Wireless charging eliminates cumbersome cables, revolutionizing how to charge mobile devices, yet reliably detecting metal foreign objects (e.g., keys, SIM ejectors, paper clips) poses a persistent challenge. This detection is critical, as such objects can inadvertently enter the charging zone, absorb energy, and trigger overheating, diminished efficiency, device damage, and even burns or fires. Existing approaches mainly monitor energy loss at the mobile device to infer intrusions, but this loss mixes inherent system dissipation with object-induced effects, both highly variable across devices and conditions, which often results in missed detections (as found in a range of commercial chargers). In this paper, we present Met-Sentry, a novel system design that takes a fundamentally different approach. Our key insight is that, beyond energy absorption, metal foreign objects also alter the electromagnetic field: they disproportionately attenuate the high-frequency harmonics in the in-band communication waveforms during charging, acting as a low-pass filter and yielding a distinctive, physics-grounded fingerprint of their presence. MetSentry captures and analyzes these fingerprints through a lightweight sensing circuit and a tailored software pipeline that extracts robust, discriminative features, which can be seamlessly integrated into wireless chargers, enabling reliable detection. Extensive experiments with various commercial wireless chargers, smartphones, and metal foreign objects demonstrate that MetSentry consistently outperforms both built-in charger detection and state-of-the-art methods. © 2026 Copyright held by the owner/author(s).

Motor Imagery (MI) is an emerging Brain–Computer Interface (BCI) paradigm in which a person imagines a body movement without any physical action. By decoding the scalp-recorded electroencephalography (EEG) signals, BCIs can establish direct communication pathways to control external devices, offering significant potential in prosthetics, rehabilitation, and human–computer interaction. However, existing solutions remain difficult to deploy in practice. (i) Most employ independent, opaque models for each MI task. This fragmented methodology lacks a unified architectural foundation. Consequently, these models are trained in isolation and fail to learn robust representations from diverse datasets, which often results in modest performance. (ii) They primarily adopt fixed sensor deployment, whereas real-world setups vary in electrode number and placement, causing models trained on one configuration to fail under another. (iii) Performance degrades sharply under low-SNR conditions typical of consumer-grade EEG. Together, these limitations hinder the practical adoption of MI-based BCIs.

Low-Power Wide-Area Networks (LPWANs) are essential for IoT connectivity, but they face a longstanding fundamental challenge: achieving both continuous receiver availability and long-term battery operation without compromising network performance. In low-duty-cycle modes like LoRaWAN’s Class A or B, data timeliness and network throughput are limited, and devices are deaf to peer transmissions, restricting topologies to a single hop, hindering scalability in practical applications. While Class C enables always-on operation for real-time communication and multi-hop networking, commercial LoRa transceivers consume up to 42.4 mW in this mode, rendering sustained battery-powered deployments infeasible. Recent low-power designs cut consumption to sub-milliwatt levels but limit communication ranges to mere hundreds of meters, undermining LPWAN’s “wide-area’’ vision. This paper introduces Longan, the first LoRa receiver to overcome this power-range tradeoff boundary, enabling kilometer-scale links with a power of about 1 mW only. Longan achieves this via two breakthroughs: (1) We design a novel LoRa receiver analog radio frequency (RF) front-end that exploits negative differential resistors for high-efficiency signal amplification while simultaneously reducing power consumption by 2–3 orders of magnitude; (2) Building on this, we further introduce an architectural decoupling of detection and demodulation, and design a lightweight analog dechirping circuit using our front-end. This circuit enables always-on preamble detection at just one-tenth the power of digital counterparts, triggering COTS demodulation only on demand. Evaluation shows that Longan sustains continuous detection at 1.16 mW, a 36 × reduction from COTS LoRa, while preserving sensitivity within 3–13 dB of commercial devices and enabling long-range communication.

