy as 0.80, calculated as the ratio between the number of correct traffic state category estimations and the number of all estimation times, based on only 50 probe vehicles' intervention, which significantly outperforms the state-of-the-art methods in both cost and traffic estimation accuracy.

This paper presents a distanceless networking approach for wireless sensor networks sparsely deployed in large areas. By leveraging rateless codes, we provide distanceless transmission to expand the communication range of sensor motes and fully exploit network diversity. We address a variety of practical challenges to accommodate rateless coding on resource-constrained sensor motes and devise a communication protocol to efficiently coordinate the distanceless link transmissions. We propose a new metric (expected distanceless transmission time) for routing selection and further adapt the distanceless transmissions to low duty-cycled sensor networks. We implement the proposed scheme in TinyOS on the TinyNode platform and deploy the sensor network in a real-world project, in which 12 wind measurement sensors are installed around a large urban reservoir of 2.5 × 3.0 km2to monitor the field wind distribution. Extensive experiments show that our proposed scheme significantly outperforms the state-of-the-art approaches for data collection in sparse sensor networks.

Estimating the number of RFID tags is a fundamental operation in RFID systems and has recently attracted wide attentions. Despite the subtleties in their designs, previous methods estimate the tag cardinality from the slot measurements, which distinguish idle and busy slots and based on that derive the cardinality following some probability models. In order to fundamentally improve the counting efficiency, in this paper we introduce PLACE, a physical layer based cardinality estimator. We show that it is possible to extract more information and infer integer states from the same slots in RFID communications. We propose a joint estimator that optimally combines multiple sub-estimators, each of which independently counts the number of tags with different inferred PHY states. Extensive experiments based on the GNURadio/USRP platform and the large-scale simulations demonstrate that PLACE achieves approximately 3 ~ 4× performance improvement over state-of-the-art cardinality estimation approaches.

This paper presents CSPR, a compressive-sensing-based approach for path reconstruction in wireless sensor networks. By viewing the whole network as a path representation space, an arbitrary routing path can be represented by a path vector in the space. As path length is usually much smaller than the network size, such path vectors are sparse, i.e., the majority of elements are zeros. By encoding sparse path representation into packets, the path vector (and thus the represented routing path) can be recovered from a small amount of packets using compressive sensing technique. CSPR formalizes the sparse path representation and enables accurate and efficient per-packet path reconstruction. CSPR is invulnerable to network dynamics and lossy links due to its distinct design. A set of optimization techniques is further proposed to improve the design. We evaluate CSPR in both testbed-based experiments and large-scale trace-driven simulations. Evaluation results show that CSPR achieves high path recovery accuracy (i.e., 100% and 96% in experiments and simulations, respectively) and outperforms the state-of-the-art approaches in various network settings.

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.

Without the need of local energy supply, computational RFID (CRFID) sensors are emerging as important platforms enabling a variety of sensing and computing applications. Nevertheless, the data throughput of CRFIDs is very low. This paper aims at efficiently reading bulk data from CRFIDs using commodity RFID readers. We carry out thorough experiment studies to investigate the root cause of the low data throughput of CRFIDs. The experiment results suggest that the fundamental problem of data transfer stems from the mismatch between the stringent timing requirement of commodity standard and the limited packet handling capability of CRFIDs. We further propose several simple yet effective techniques to allow CRFIDs to meet stringent timing requirement of commodity RFID readers and achieve efficient data transfer. We implement a prototype system based on the WISP CRFIDs and commercial off-the-self RFID readers. We carry out extensive experiments on the prototype systems, which show that the proposed scheme works well with the commodity RFID readers.

Real-time urban traffic conditions are critical to wide populations in the city and serve the needs of many transportation dependent applications. This paper presents our experience of building a participatory urban traffic monitoring system that exploits the power of bus riders' mobile phones. The system takes lightweight sensor hints and collects minimum set of cellular data from the bus riders' mobile phones. Based on such a participatory sensing framework, the system turns buses into dummy probes, monitors their travel statuses, and derives the instant traffic map of the city. Unlike previous works that rely on intrusive detection or full cooperation from "probe vehicles", our approach resorts to the crowd-participation of ordinary bus riders, who are the information source providers and major consumers of the final traffic output. The experiment results demonstrate the feasibility of such an approach achieving fine-grained traffic estimation with modest sensing and computation overhead at the crowd.

Estimating the number of RFID tags is a fundamental operation in RFID systems and has recently attracted wide attentions. Despite the subtleties in their designs, previous methods estimate the tag cardinality from the slot measurements, which distinguish idle and busy slots and based on that derive the cardinality following some probability models. In order to fundamentally improve the counting efficiency, in this paper we introduce PLACE, a physical layer based cardinality estimator. We show that it is possible to extract more information and infer integer states from the same slots in RFID communications. We propose a joint estimator that optimally combines multiple sub-estimators, each of which independently counts the number of tags with different inferred PHY states. Extensive experiments based on the GNURadio/USRP platform and the large-scale simulations demonstrate that PLACE achieves approximately 3~4× performance improvement over state-of-the-art cardinality estimation approaches.

This paper presents Recitation, the first software system that uses lightweight channel state information (CSI) to accurately predict error-prone bit positions in a packet so that applications atop the wireless physical layer may take the best action during subsequent transmissions. Our key insight is that although Wi-Fi wireless physical layer operations are complex, they are deterministic. This enables us to rehearse physical-layer operations on packet bits before they are transmitted. Based on this rehearsal, we calculate a hidden parameter in the decoding process, called error event probability (EVP). EVP captures fine-grained information about the receiver's convolutional or LDPC decoder, allowing Recitation to derive precise information about the likely fate of every bit in subsequent packets, without any wireless channel training. Recitation is the first system of its kind that is both software-implementable and compatible with the existing 802.11 architecture for both SISO and MIMO settings. We experiment with commodity Atheros 9580 Wi-Fi NICs to demonstrate Recitation's utility with three representative applications in static, mobile, and interference-dominated scenarios. We show that Recitation achieves 33.8% and 16% average throughput gains for bit-rate adaptation and partial packet recovery, respectively, and 6 dB PSNR quality improvement for unequal error protection-based video.

Current commodity RFID systems incur high communication overhead due to severe tag-to-tag collisions. Although some recent works have been proposed to support parallel decoding for concurrent tag transmissions, they require accurate channel measurements, tight tag synchronization, or modifications to standard RFID tag operations. In this paper, we present BiGroup, a novel RFID communication paradigm that allows the reader to decode the collision from multiple COTS (commodity-off-the-shelf) RFID tags in one communication round. In BiGroup, COTS tags can directly join ongoing communication sessions and get decoded in parallel. The collision resolution intelligence is solely put at the reader side. To this end, BiGroup examines the tag collisions at RFID physical layer from constellation domain as well as time domain, exploits the under-utilized channel capacity due to low tag transmission rate, and leverages tag diversities. We implement BiGroup with USRP N210 software radio that is able to read and decode multiple concurrent transmissions from COTS passive tags. Our experimental study gives encouraging results that BiGroup greatly improves RFID communication efficiency, i.e., 11× performance improvement compared to the alternative decoding scheme for COTS tags and 6× gain in time efficiency when applied to EPC C1G2 tag identification.

Power delay profiles characterize multipath channel features, which are widely used in motion- or localization-based applications. Recent studies show that the power delay profile may be derived from the CSI traces collected from commodity WiFi devices, but the performance is limited by two dominating factors. The resolution of the derived power delay profile is determined by the channel bandwidth, which is however limited on commodity WiFi. The collected CSI reflects the signal distortions due to both the channel attenuation and the hardware imperfection. A direct derivation of power delay profiles using raw CSI measures, as has been done in the literature, results in significant inaccuracy. In this paper, we present Splicer, a software-based system that derives high-resolution power delay profiles by splicing the CSI measurements from multiple WiFi frequency bands. We propose a set of key techniques to separate the mixed hardware errors from the collected CSI measurements. Splicer adapts its computations within stringent channel coherence time and thus can perform well in presence of mobility. Our experiments with commodity WiFi NICs show that Splicer substantially improves the accuracy in profiling multipath characteristics, reducing the errors of multipath distance estimation to be less than $2m$. Splicer can immediately benefit upper-layer applications. Our case study with recent single-AP localization achieves a median localization error of $0.95m$.