Embedded system development is crucial for enabling seamless connectivity and functionality across a wide range of Internet of Things (IoT) applications. However, such a complex process requires cross-domain knowledge of hardware and software and hence often necessitates direct developer involvement, making it labor-intensive, time-consuming, and error-prone. To address this challenge, this paper introduces AutoEmbed, the first automated software development platform for general-purpose embedded IoT systems. The key idea is to leverage the reasoning ability of Large Language Models (LLMs) and embedded system expertise to automate the hardware-in-the-loop development process. The main methods include a component-aware library resolution method for addressing hardware dependencies, a library knowledge generation method that injects utility domain knowledge into LLMs, and an auto-programming method that ensures successful deployment. We evaluate AutoEmbed’s performance across 71 modules and four mainstream embedded development platforms with over 350 IoT tasks. Experimental results show that AutoEmbed can generate codes with an accuracy of 95.7% and complete tasks with a success rate of 86.5%, surpassing human-in-the-loop baselines by 15.6%–37.7% and 25.5%–53.4%, respectively. We also show AutoEmbed ’s potential through case studies in environmental monitoring and remote control systems development. © 2026 Copyright held by the owner/author(s).

In the digital era, the increasing demand for network traffic necessitates strategic network infrastructure planning. Accurate modeling of traffic demand through cellular traffic generation is crucial for optimizing base station deployment, enhancing network efficiency, and fostering technological innovation. In this paper, we introduce STOUTER, a spatio-temporal diffusion model for cellular traffic generation. STOUTER incorporates noise into traffic data through a forward diffusion process, followed by a reverse reconstruction process to generate realistic cellular traffic. To effectively capture the spatio-temporal patterns inherent in cellular traffic, we pre-train a temporal graph and a base station graph, and design the Spatio-Temporal Feature Fusion Module (STFFM). Leveraging STFFM, we develop STUnet, which estimates noise levels during the reverse denoising process, successfully simulating the spatio-temporal patterns and uncertainty variations in cellular traffic. Extensive experiments conducted on five cellular traffic datasets across two regions demonstrate that STOUTER improves cellular traffic generation by 52.77% in terms of the Jensen-Shannon Divergence (JSD) metric compared to existing models. These results indicate that STOUTER can generate cellular traffic distributions that closely resemble real-world data, providing valuable support for downstream applications.

With the rapid development of new smart devices, such as smart home appliances and VR/AR equipment, there is an increasing demand for novel interaction methods. However, many existing interaction methods require external devices, are unintuitive, and demand substantial user learning effort. To fill this gap, we propose ArmPad, a system that leverages the smartwatch's built-in IMU to enablemultidimensional inputon the user's forearm. Methodologically, ArmPad is explicitly designed to address three core research challenges in forearm-based interaction. First, to resolve the inherent feature conflicts between discrete gesture recognition and continuous distance estimation, we propose a multi-task learning framework with a dynamic gating mechanism for cross-task synergy. Second, to tackle the physical limitation of rapid vibration attenuation across the forearm, we introduce a cross-device guidance strategy that incorporates high-fidelity fingertip knowledge during the training phase. Finally, to ensure robust generalization across diverse populations, we develop task-specific data augmentation and a lightweight user registration mechanism to effectively mitigate physiological variances. Experiments on 20 subjects demonstrate that ArmPad achieves an accuracy of 92.51% on nine gestures and a Mean Absolute Error of 1.53$cm$for sliding distance in cross-user settings. Extensive robustness evaluations and case studies further confirm the system's stability and usability under diverse real-world conditions.

Federated learning (FL) has been recognized as a promising paradigm to support distributed AI model training among wireless devices (WDs) under the coordination of an edge server (ES) without sharing local datasets. To alleviate computation burden of resource-limited WDs, model partitioning that leverages computing capability at the ES is further integrated into FL, yielding the split (S) FL framework. In this paper, we study online bandit model partitioning and resource allocation for SFL over dynamic wireless networks, aiming to minimize overall energy-latency cost (ELC). Unlike prior works focusing on offline static or online gradient-based model splitting and resource allocation, we consider a practical setting where the analytical expression of ELC function is unavailable, and instead only the function values at queried points are revealed. To tackle such a challenging mixed-integer non-linear programming problem in the online bandit context, novel Bayesian optimization (BO)-based approaches are put forth by relying on a Gaussian process (GP)-based surrogate model to actively select the model splitting points and resource allocation decisions per round via the low-complexity acquisition. Besides incorporating training model-specific structural information in the kernel design of the GP surrogate, an ensemble of GP models with data-adaptive weights is further leveraged to capture system dynamics. To cope with the challenging combinatorial nature and strong coupling over mixed action space during acquisition, an efficient alternating optimization approach is proposed building upon a novel contextual local search method. Numerical tests demonstrate that the proposed BO-based approaches outperform the contemporary baselines under various practical SFL settings.

Accurate modeling of traffic demand through cellular traffic generation is crucial for optimizing base station deployment. We thus present STOUTER, a Spatio-Temporal diffusiOn model for cellUlar Traffic genERation. To effectively capture spatial and temporal dynamics, we pretrain both a temporal graph and a base-station graph, and introduce a Spatio-Temporal Feature Fusion Module (STFFM). On five datasets from two regions, STOUTER reduces Jensen-Shannon Divergence by 52.8% over prior methods, generating distributions that closely match real traffic and aiding downstream planning tasks.

Mobile devices have increasingly integrated with numerous deep learning-based visual applications, such as object classification and recognition models. While these models perform well in controlled environments, their effectiveness declines in real-world environment due to out-of-distribution (OOD) data not seen during training. Existing methods for detecting OOD data often compromise normal data recognition and require extensive training on unattainable OOD data. To address these issues, we propose$\mathtt {POD}$, a framework designed to enhance mobile visual applications by providing high-precision OOD detection without affecting original model performance. In the offline phase,$\mathtt {POD}$generates OOD detectors from any classification model by analyzing model's neuron responses to various data types. In the online phase, it continuously adjusts decision boundaries by integrating results from both the original model and the detector. Evaluated on two public datasets and one self-collected dataset across various popular classification models,$\mathtt {POD}$significantly improves OOD detection performance while maintaining the accuracy of original models.

Wireless-based human activity recognition (WHAR) enables various promising applications. However, since WHAR is sensitive to changes in sensing conditions (e.g., different environments, users, and new activities), trained models often do not work well under new conditions. Recent research uses meta-learning to adapt models. However, they must fine-tune the model, which greatly hinders the widespread adoption of WHAR in practice because model fine-tuning is difficult to automate and requires deep-learning expertise. The fundamental reason for model fine-tuning in existing works is because their goal is to find the mapping relationship between data samples and corresponding activity labels. Since this mapping reflects the intrinsic properties of data in the perceptual scene, it is naturally related to the conditions under which the activity is sensed. To address this problem, we exploit the principle that under the same sensing condition, data of the same activity class are more similar (in a certain latent space) than data of other classes, and this property holds invariant across different conditions. Our main observation is that meta-learning can actually also transform WHAR design into a learning problem that is always under similar conditions, thus decoupling the dependence on sensing conditions. With this capability, general and accurate WHAR can be achieved, avoiding model fine-tuning. In this paper, we implement this idea through two innovative designs in a system called RoMF. Extensive experiments using FMCW, Wi-Fi and acoustic three sensing signals show that it can achieve up to 95.3% accuracy in unseen conditions, including new environments, users and activity classes.

This poster introduces SynMotion, a novel mmWave-based human motion sensing system addressing the scarcity of training datasets. By synthesizing mmWave signals using existing vision-based human motion datasets, this system overcomes the challenge of collecting and labeling mmWave data, facilitating wider adoption of mmWave technology for applications like activity recognition, skeleton tracking and radar placement recommendation.

Device authentication plays a crucial role in preventing illegal access and ensuring smooth usage of magnetic wireless charging. However, current authentication techniques suffer from security vulnerabilities and are incompatible with low-cost receiver devices, thus severely limiting their applications. In this paper, we propose FreAuth, a novel Frequency feature-based device Authentication technology for magnetic wireless power transfer systems. Technically, we begin by conducting circuit measurements at the transmitter side to subtly retrieve impedance information related to the receiver without the need for its corporation. Then, we employ a dual-frequency interleaved-based subtraction technique to remove the ideal receiver impedance and capture the fairly weak frequency features. Furthermore, we normalize the captured frequency features to account for environment variations. These steps allow us to generate and store a hardware fingerprint for the receiver based on its frequency features. During device authentication, we use a discrete Frechet distance-based algorithm for fingerprint matching. We devise and implement a prototype of FreAuth and conduct extensive experiments to evaluate the proposed scheme. The experimental results validate the reliability (95.74% authentication accuracy among 60+ devices) and robustness (anti-interference with device location variations) of our FreAuth.