This paper presents a hopless networking paradigm. Incorporating recent techniques of rateless codes, senders break packets into rateless information streams and each single stream automatically adapts to diverse channel qualities at all potential receivers, regardless of their hop distances. The receivers are capable of accumulating rateless information pieces from different senders and jointly decoding the packet, largely improving throughput. We develop a practical protocol, called HOPE, which instantiates the hopless networking paradigm. Compared with the existing opportunistic routing protocol family, HOPE best exploits the wireless channel diversity and takes full advantage of the wireless broadcast effect. HOPE incurs minimum protocol overhead and serves general networking applications. We extensively evaluate the performance of HOPE with indoor network traces collected from USRP N210s and Intel 5300 NICs. The results show that HOPE achieves 1.7x and 1.3x goodput gain over ExOR and MIXIT, respectively.

This paper presents Pando, a completely contention-free data dissemination protocol for wireless sensor networks. Pando encodes data by Fountain codes and disseminates the rateless stream of encoded packets along the fast and parallel pipelines built on constructive interference and channel diversity. Since every encoded packet contains innovative information to the original data object, Pando avoids duplicate retransmissions and fully exploits the wireless broadcast effect in data dissemination. To transform Pando into a practical system, we devise several techniques, including the integration of Fountain coding with the timing-critical operations of constructive interference and pipelining, a silence based feedback scheme for the one-way pipelined dissemination, and packet-level adaptation of network density and channel diversity. Based on these techniques, Pando can accomplish the data dissemination process entirely over the fast and parallel pipelines. We implement Pando in Contiki and for TelosB sensor motes. We evaluate Pando's performance with various settings on two large-scale open testbeds, Indriya and Flocklab. Our experimental results show that Pando can provide 100% reliability and reduce the dissemination time of the state-of-the-art by 3.5.

Radio frequency identification (RFID) systems are emerging platforms that support a variety of pervasive applications. RFID tags can be used to label items and enable item-level monitoring. The problem of identifying missing tags in RFID systems has attracted wide attention due to its practical importance (e.g., anti-theft). This paper presents P-MTI: a Physical-layer Missing Tag Identification scheme that effectively makes use of the lower-layer information and dramatically improves operational efficiency. Unlike conventional approaches, P-MTI looks into the aggregated tag responses instead of focusing on individual tag responses and extracts useful information from physical-layer collisions. P-MTI leverages the sparsity of missing tag events and reconstructs tag responses through compressive sensing. We prototype P-MTI using the USRP software defined radio and Intel WISP platform. We also evaluate the performance of P-MTI with extensive simulations and compare to previous approaches. The experiment results show the promising performance of P-MTI in identification accuracy, time efficiency, as well as robustness over noisy channels.

We study the water quality in an urban district, where the surface wind distribution is an essential input but undergoes high spatial and temporal variations due to the impact of surrounding buildings. In this work, we develop an optimal sensor placement scheme to measure the wind distribution over a large urban reservoir using a limited number of wind sensors. Unlike existing solutions that assume Gaussian process of target phenomena, this study measures the wind that inherently exhibits strong non-Gaussian yearly distribution. By leveraging the local monsoon characteristics of wind, we segment a year into different monsoon seasons that follow a unique distribution respectively. We also use computational fluid dynamics to learn the spatial correlation of wind. The output of sensor placement is a set of the most informative locations to deploy the wind sensors, based on the readings of which we can accurately predict the wind over the entire reservoir in real time. Ten wind sensors are deployed. The in-field measurement results of more than 3 months suggest that the proposed sensor placement and spatial prediction scheme provides accurate wind measurement that outperforms the state-of-the-art Gaussian model based on interpolation-based approaches.

The location and context switching, especially the indoor/outdoor switching, provides essential and primitive information for upper-layer mobile applications. In this article, we present IODetector: a lightweight sensing service that runs on the mobile phone and detects the indoor/outdoor environment in a fast, accurate, and efficient manner. Constrained by the energy budget, IODetector primarily leverages lightweight sensing resources, such as light sensors, magnetism sensors, and cell tower signals. For universal applicability, IODetector assumes no prior knowledge (e.g., fingerprints) of the environment and uses only on-board sensors common to mainstream mobile phones. Being a generic and lightweight service component, IODetector greatly benefits many location-based and context-aware applications. We prototype the IODetector on Android mobile phones and evaluate the system comprehensively with data collected from 34 traces that include 133 different places during a 6-week period, employing different phone models. We further perform a case study where we make use of IODetector to instantly infer the GPS availability and localization accuracy in different indoor/outdoor environments.

Time synchronization is one of the most fundamental services for wireless sensor networks. Prior studies have investigated the clock stability due to environmental dynamics. In this paper, we demonstrate by experiment that in spite of the surrounding environment, time synchronization is unavoidably impacted by in-network energy heterogeneity, which may incur up to 30-40 ppm clock uncertainty. We mathematically analyze the root cause of such clock uncertainty and propose a protocol called EATS. Sensor nodes with EATS can intelligently select the best synchronization parents that minimize the negative impact of the energy heterogeneity. The selection is robust to multiple impacting factors in the network and provides fine-grained synchronization accuracy. In addition, nodes can make use of local energy information and further calibrate the clocks. In light of this, the logic time maintained among different nodes is more consistent and the synchronization can be performed with a longer re-synchronization interval and less energy consumption. We implement EATS with TelosB motes and evaluate the effectiveness and efficiency of our design through extensive experiments and simulations.

In this paper, we present a comprehensive and in-depth study on convergence preservation and avoidance in Operational Transformation (OT) systems. In this study, we discovered basic conditions, transformation patterns, and mechanisms for avoiding Convergence Property 2 (CP2), and established CP2-avoidance correctness of seven major OT systems. Furthermore, we proposed improvements to existing systems and designed a new OT system capable of avoiding CP2 with a unique combination of novel features. These results contribute significantly to the advancement of OT and collaboration-enabling technology.

This paper presents our implementation experience in building an energy efficient HVAC system for cooling and air conditioning. The system exercises the "low exergy" theory and leverages high temperature water (18°C) cooling for better energy efficiency. In order to achieve this, the system decomposes the cooling and dehumidification functionalities, and employs decentralized air control for on-demand dehumidification and ventilation. The system comprises two control modules, namely, radiant cooling module and distributed ventilation module, cooperating with each other to provide the HVAC control. Abundant sensors and embedded control devices are customized and instrumented, and we develop a wireless sensor network to support control data exchange among those devices. Our experimental evaluation demonstrates that the system achieves accurate control targets and promptly responses to environment dynamics. The wireless sensor network effectively supports the system needs with long system lifespan. Compared with traditional HVAC systems, our system is of much higher energy efficiency, as measured by the standard Coefficient of Performance (COP) metric.

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.

802.11 WLANs suffer from high packet losses due to interference and noise. Packet retransmission is a fundamental way to recover a lost packet. To extract useful information from incorrect symbols and improve retransmission efficiency, we present MISC, a packet retransmission scheme that merges incorrect symbols from multiple transmissions to produce correct ones. MISC proactively creates constellation diversity by rearranging the constellation maps in retransmissions. MISC addresses practical implementation issues and makes minimum amendments to integrate into current 802.11 WLAN framework. We implement MISC in an 802.11-based GNURadio/USRP platform and conduct extensive experiments to evaluate its efficacy. Experiment results demonstrate that MISC can substantially improve the throughput.

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.