Accurate estimation of on-device model training time is increasingly required for emerging learning paradigms on mobile edge devices, such as heterogeneous federated learning (HFL). HFL usually customizes the model architecture according to the different capabilities of mobile edge devices to ensure efficient use of local data from all devices for training. However, due to oversimplification of latency modeling, existing methods rely on a single coefficient to represent computational heterogeneity, resulting in sub-optimal HFL efficiency. We find that existing methods ignore the important impact of runtime optimization of deep learning frameworks, which we call development-chain diversity. Specifically, layers of a model may have different algorithm implementations, and deep learning frameworks often have different strategies for selecting the algorithm they believe is the best based on a range of runtime factors, resulting in different training latencies and invalid predictions from existing methods. In this paper, in addition to considering this diversity to ensure synchronized completion time of model training, we also study how to select the best algorithm each time to reduce the latency of the per-round training, thereby further improving the overall efficiency of federated training. To this end, we propose LATTE, which consists of a novel selector that identifies the best algorithm at runtime based on relative runtime factors. By further integrating it into our training latency model, LATTE provides accurate training time estimation. We develop LATTE as middleware, compatible with different deep learning frameworks. Extensive results show significantly improved training convergence speed and model accuracy compared to state-of-the-art methods.

Diverse Wi-Fi-based wireless applications have been proposed, ranging from daily activity recognition to vital sign monitoring. Despite their remarkable sensing accuracy, the high energy consumption and the requirement for customized hardware modification hinder the wide deployment of the existing sensing solutions. In this paper, we propose REHSense, an energy-efficient wireless sensing solution based on Radio-Frequency (RF) energy harvesting. Instead of relying on a power-hungry Wi-Fi receiver, REHSense leverages an RF energy harvester as the sensor and utilizes the voltage signals harvested from the ambient Wi-Fi signals to enable simultaneous context sensing and energy harvesting. We design and implement REHSense using a commercial-off-the-shelf (COTS) RF energy harvester. Extensive evaluation of three fine-grained wireless sensing tasks (i.e., respiration monitoring, human activity recognition, and hand gesture recognition) shows that REHSense can achieve comparable sensing accuracy with conventional Wi-Fi-based solutions while adapting to different sensing environments, reducing the power consumption of sensing by 98.7% and harvesting up to 4.5 mW of power from RF energy.

GPUs are increasingly utilized for running DNN tasks on emerging mobile edge devices. Beyond accelerating single task inference, their value is also particularly apparent in efficiently executing multiple DNN tasks, which often have strict latency requirements in applications. Preemption is the main technology to ensure multitasking timeliness, but mobile edges primarily offer two priorities for task queues, and existing methods thus achieve only coarse-grained preemption by categorizing DNNs into real-time and best-effort, permitting a real-time task to preempt best-effort ones. However, the efficacy diminishes significantly when other real-time tasks run concurrently, but this is already common in mobile edge applications. Due to different hardware characteristics, solutions from other platforms are unsuitable. For instance, GPUs on traditional mobile devices primarily assist CPU processing and lack special preemption support, mainly following FIFO in GPU scheduling. Clouds handle concurrent task execution, but focus on allocating one or more GPUs per complex model, whereas on mobile edges, DNNs mainly vie for one GPU. This paper introduces Pantheon, designed to offer fine-grained preemption, enabling real-time tasks to preempt each other and best-effort tasks. Our key observation is that the two-tier GPU stream priorities, while underexplored, are sufficient. Efficient preemption can be realized through software design by innovative scheduling and novel exploitation of the nested redundancy principle for DNN models. Evaluation on a diverse set of DNNs shows substantial improvements in deadline miss rate and accuracy of Pantheon over state-of-the-art methods.