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 (~ 10cm) can be precisely perceived, and the accuracy is up to 92.34% while the false positive is suppressed under 0.5%.

Without the need of local energy supply, computational RFID (CRFID) sensors are emerging as important platforms enabling a variety of sensing and computing applications. Nevertheless, the data throughput of CRFIDs is very low. This paper aims at efficiently transferring bulk data from CRFIDs to commodity RFID readers. We first investigate the problem of low data throughput of CRFIDs. We then propose several simple yet effective techniques to allow CRFIDs to meet stringent timing requirement of commodity RFID readers and achieve efficient data transfer. We implement a prototype system based on the WISP CRFIDs and commercial off-the-self RFID reader. The experiment results show that our approach provides better compatibility with EPCglobal C1G2 compliant RFID devices and works perfectly with the commodity RFID readers.

We consider the problem of allocating two network resources (wireless bandwidth and backhaul router buffer) to support fair QoS in WLAN. We transform the QoS requirements to multi-resource demands and apply the DRF scheme to achieve fair allocation of the two different types of resources. We define a QoS utility function which measures the user QoS satisfaction. We briefly prove the property of resource-based strategy proofness of DRF, the corresponding mathematical expression is used to prove QoS-based strategy-proofness in terms of both resource share and user QoS. Finally our simulation results confirm the properties of DRF and demonstrate that our DRF-based approach performs better than several multi-resource allocation schemes with respect to both fairness and utility.

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.

We present Travi-Navi - a vision-guided navigation system that enables a self-motivated user to easily bootstrap and deploy indoor navigation services, without comprehensive indoor localization systems or even the availability of floor maps. Travi-Navi records high quality images during the course of a guider's walk on the navigation paths, collects a rich set of sensor readings, and packs them into a navigation trace. The followers track the navigation trace, get prompt visual instructions and image tips, and receive alerts when they deviate from the correct paths. Travi-Navi also finds the most efficient shortcuts whenever possible. We encounter and solve several challenges, including robust tracking, shortcut identification, and high quality image capture while walking. We implement Travi-Navi and conduct extensive experiments. The evaluation results show that Travi-Navi can track and navigate users with timely instructions, typically within a 4-step offset, and detect deviation events within 9 steps.

The phone attitude is an essential input to many smartphone applications. Based on in-depth understanding of the nature of the MEMS gyroscope and other IMU sensors, we propose A3 - an accurate and automatic attitude detector for commodity smartphones. In the demo, we show the performance of our attitude tracking algorithm and its usability in attitude-based mobile applications.

The phone attitude is an essential input to many smartphone applications, which has been known very difficult to accurately estimate especially over long time. Based on in-depth understanding of the nature of the MEMS gyroscope and other IMU sensors commonly equipped on smartphones, we propose A3 - an accurate and automatic attitude detector for commodity smartphones. A3 primarily leverages the gyroscope, but intelligently incorporates the accelerometer and magnetometer to select the best sensing capabilities and derive the most accurate attitude estimation. Extensive experimental evaluation on various types of Android smartphones confirms the outstanding performance of A3. Compared with other existing solutions, A3 provides 3x improvement on the accuracy of attitude estimation.

This paper presents CSPR, a compressive sensing based approach for path reconstruction in wireless sensor networks. By viewing the whole network as a path representation space, an arbitrary routing path can be represented by a path vector in the space. As path length is usually much smaller than the network size, such path vectors are sparse, i.e., the majority of elements are zeros. By encoding sparse path representation into packets, the path vector (and thus the represented path) can be recovered from a small amount of packets using compressive sensing technique. CSPR formalizes the sparse path representation and enables accurate and efficient per-packet path reconstruction. CSPR is invulnerable to network dynamics and lossy links due to its distinct design. A set of optimization techniques are further proposed to improve the design. We evaluate CSPR in both testbed-based experiments and large-scale trace-driven simulations. Evaluation results show that CSPR achieves high path recovery accuracy (i.e., 100% and 96% in experiments and simulations, respectively), and outperforms the state-of-the-art approaches in various network settings.

Water quality monitoring and prediction are critical for ensuring the sustainability of water resources which are essential for social security, especially for countries with limited land like Singapore. For example, the Singapore government identified water as a new growth sector and committed in 2006 to invest S$ 330 million over the following five years for water research and development [1]. To investigate the water quality evolution numerically, some key water quality parameters at several discrete locations in the reservoir (e.g., dissolved oxygen, chlorophyll, and temperature) and some environmental parameters (e.g., the wind distribution above water surface, air temperature and precipitation) are used as inputs to a three-dimensional hydrodynamics-ecological model, Estuary Lake and Coastal Ocean Model — Computational Aquatic Ecosystem Dynamics Model (ELCOM-CAEDYM) [2]. Based on the calculation in the model, we can obtain the distribution of water quality in the whole reservoir. We can also study the effect of different environmental parameters on the water quality evolution, and finally predict the water quality of the reservoir with a time step of 30 seconds. In this demo, we introduce our data collection system which enables water quality studies with real-time sensor data.

This paper presents a distanceless networking approach for wireless sensor networks sparsely deployed in large areas. By leveraging rateless codes, we provide distanceless transmission to expand the communication range of sensor motes and fully exploit network diversity. We address a variety of practical challenges to accommodate rateless coding on resource-constrained sensor motes and devise a communication protocol to efficiently coordinate the distanceless link transmissions. We propose a new metric (expected distanceless transmission time) for routing selection and further adapt the distanceless transmissions to low duty-cycled sensor networks. We implement the proposed scheme in TinyOS on the TinyNode platform and deploy the sensor network in a real-world project, in which 12 wind measurement sensors are installed around a large urban reservoir of 2.5km * 3.0km to monitor the field wind distribution. Extensive experiments show that our proposed scheme significantly outperforms the state-of-the-art approaches for data collection in sparse sensor networks.

This demo presents a distanceless networking approach for wireless sensor networks sparsely deployed in large areas. We implement the proposed scheme and deploy the sensor network in a large urban reservoir of 2.5km * 3.0km to monitor the field wind distribution. We show the in-field deployment procedure of the wind field measurement system and demonstrate the performance of the data collection protocol by a small testbed on site.

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.

The bus arrival time is primary information to most city transport travelers. Excessively long waiting time at bus stops often discourages the travelers and makes them reluctant to take buses. In this paper, we present a bus arrival time prediction system based on bus passengers' participatory sensing. With commodity mobile phones, the bus passengers' surrounding environmental context is effectively collected and utilized to estimate the bus traveling routes and predict bus arrival time at various bus stops. The proposed system solely relies on the collaborative effort of the participating users and is independent from the bus operating companies, so it can be easily adopted to support universal bus service systems without requesting support from particular bus operating companies. Instead of referring to GPS-enabled location information, we resort to more generally available and energy efficient sensing resources, including cell tower signals, movement statuses, audio recordings, etc., which bring less burden to the participatory party and encourage their participation. We develop a prototype system with different types of Android-based mobile phones and comprehensively experiment with the NTU campus shuttle buses as well as Singapore public buses over a 7-week period. The evaluation results suggest that the proposed system achieves outstanding prediction accuracy compared with those bus operator initiated and GPS supported solutions. We further adopt our system and conduct quick trial experiments with London bus system for 4 days, which suggests the easy deployment of our system and promising system performance across cities. At the same time, the proposed solution is more generally available and energy friendly.

Radio frequency identification (RFID) cardinality estimation with an accuracy guarantee is of practical importance in various large-scale RFID applications. This paper proposes a fast RFID cardinality estimation protocol, named Zero-One Estimator (ZOE). ZOE only requires 1-bit response from the RFID tags per estimation round. More importantly, ZOE rapidly converges to optimal parameter configurations and achieves higher estimation efficiency compared to existing protocols. ZOE guarantees arbitrary accuracy requirement without imposing heavy computation and memory overhead at RFID tags except the routine operations of C1G2 standard. ZOE also provides reliable cardinality estimation with unreliable channels due to the robust protocol design. We prototype ZOE using the USRP software defined radio and the Intel WISP tags. We extensively evaluate the performance of ZOE compared to existing protocols, which demonstrates encouraging results in terms of estimation accuracy, time efficiency, as well as robustness over a large range of tag population.

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.

In many sensor network applications, multiple data forwarding tasks usually exist with different source-destination node pairs. Due to limitations of the duty-cycling operation and interference, however, not all tasks can be guaranteed to be scheduled within their required delay constraints. We investigate a fundamental scheduling problem of both theoretical and practical importance, called multi-task schedulability problem, i.e., given multiple data forwarding tasks, to determine the maximum number of tasks that can be scheduled within their deadlines and work out such a schedule. We formulate the multi-task schedulability problem, prove its NP-Hardness, and propose an approximate algorithm with analysis on the performance bound and complicity. We further extend the proposed algorithm by explicitly altering duty cycles of certain sensor nodes so as to fully support applications with stringent delay requirements to accomplish all tasks. We then design a practical scheduling protocol based on proposed algorithms. We conduct extensive trace-driven simulations to validate the effectiveness and efficiency of our approach with various settings.

Influence Maximization aims to find the top-$(K)$ influential individuals to maximize the influence spread within a social network, which remains an important yet challenging problem. Proven to be NP-hard, the influence maximization problem attracts tremendous studies. Though there exist basic greedy algorithms which may provide good approximation to optimal result, they mainly suffer from low computational efficiency and excessively long execution time, limiting the application to large-scale social networks. In this paper, we present IMGPU, a novel framework to accelerate the influence maximization by leveraging the parallel processing capability of graphics processing unit (GPU). We first improve the existing greedy algorithms and design a bottom-up traversal algorithm with GPU implementation, which contains inherent parallelism. To best fit the proposed influence maximization algorithm with the GPU architecture, we further develop an adaptive K-level combination method to maximize the parallelism and reorganize the influence graph to minimize the potential divergence. We carry out comprehensive experiments with both real-world and sythetic social network traces and demonstrate that with IMGPU framework, we are able to outperform the state-of-the-art influence maximization algorithm up to a factor of 60, and show potential to scale up to extraordinarily large-scale networks.

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.

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.

Based on plenty of sensors on smartphones and tablets, their location information becomes more accurate and easy to access. Many position-based applications have thus been developed. As these mobile devices have become the main access approach of 802.11-based wireless local area networks (WLAN), location information also provides opportunities to improve the performance of underlying wireless communication. This paper presents CO-MAP (Co-Occurrence MAP), which leverages location information to handle exposed and hidden terminal problems, which are still a major cause of throughput degradation in mobile WLANs. With the positions of nodes, CO-MAP rapidly builds a co-occurrence map showing which two links can occur concurrently. Meanwhile, to avoid potential collisions, it selects the best settings of frame transmissions based on a novel analytic network model when hidden terminals are distinguished. CO-MAP improves the goodputs of both downlinks and uplinks in an instant and distributed manner. Our implementation on a hardware testbed demonstrates that CO-MAP can accurately detect potential interferers, and provide significant gain of goodput for both exposed and hidden terminal scenarios. The simulation results also suggest that imperfect position hints can still bring substantial improvement on the multiple access efficiency.

Benefiting from the abundant sensor hints of current mobile devices, their location information has become pervasively available and easy to access. As smartphones and tablets have become recently the main access devices of 802.11-based WLAN, location information provides large opportunity to improve the performance of underlying wireless communication. This paper presents CO-MAP (Co-Occurrence MAP) which leverages the position of devices to handle exposed and hidden terminal problems in mobile WLANs so as to improve the multiple access efficiency. With the location information, CO-MAP rapidly builds a co-occurrence map showing which two links can occur concurrently. Meanwhile, it selects the best settings of frame transmissions based on a novel analytical network model when hidden terminals are distinguished. CO-MAP improves the goodputs of both downlinks and uplinks in instant and distributed manner. Our implementation on a testbed of 6 laptops demonstrates that CO-MAP can accurately detect potential hidden and exposed interferers, and provide a large gain of goodput for both exposed terminal and hidden terminal scenarios. The simulation results of large scale networks on NS-2 also suggest that imperfect position hints can still bring substantial improvement on the multiple access efficiency.

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.

Estimating the RFID cardinality with accuracy guarantee is an important task in large-scale RFID systems. This paper proposes a fast RFID cardinality estimation scheme. The proposed Zero-One Estimator (ZOE) protocol rapidly converges to optimal parameter settings and achieves high estimation efficiency. ZOE significantly improves the cardinality estimation efficiency, achieving 3x performance gain compared with existing protocols. Meanwhile, ZOE guarantees arbitrary accuracy requirement without imposing computation and memory overhead at RFID tags. Due to the simplicity and robustness, the ZOE protocol provides reliable cardinality estimation even over noisy channel. We implement a prototype system using the USRP software defined radio and Intel WISP RFID tags. We also evaluate the performance of ZOE with extensive simulations. The evaluation of ZOE shows encouraging results in terms of estimation accuracy, time efficiency, as well as robustness.

RFID systems are emerging platforms that support a variety of pervasive applications. The problem of identifying missing tag in RFID systems has attracted wide attention due to its practical importance. This paper presents P-MTI: a Physical-layer Missing Tag Identification scheme which effectively makes use of the lower layer information and dramatically improves operational efficiency. Unlike conventional approaches, P-MTI looks into the aggregated responses instead of focusing on individual tag responses and extracts useful information from physical layer symbols. P-MTI leverages the sparsity of missing tag events and reconstructs tag responses through compressive sensing. We implement P-MTI and prototype the system based on the USRP software defined radio and Intel WISP platform which demonstrates the efficacy. We also evaluate the performance of P-MTI with extensive simulations and compare with previous approaches under various scenarios. The evaluation shows promising results of P-MTI in terms of identification accuracy, time efficiency, as well as robustness over noisy channels.

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.

Background. Real time urban traffic information is critical to wide populations living in the city but undergoes significant spatial and temporal variation especially in the urban areas and calls for well monitoring. For the past decades, increasing efforts have been put into exploring an accurate, efficient, and inexpensive way to instantly monitor the urban traffic conditions. Conventional methods rely on intrusive sensing, where people deploy infrastructural devices like loop detectors or traffic cameras at roadsides to actively detect traffic references. Installing such intrusive devices incurs substantial deployment and maintenance costs and can only provide limited observation at sparse positions. Recent studies resort to the GPS traces collected from "probe vehicles" like taxis or private cars to estimate the road traffic conditions [2, 5]. Such a passive probing method avoids cumbrous infrastructure deployment and enjoys flexible information extraction from the running probes in the city. Nevertheless, most of them largely rely on full cooperation from the probe vehicles and bear substantial cost in obtaining their location references.

The wireless sensor network (WSN) technology spawns a surge of unforeseen applications. The diversity of these emerging applications represents the great success of this technology. A fundamental performance benchmark of such applications is topology control, which characterizes how well a sensing field is monitored and how well each pair of sensors is mutually connected in WSNs. This paper provides an overview of topology control techniques. We classify existing topology control techniques into two categories: network coverage and network connectivity. For each category, a surge of existing protocols and techniques are presented with the focus on blanket coverage, barrier coverage, sweep coverage, power management, and power control, five rising aspects that attract significant research attention in recent years. In this survey, we emphasize the basic principles of topology control to understand the state of the arts, while we explore future research directions in the new open areas and propose a series of design guidelines under this topic.

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.

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.