This paper introduces ASGaze, a novel gaze tracking system using the RGB camera of smartphones. ASGaze improves the accuracy of existing methods and uniquely tracks gaze points on various surfaces, including phone screens, computer displays, and non-electronic surfaces like whiteboards or paper - a situation that is challenging for existing methods. To achieve this, we revisit the 3D geometric eye model, commonly used in high-end commercial trackers, and it has the potential to achieve our goals. To avoid the high cost of commercial solutions, we identify three fundamental issues when processing the eye model with an RGB camera, including how to accurately extract iris boundary that is the meta-information in our design, how to remove ambiguity from iris boundary to gaze point transformation, and how to map gaze points onto the target surface. Furthermore, as we consider deploying ASGaze in real-world applications, two additional challenges should be addressed: how to automatically and accurately annotate the training dataset to reduce manual labor and time costs, and how to accelerate the inference speed of ASGaze on mobile devices to improve user experience. We propose effective techniques to resolve these issues. Our prototype and experiments on three tracking surfaces demonstrate significant performance gains.

This paper introduces WETrak, a finger tracking system using wrist-worn electromyography (EMG) sensors. Recent finger tracking methods mainly employ EMGs on armbands. Compared to a range of contactless methods using cameras or wireless, they are not limited by high computational costs, privacy concerns, and mobility, while unlike other wearable-based approaches, they do not require the deployment of sensors on the user's hands. However, users need to wear an additional armband on their forearm each time solely for tracking purpose, which hinders the widespread adoption of finger tracking in practice. This paper investigates the feasibility of moving EMG sensors from the forearm to the wrist for finger tracking. WETrak inherits the advantages of existing EMG-based armband tracking while avoiding the limitation of requiring additional armbands, which brings a strong incentive for integrating EMG sensors into wrist-worn wearables in the future. As sensor placement varies, we find new challenges in determing good locations to place sensors to gather useful information to capture all finger movements and using low-quality signals to still ensure accurate tracking. In this paper, we introduce new, efficient solutions to these problems. We develop a prototype, and the results show that WETrak outperforms the state-of-the-art method and performs consistently well under various settings.

This paper addresses a significant problem in American Sign Language (ASL) translation systems that has been overlooked. Current designs collect excessive sensing data for each word and treat every sentence as new, requiring the collection of sensing data from scratch. This approach is time-consuming, taking hours to half a day to complete the data collection process for each user. As a result, it creates an unnecessary burden on end-users and hinders the widespread adoption of ASL systems. In this study, we identify the root cause of this issue and proposeGASLA–a wearable sensor-based solution that automatically generates sentence-level sensing data from word-level data. An acceleration approach is further proposed to optimize the data generation speed. Moreover, due to the gap between the generated sentence data and directly collected sentence data, a template strategy is proposed to make the generated sentences more similar to the collected sentence. The generated data can be used to train ASL systems effectively while reducing overhead costs significantly.GASLAoffers several benefits over current approaches: it reduces initial setup time and future new-sentence addition overhead; it requires only two samples per sentence compared to around ten samples in current systems; and it improves overall performance significantly.

Executing deep neural networks (DNNs) on edge artificial intelligence (AI) devices enables various autonomous mobile computing applications. However, the memory budget of edge AI devices restricts the number and complexity of DNNs allowed in such applications. Existing solutions, such as model compression or cloud offloading, reduce the memory footprint of DNN inference at the cost of decreased model accuracy or autonomy. To avoid these drawbacks, we divide DNN into blocks and swap them in and out in order, such that large DNNs can execute within a small memory budget. Nevertheless, naive swapping on edge AI devices induces significant delays due to the redundant memory operations in the DNN development ecosystem for edge AI devices. To this end, we develop SwapNet, an efficient DNN block swapping middleware for edge AI devices. We systematically eliminate the unnecessary memory operations during block swapping while retaining compatible with the deep learning frameworks, GPU backends, and hardware architectures of edge AI devices. We further showcase the utility of SwapNet via a multi-DNN scheduling scheme. Evaluations on eleven DNN inference tasks in three applications demonstrate that SwapNet achieves almost the same latency as the case with sufficient memory even when DNNs demand$2.32\times$$\sim$$5.81\times$memory beyond the available budget. The design of SwapNet also provides novel and feasible insights for deploying large language models (LLMs) on edge AI devices in the future.