In this paper, we study the set reconciliation problem, in which each member of a node pair has a set of objects and seeks to deliver its unique objects to the other member. How could each node compute the set difference, however, is challenging in the set reconciliation problem. To address such an issue, we propose a lightweight but efficient method that only requires the pair of nodes to represent objects using a counting Bloom filter (CBF) of size $(O(d))$ and exchange with each other, where $(d)$ denotes the total size of the set differences. A receiving node then subtracts the received CBF from its local one via minus operation proposed in this paper. The resultant CBF can approximately represent the union of the set differences and thus the set difference to each node can be identified after querying the resultant CBF. In this paper, we propose a novel estimator through which each node can accurately estimate not only the value of $(d)$ but also the size of the set difference to each node. Such an estimation result can be used to optimize the parameter setting of the CBF to achieve less false positives and false negatives. Comprehensive analysis and evaluation demonstrates that our method is more efficient than prior BF-based methods in terms of achieving the same accuracy with less communication cost. Moreover, our reconciliating method needs no prior context logs and it is very useful in networking and distributed applications.

Fast searching a particular subset in a large number of products attached with radio frequency identification (RFID) tags is of practical importance for a variety of applications, but not yet thoroughly investigated. Since the cardinality of the products can be extremely large, collecting the tag information directly from each of those tags could be highly inefficient. To address the tag searching efficiency in large-scale RFID systems, this paper proposes several algorithms to meet the stringent delay requirement in developing fast tag searching protocols. We formally formulate the tag searching problem in large-scale RFID systems. We propose utilizing compact approximators to efficiently aggregate a large volume of RFID tag information and exchange such information with a two-phase approximation protocol. By estimating the intersection of two compact approximators, the proposed two-phase compact approximator-based tag searching protocol significantly reduces the searching time compared to all possible solutions we can directly borrow from existing studies. We further introduce a scalable cardinality range estimation method that provides inexpensive input for our tag searching protocol. We conduct comprehensive simulations to validate our design. The results demonstrate that the proposed tag searching protocol is highly efficient in terms of both time efficiency and transmission overhead, leading to good applicability and scalability for large-scale RFID systems.

This article presents a scalable algorithm for managing property information about moving objects tracked by a sensor network. Property information is obtained via distributed sensor observations, but will be corrupted when objects mix up with each other. The association between properties and objects then becomes ambiguous. We build a novel representation framework, exploiting an overcomplete Radon basis dictionary to model property uncertainty in such circumstances. By making use of the combinatorial structure of the basis design and sparse representations we can efficiently approximate the underlying probability distribution of the association between target properties and tracks, overcoming the exponential space that would otherwise be required. Based on the proposed theories, we design a fully distributed algorithm on wireless sensor networks. We conduct comparative simulations and the results validate the effectiveness of our approach.

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.

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.

Radio Frequency Identification (RFID) technique is gaining increasing popularity in supply chain for the product management. By attaching a tag to each product, a reader can employ an authentication protocol to interrogate the tag's information for verification, which facilitates the automatic processing and monitoring of products in many applications. However, most current solutions cannot be directly used as they cannot balance the tradeoff between the privacy and efficiency for individual parties. In this paper, we design a bidirectional efficiency-privacy transferable (BEST) authentication protocol to address this issue. In a relatively secure domain, BEST works in an efficient manner to authenticate batches of tags with less privacy guarantee. Once the tags flow into open environment, BEST can migrate to provide stronger privacy protection to the tags with moderate efficiency degradation. The analytic result shows that BEST can well adapt to the RFID-enabled supply chain.

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.

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.

The bus arrival time is primary information to most city transport travelers. Excessively long waiting time at bus stops often discourages the travelers and makes them reluctant to take buses. In this paper, we present a bus arrival time prediction system based on bus passengers' participatory sensing. With commodity mobile phones, the bus passengers' surrounding environmental context is effectively collected and utilized to estimate the bus traveling routes and predict bus arrival time at various bus stops. The proposed system solely relies on the collaborative effort of the participating users and is independent from the bus operating companies, so it can be easily adopted to support universal bus service systems without requesting support from particular bus operating companies. Instead of referring to GPS enabled location information, we resolve to more generally available and energy efficient sensing resources, including cell tower signals, movement statuses, audio recordings, etc., which bring less burden to the participatory party and encourage their participation. We develop a prototype system with different types of Android based mobile phones and comprehensively experiment over a 7 week period. The evaluation results suggest that the proposed system achieves outstanding prediction accuracy compared with those bus company initiated and GPS supported solutions. At the same time, the proposed solution is more generally available and energy friendly.

No abstract available.

The location and context switching, especially the indoor/outdoor switching, provides essential and primitive information for upper layer mobile applications. In this paper, we present IODetector: a lightweight sensing service which runs on the mobile phone and detects the indoor/outdoor environment in a fast, accurate, and efficient manner. Constrained by the energy budget, IODetector leverages primarily lightweight sensing resources including light sensors, magnetism sensors, celltower signals, etc. For universal applicability, IODetector assumes no prior knowledge (e.g., fingerprints) of the environment and uses only on-board sensors common to mainstream mobile phones. Being a generic and lightweight service component, IODetector greatly benefits many location-based and context-aware applications. We prototype the IODetector on Android mobile phones and evaluate the system comprehensively with data collected from 19 traces which include 84 different places during one month period, employing different phone models. We further perform a case study where we make use of IODetector to instantly infer the GPS availability and localization accuracy in different indoor/outdoor environments.

A generic and lightweight service for indoor and outdoor detection is demonstrated that mainly uses three lightweight sensing resources, including light sensors, cell tower signals and magnetism sensors, to make ambient environment detection in a fast, accurate and efficient manner. In particular, we do not need to fingerprint the environment to acquire a priori knowledge. Thus the proposed system greatly benefits many location-based and context-ware applications.

Estimating the number of RFID tags in the region of interest is an important task in many RFID applications. In this paper, we propose a novel approach for efficiently estimating the approximate number of RFID tags. Compared with existing approaches, the proposed Probabilistic Estimating Tree (PET) protocol achieves {\cal O}(\log \log n) estimation efficiency, which remarkably reduces the estimation time while meeting the accuracy requirement. PET also largely reduces the computation and memory overhead at RFID tags. As a result, we are able to apply PET with passive RFID tags and provide scalable and inexpensive solutions for large-scale RFID systems. We validate the efficacy and effectiveness of PET through theoretical analysis as well as extensive simulations. Our results suggest that PET outperforms existing approaches in terms of estimation accuracy, efficiency, and overhead.

Demands on better interacting with physical world require an effective and efficient collaboration mechanism of multiple heterogeneous sensor networks. Previous works mainly focus on each single and specific sensor network, thus failing to address issues in the newly emerging scenario. In this paper, we propose COSE, a query-centric framework of collaborative heterogeneous sensor networks, where sensor networks collaborate with each other for effective and efficient processing of queries. Finding an optimal strategy of query processing with respect to energy efficiency is a crucial issue in COSE, which we formulate into an optimization problem, called EE-QPS. We prove the NP-hardness of EE-QPS, and then design a heuristic approach named IAP by utilizing the correlation (called implication in this paper) among different sensor networks. The experimental results demonstrate that in the context of COSE, IAP achieves optimized energy efficiency under various settings.

We demonstrate that the network flux over the sensor network provides fingerprint information about the mobile users within the field. Such information is exoteric in the physical space and easy to access through passive sniffing. We present a theoretical model to abstract the network flux according to the statuses of mobile users. We fit the theoretical model with the network flux measurements through Nonlinear Least Squares (NLS) and develop an algorithm that iteratively approaches the NLS solution by Sequential Monte Carlo Estimation. With sparse measurements of the flux information at individual sensor nodes, we show that it is easy to identify the mobile users within the network and instantly track their movements without breaking into the details of the communicational packets. Our study indicates that most of existing systems are vulnerable to such attack against the privacy of mobile users. We further propose a set of countermeasures that redistribute and reshape the network traffic to preserve the location privacy of mobile users. With a trace driven simulation, we demonstrate the substantial threats of the attacks and the effectiveness of the proposed countermeasures.

Estimating the number of RFID tags in the region of interest is an important task in many RFID applications. In this paper we propose a novel approach for efficiently estimating the approximate number of RFID tags. Compared with existing approaches, the proposed Probabilistic Estimating Tree (PET) protocol achieves O(loglogn) estimation efficiency, which remarkably reduces the estimation time while meeting the accuracy requirement. PET also largely reduces the computation and memory overhead at RFID tags. As a result, we are able to apply PET with passive RFID tags and provide scalable and inexpensive solutions for large-scale RFID systems. We validate the efficacy and effectiveness of PET through theoretical analysis as well as extensive simulations. Our results suggest that PET outperforms existing approaches in terms of estimation accuracy, efficiency, and overhead.

Fast searching a particular subset in a large number of products attached with RFID tags is of practical importance for a variety of applications but not yet thoroughly investigated. Since the cardinality of the products can be extremely large, collecting the tag information directly from each of those tags could be highly inefficient. To address the tag searching efficiency in large-scale RFID systems, this paper proposes several algorithms to meet the stringent delay requirement in developing fast tag searching protocols. We formally formulate the tag searching problem in large-scale RFID systems. We propose utilizing compact approximators to efficiently aggregate a large volume of RFID tag information and exchange such information with a two-phase approximation protocol. By estimating the intersection of two compact approximators, the proposed two-phase compact approximator based tag searching protocol significantly reduces the searching time compared with all possible solutions we can directly borrow from existing studies. We further introduce a scalable cardinality range estimation method which provides inexpensive input for our tag searching protocol. We conduct comprehensive simulations to validate our design. The results demonstrate that the proposed tag searching protocol is highly efficient in terms of both time-efficiency and transmission overhead, leading to good applicability and scalability for large-scale RFID systems.

In low-duty-cycle networks, sensors stay dormant most of time to save their energy and wake up based on their needs. Such a technique, while prolonging the network lifetime, sets excessive challenges for efficient flooding within the network. Tailored for obtaining short delay in low-duty-cycle networks, recently proposed flooding protocols have achieved some initial success. Many fundamental problems of flooding in low-duty-cycle networks, however, are still not well understood. In this paper, we thoroughly investigate how the flooding behaviors are fundamentally affected from theory to practice in a low-duty-cycle sensor network. We study how practical factors like duty cycle length and link loss affect the flooding delay. We mathematically quantify the performance deterioration caused by those factors and present initial learning in achieving efficient flooding against them. Our theoretical analysis brings us not only an in-depth understanding of several fundamental trade-offs in low-duty-cycle sensor networks, but also insights on the design of flooding protocols that can approach excellent performance.

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 local 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.

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 optimal and thus better adapt to the wild environment when building a large scale sensor network.

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 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 test-bed 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.

For energy conservation, a wireless sensor network is usually designed to work in a low-duty-cycle mode, in which a sensor node keeps active for a small percentage of time during its working period. In applications where there are multiple data delivery tasks with high data rates and time constraints, low-duty-cycle working mode may cause severe transmission congestion and data loss. In order to alleviate congestion and reduce data loss, the tasks need to be carefully scheduled to balance the workloads among the sensor nodes in both spatial and temporal dimensions. This paper studies the load balancing problem, and proves it is NP-Complete in general network graphs. Two efficient scheduling algorithms to achieve load balance are proposed and analyzed. Furthermore, a task scheduling protocol is designed relying on the proposed algorithms. To the best of our knowledge, this paper is the first one to tackle multiple task scheduling for low-duty-cycled sensor networks. The simulation results show that the proposed algorithms greatly improve the network performance in most scenarios.

We study the rendezvous data collection problem for the mobile sink in wireless sensor networks. We introduce to jointly optimize trajectory planning for the mobile sink and workload balancing for the network. By doing so, the mobile sink is able to efficiently collect network-wide data within a given delay bound and the network can eliminate the energy bottleneck to dramatically prolong its lifetime. Such a joint optimization problem is shown to be NP-hard and we propose an approximation algorithm, named RPS-LB, to approach the optimal solution. In RPS-LB, according to observed properties of the median reference structure in the network, a series of Rendezvous Points (RPs) are selected to construct the trajectory for the mobile sink and the derived approximation ratio of RPSLB guarantees that the formed trajectory is comparable with the optimal solution. The workload allocated to each RP is proven to be balanced mathematically. We then relax the assumption that mobile sink knows the location of each sensor node and present a localized, fully distributed version, RPS-LB-D, which largely improves the system applicability in practice. We verify the effectiveness of our proposals via extensive experiments.

Many efforts have been made for addressing coverage problems in sensor networks. They fall into two categories, full coverage and barrier coverage, featured as static coverage. In this work, we study a new coverage scenario, sweep coverage, which differs with the previous static coverage. In sweep coverage, we only need to monitor certain points of interest (POIs) periodically so the coverage at each POI is time-variant, and thus we are able to utilize a small number of mobile sensors to achieve sweep coverage among a much larger number of POIs. We investigate the definitions and model for sweep coverage. Given a set of POIs and their sweep period requirements, we prove that determining the minimum number of required sensors (min-sensor sweep-coverage problem) is NP-hard, and it cannot be approximated within a factor of 2. We propose a centralized algorithm with constant approximation ratio 3 for the min-sensor sweep-coverage problem. We further characterize the nonlocality of the problem and design a distributed sweep algorithm, DSWEEP, cooperating sensors to provide efficiency with the best effort. We conduct extensive simulations to study the performance of the proposed algorithms. Our simulations show that DSWEEP outperforms the randomized scheme in both effectiveness and efficiency.

Wormhole attack is a severe threat to wireless ad hoc and sensor networks. Most existing countermeasures either require specialized hardware devices or make strong assumptions on the network in order to capture the specific (partial) symptom induced by wormholes. Those requirements and assumptions limit the applicability of previous approaches. In this paper, we present our attempt to understand the impact and inevitable symptom of wormholes and develop distributed detection methods by making as few restrictions and assumptions as possible. We fundamentally analyze the wormhole problem using a topology methodology and propose an effective distributed approach, which relies solely on network connectivity information, without any requirements on special hardware devices or any rigorous assumptions on network properties. We formally prove the correctness of this design in continuous geometric domains and extend it into discrete domains. We evaluate its performance through extensive simulations.

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. 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 multi-task scheduling, memory isolation, and versatile stack management. We have implemented SenSmart on MICA2/MICAz motes. Evaluation shows that SenSmart performs efficient binary translation and demonstrates a significantly better capability in managing concurrent tasks than other sensor net operating systems.

We demonstrate that the network flux over the sensor network provides us fingerprint information about the mobile users within the field. Such information is exoteric in the physical space and easy to access through passive sniffing. We present a theoretical model to the network flux according to the statuses of mobile users. We fit the theoretical model with the network flux measurements through Non-linear Least Squares (NLS) and develop an algorithm that iteratively approaches the NLS solution by Sequential Monte Carlo Estimation. With sparse measurements of the flux information at individual sensor nodes, we are able to identify the mobile users within the network and instantly track their movements without breaking into the details of the communicational packets. A particular advantage of this approach is that compared to the vast information we can reveal the required knowledge is extremely cheap. As all fingerprint information comes from the network flux that is public under current wireless communication medium, our study indicates that most of existing systems are vulnerable in protecting the privacy of mobile users.

The high mobility of VANET makes information exchange across the network excessively difficult. Traditional approaches designed for stationary networks are not applicable due to the high dynamics among the nodes. Applying the routing techniques tailored for general mobile networks inevitably brings huge traffic burden to the crowded urban VANET and leads to low efficiency. To make the information exchange fluent and efficient, we explore the unique features of the urban VANET. By exploring the invariants in the mobile network topology, we are able to efficiently manage the information on top of the “intersection graph” transformed from the underlying network of road segments in the urban area. Our approach can thus achieve efficient query dissemination and data retrieval on this information organization. We intensively investigate and analyze a trace that records the movement of more than 4000 taxies in the urban area of Shanghai City over several months. We grasp the key impact of the fundamental factors that affect the VANET behaviors and accordingly develop tailored techniques to maximize the performance of this design. Experimental results validate the effectiveness and efficiency of our design.

Wireless sensor networks are deployed in various territories executing different tasks. In many applications, it is very useful to understand their topological characteristics. This paper studies the problem of discovering the topological properties of a sensor network such as boundaries and holes. Previous works have revealed that, such a problem could be addressed with knowledge of node locations, measures of interdistances, or ideal assumptions of particular communication models, e.g., unit disk graph model. In this work, however, we explore the possibility of discovering sensor network topology merely with connectivity information. We propose a virtual voronoi diagram approach to detect both the inner and outer boundaries of a sensor network. We do not rely on any communication models, yet any geometric knowledge of the network. Compared with previous connectivity based approaches, we further release the assumption of regular wireless signals. Our approach works even for anisotropic network with irregular wireless links. We design our approach to be light-weight, preventing frequent global operations that have been intensively used in previous designs. We conduct intensive simulations in networks of different topologies with different node degrees and densities, and containing various signal irregularities. The results validate the effectiveness and efficiency of our approach.

Contour mapping is a crucial part of many wireless sensor network applications. Many efforts have been made to avoid collecting data from all the sensors in the network and producing maps at the sink, which is proven to be inefficient. The existing approaches (often aggregation based), however, suffer from heavy transmission traffic and incur large computational overheads on each sensor node. We propose Iso-Map, an energy-efficient protocol for contour mapping, which builds contour maps based solely on the reports collected from intelligently selected “isoline nodes” in wireless sensor networks. Iso-Map achieves high-quality contour mapping while significantly reducing the generated traffic from O(n) to O(\sqrt n), where n is the total number of sensor nodes in the field. The pernode computation overhead is also restrained as a constant. We conduct comprehensive trace-driven simulations to verify this protocol, and demonstrate that Iso-Map outperforms the previous approaches in the sense that it produces contour maps of high fidelity with significantly reduced energy cost.

Network diagnosis, an essential research topic for traditional networking systems, has not received much attention for wireless sensor networks (WSNs). Existing sensor debugging tools like sympathy or EmStar rely heavily on an add-in protocol that generates and reports a large amount of status information from individual sensor nodes, introducing network overhead to the resource constrained and usually traffic-sensitive sensor network. We report our initial attempt at providing a lightweight network diagnosis mechanism for sensor networks. We further propose PAD, a probabilistic diagnosis approach for inferring the root causes of abnormal phenomena. PAD employs a packet marking scheme for efficiently constructing and dynamically maintaining the inference model. Our approach does not incur additional traffic overhead for collecting desired information. Instead, we introduce a probabilistic inference model that encodes internal dependencies among different network elements for online diagnosis of an operational sensor network system. Such a model is capable of additively reasoning root causes based on passively observed symptoms. We implement the PAD prototype in our sea monitoring sensor network test-bed. We also examine the efficiency and scalability of this design through extensive trace-driven simulations.

Wormhole attack is a severe threat against wireless ad hoc and sensor networks. It can be launched without compromising any legitimate node or cryptographic mechanisms, and often serves as a stepping stone for many serious attacks. Most existing countermeasures often make critical assumptions or require specialized hardware devices in the network. Those assumptions and requirements limit the applicability of previous approaches. In this work, we explore the impact of wormhole attacks on network connectivity topologies, and develop a simple distributed method to detect wormholes, called WormCircle-. WormCircle relies solely on local connectivity information without any requirements on special hardware devices or making any rigorous assumptions on network properties. We establish the correctness of this design in continuous geometric domains and extend it into discrete networks. We evaluate the effectiveness in randomly deployed sensor networks through extensive simulations.

Wireless sensor networks have become a promising technology in monitoring physical world. In many applications with wireless sensor networks, it is essential to understand how well an interested area is monitored (covered) by sensors. The traditional way of evaluating sensor coverage requires that every point in the field should be monitored and the sensor network should be connected to transmit messages to a processing center (sink). Such a requirement is too strong to be financially practical in many scenarios. In this study, we address another type of coverage problem, sweep coverage, when we utilize mobile nodes as supplementary in a sparse and probably disconnected sensor network. Different from previous coverage problem, we focus on retrieving data from dynamic Points of Interest (POIs), where a sensor network does not necessarily have fixed data rendezvous points as POIs. Instead, any sensor node within the network could become a POI. We first analyze the relationship among information access delay, information access probability, and the number of required mobile nodes. We then design a distributed algorithm based on a virtual 3D map of local gradient information to guide the movement of mobile nodes to achieve sweep coverage on dynamic POIs. Using the analytical results as the guideline for setting the system parameters, we examine the performance of our algorithm compared with existing approaches.

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 pre-knowledge of user and sensor locations, the design of an effective and efficient navigation protocol faces non-trivial 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.

Environment monitoring in coal mines is an important application of wireless sensor networks (WSNs) that has commercial potential. We discuss the design of a Structure-Aware Self-Adaptive WSN system, SASA. By regulating the mesh sensor network deployment and formulating a collaborative mechanism based on a regular beacon strategy, SASA is able to rapidly detect structure variations caused by underground collapses. We further develop a sound and robust mechanism for efficiently handling queries under instable circumstances. A prototype is deployed with 27 mica2 motes in a real coal mine. We present our implementation experiences as well as the experimental results. To better evaluate the scalability and reliability of SASA, we also conduct a large-scale trace-driven simulation based on real data collected from the experiments.

In wireless sensor networks, mobile sensors are often employed for enhancing the sensing coverage and detection accuracy. Current approaches assume mobile sensors with the capability of arbitrary movement. The usage of such sensors with unlimited mobility, however, requires complicated sensor manufactures and high intelligence of movement which are practically unrealistic in many practical applications. We investigate the usage of cable-based mobile sensors which move along pre-deployed cables to accomplish sensing tasks at different positions. A target area is said to be reachable, if for any point in this area, at least one mobile sensor can move along the cable and achieve coverage to the point within a specified delay bound. We propose to achieve k reachability for the sensing field with minimum mobile sensors along the cable. Further, during special events, mobile sensors need to move and help surveillance. We need adjust the positions of the rest of mobile sensors accordingly to balance the reachability within the area. We prove the NP-hardness of the targeted problems and give heuristic approaches. Through comprehensive simulations, we evaluate the performance of this design and show its effectiveness.

Many efforts have been made for addressing coverage problems in sensor networks. They fall into two categories, full coverage and barrier coverage, featured as static coverage. In this work, we study a new coverage scenario, sweep coverage, which differs with the previous static coverage. In sweep coverage, we only need to monitor certain points of interest (POIs) periodically so the coverage at each POI is time-variant, and thus we are able to utilize a small number of mobile sensors to achieve sweep coverage among a much larger number of POIs. We investigate the definitions and model for sweep coverage. Given a set of POIs and their sweep period requirements, we prove that determining the minimum number of required sensors (min-sensor sweep-coverage problem) is NP-hard, and it cannot be approximated within a factor of 2. We propose a centralized algorithm with constant approximation ratio 2 + epsi for the simplified problem where all sweep periods are identical. We further characterize the non-locality of the problem and design a distributed sweep algorithm, DSWEEP, cooperating sensors to provide required sweep requirements with the best effort. We conduct extensive simulations to study the performance of the proposed algorithms. Our simulations show that DSWEEP outperforms the randomized scheme in both effectiveness and efficiency.

There have been works done by utilizing mobile sensors as supplementary to assist the sensing coverage for the static sensor nodes in possible event happenings. Most of them assume that the mobile sensors are equipped with unlimited mobility and thus can move anywhere within the monitored field. However, the assumption of unlimited mobility has its own limitations and is unrealistic in many practical applications. Alternatively, we consider pre-deploying cables within the monitored field so that mobile sensors move along cables to destinations. In this case, we can far more relax the requirements on the mobile sensors and achieve more realistic usage despite of the complex field landforms. We find that existing cables deployed in the tunnel are perfect carriers for deploying mobile nodes which help get rid of the complex circumstance in the underground tunnel.

For multi-hop ad hoc networks formed by individually owned nodes, the endpoints can only observe whether or not the end-to-end transaction was successful or not, but not the individual actions of intermediate nodes. Consequently, in the absence of properly designed incentive schemes, rational (i.e., selfish) intermediate nodes may choose to forward data packets at a very low priority or simply drop the packets at all, and it could put the blame on the unreliable wireless channel. Using a principal-agent model, we propose several efficient methods that can eliminate the hidden actions under hidden information in multi-hop wireless networks with high probability. We design several algorithmic mechanisms for a number of routing scenarios such that each selfish agent will maximize its utility (i.e., profit) when it truthfully declares its type (i.e., cost and its actions) and it truthfully follows its declared actions. Our simulations show that the payment by our mechanisms is only slightly larger than the actual cost incurred by all intermediate nodes.

Geographical hash table (GHT) has been widely used to provide energy efficiency for data-centric storage in wireless sensor networks. Such a mechanism, however, suffers from high communication cost when we apply multi-dimensional event search in the network. In this work, we present MDS, a flexible, complete, and efficient multi-dimensional search mechanism atop traditional GHT based data-centric storage architecture. MDS utilizes bloom filters to reduce the communication cost of in-network intersection and union operations for multi-dimensional queries in wireless sensor networks. This scheme can be easily extended to support multi-dimensional range queries. Our mathematical analysis indicates the optimal settings for the bloom filters that maximize the traffic savings according to the information popularities. We conduct comprehensive simulations to evaluate our design. Results show that MDS achieves significant performance improvement in terms of energy consumptions and thus improves the applicability of the multi-dimensional search over the GHT based data-centric storage in sensor networks.

Abstract—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 non-trivial 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. Keywords—navigation; sensor networks; cyber-physical system I.

Network diagnosis, an essential research topic for traditional networking systems, has not received much attention for wireless sensor networks. Existing sensor debugging tools like sympathy or EmStar rely heavily on an add-in protocol that generates and reports a large amount of status information from individual sen-sor nodes, introducing network overhead to a resource constrained and usually traffic sensitive sensor network. We report in this study our initial attempt at providing a light-weight network diag-nosis mechanism for sensor networks. We propose PAD, a prob-abilistic diagnosis approach for inferring the root causes of ab-normal phenomena. PAD employs a packet marking algorithm for efficiently constructing and dynamically maintaining the inference model. Our approach does not incur additional traffic overhead for collecting desired information. Instead, we introduce a prob-abilistic inference model which encodes internal dependencies among different network elements, for online diagnosis of an operational sensor network system. Such a model is capable of additively reasoning root causes based on passively observed symptoms. We implement the PAD design in our sea monitoring sensor network test-bed and validate its effectiveness. We further evaluate the efficiency and scalability of this design through ex-tensive trace-driven simulations.

No abstract available.

Event detection is a crucial task for wireless sensor network applications, especially environment monitoring. Existing approaches for event detection are mainly based on some predefined threshold values, and thus are often inaccurate and incapable of capturing complex events. For example, in coal mine monitoring scenarios, gas leakage or water osmosis can hardly be described by the overrun of specified attribute thresholds, but some complex pattern in the full-scale view of the environmental data. To address this issue, we propose a non-threshold based approach for the real 3D sensor monitoring environment. We employ energy-efficient methods to collect a time series of data maps from the sensor network and detect complex events through matching the gathered data to spatio-temporal data patterns. Finally, we conduct trace driven simulations to prove the efficacy and efficiency of this approach on detecting events of complex phenomena from real-life records.

Event detection is a crucial task for wireless sensor network applications, especially environment monitoring. Existing approaches for event detection are mainly based on some predefined threshold values, and thus are often inaccurate and incapable of capturing complex events. For example, in coal mine monitoring scenarios, gas leakage or water osmosis can hardly be described by the overrun of specified attribute thresholds, but some complex pattern in the full-scale view of the environmental data. To address this issue, we propose a non-threshold based approach for the real 3D sensor monitoring environment. We employ energy-efficient methods to collect a time series of data maps from the sensor network and detect complex events through matching the gathered data to spatio-temporal data patterns. Finally, we conduct trace driven simulations to prove the efficacy and efficiency of this approach on detecting events of complex phenomena from real-life records.

Contour mapping is a crucial part of many wireless sensor network applications. Many efforts have been made to avoid collecting data from all the sensors in the network and producing maps at the sink, which is proven to be inefficient. The existing approaches (often aggregation based), however, suffer from heavy transmission traffic and incur large computational overheads on each sensor node. We propose Iso-Map, an energy-efficient protocol for contour mapping, which builds contour maps based solely on the reports collected from intelligently selected "isoline nodes" in wireless sensor networks. Iso-Map achieves high-quality contour mapping while significantly reducing the generated traffic from O(n) to O(radicn), where n is the total number of sensor nodes in the field. The per-node computation overhead is also restrained as a constant. We conduct comprehensive trace-driven simulations to verify this protocol, and demonstrate that Iso-Map outperforms the previous approaches in the sense that it produces contour maps of high fidelity with significantly reduced energy cost.

Environment monitoring in coal mines is an important application of wireless sensor networks (WSNs) that has commercial potential. We discuss the design of a Structure-Aware Self-Adaptive WSN system, SASA. By regulating the mesh sensor network deployment and formulating a collaborative mechanism based on a regular beacon strategy, SASA is able to rapidly detect structure variations caused by underground collapses. A prototype is deployed with 27 Mica2 motes. We present our implementation experiences as well as the experimental results. To better evaluate the scalability and reliability of SASA, we also conduct a large-scale trace-driven simulation based on real data collected from the experiments.

Sensor positioning is a crucial part of many location-dependent applications that utilize wireless sensor networks (WSNs). Current localization approaches can be divided into two groups: range-based and range-free. Due to the high costs and critical as-sumptions, the range-based schemes are often impractical for WSNs. The existing range-free schemes, on the other hand, suffer from poor accuracy and low scalability. Without the help of a large number of uniformly deployed seed nodes, those schemes fail in anisotropic WSNs with possible holes. To address this issue, we propose the Rendered Path (REP) protocol. To the best of our knowledge, REP is the only range-free protocol for locating sen-sors with constant number of seeds in anisotropic sensor net-works.

Sea depth monitoring is a critical task to ensure the safe operation of harbors. Traditional schemes largely rely on labor-intensive work and expensive hardware. This study explores the possibility of deploying networked sensors on the surface of sea, measuring and reporting sea depth of given areas. We propose a restricted floating sensors (RFS) model, in which sensor nodes are anchored to the sea bottom, floating within a restricted area. Distinguished from traditional stationary or mobile sensor networks, the RFS network consists of sensor nodes with restricted mobility. We construct the network model and elaborate the corresponding localization problem. We show that by locating such RFS sensors, the sea depth can be estimated without the help of any extra ranging devices. A prototype system with 25 Telos sensor nodes is deployed to validate this design. We also examine the efficiency and scalability of this design through large-scale simulations.

In wireless mobile ad hoc networks (MANETs), energy is a crucial resource. Topology control technology allows network nodes to reduce their transmission power while preserving the network connectivity. A MANET is a non-cooperative system so that only when a node earns its payment, which can cover its cost, the cooperation can be stimulated. We design a truthful topology control mechanism (TRUECON) for MANETs to induce the selfish, but rational, network nodes to collaborate. Truth-telling is a dominant strategy in TRUECON. A node needs to reveal its true value in order to obtain the maximum expected utility. We prove the overpayment of TRUECON has a bound depending on different radio propagation models