As an essential component, roadside units (RSUs) play an indispensable role in realizing Vehicle-to-Everything (V2X) by seamlessly connecting various intelligent devices and vehicles. To facilitate the construction of V2X, much research has been done in designing effective RSU deployment strategies. However, most of these efforts are largely limited by design utility and deployment scalability. To address the limitations of previous works, this paper proposes a general RSU deployment framework,Greta, which can evaluate candidate deployment sites from different perspectives with rich input data, and satisfy different requirements on optimization metrics. To this end, we model the general RSU deployment problem as a customized reinforcement learning (RL) problem that intelligently explores the deployment environment to find a good deployment strategy. Specifically, we design an effective data profiling network to extract features from multi-modality input data. These extracted features are gradually weighted, fused, and encoded as part of the state representation of the RL model. We further design new reward functions considering various deployment metrics and propose an action space pruning scheme to speed up model training. We implement a prototype system ofGretaand extensively evaluate its performance using real-world data. The results showGretaachieves remarkable performance gains compared to recent RSU deployment methods.

The increasing proliferation of Internet-of-Things (IoT) devices in daily life has rendered secure Device-to-Device (D2D) communication increasingly crucial. Achieving secure D2D communication necessitates key agreement between various IoT devices without prior knowledge. Despite existing literature proposing numerous approaches, they exhibit limitations such as low key generation rates and short pairing distances. In this paper, we present InaudibleKey2.0, an inaudible acoustic signal based key generation protocol for mobile devices. Based on acoustic channel reciprocity, InaudibleKey2.0 exploits the acoustic channel frequency response of two legitimate devices as a shared secret for key generation. To significantly enhance performance, InaudibleKey2.0 incorporates novel technologies, including a deep learning-enabled channel prediction model for improved channel reciprocity, a quantization model for increased key generation rates, and a transformer-based reconciliation method for augmented key agreement rates. We conduct comprehensive experiments to evaluate InaudibleKey2.0 in diverse real-world environments. In comparison to state-of-the-art solutions, InaudibleKey2.0 achieves 1.3–9.1 times improvement in key generation rates, 3.2–44 times extension in pairing distances, and 1.2–16 times reduction in information reconciliation counts. Security analysis substantiates that InaudibleKey2.0 is resilient to numerous malicious attacks. Furthermore, we implement InaudibleKey2.0 on modern smartphones and resource-limited IoT devices. The results indicate that it is energy-efficient and can operate on both powerful and resource-limited IoT devices without causing excessive resource consumption.

RFID shows great potentials to build useful sensing applications. However, current RFID sensing can obtain mainly a single-dimensional sensing measurement from each reader-to-tag query, such as phase, RSS, etc. This is sufficient to fulfill the designs that are bound to the tag’s movement, e.g., the localization of tags. However, it imposes inevitable uncertainty on many sensing tasks relying on the features extracted from the RFID signals. These traditional sensing measurements limit the fidelity of RFID sensing fundamentally and prevent its broader usage in more sophisticated sensing scenarios. This paper presents RF-Wise to push the limit of RFID-based sensing, motivated by an insightful observation to customize RFID signals. RF-Wise can enrich the existing single-dimensional feature measure to a channel state information (CSI)-like measure with up to 150-dimensional samples across different frequencies concurrently. More importantly, RF-Wise is a software solution atop the standard EPC Gen2 protocol without using any extra hardware. It requires only one tag for sensing and works within the ISM band. RF-Wise, so far as we know, is the first system of such a kind. Extensive experiments show that RF-Wise does not impact underlying RFID communications, while by using the features extracted by RF-Wise, applications’ sensing performance can be improved remarkably. The source codes of RF-Wise are available at https://cui-zhao.github.io/RF-WISE/.

Power governing is a critical component of modern mobile devices, reducing heat generation and extending device battery life. A popular technology of power governing is dynamic voltage and frequency scaling (DVFS), which adjusts the operating frequency of a processor to balance its performance and energy consumption. With the emergence of diverse workloads on mobile devices, traditional DVFS methods that do not consider workload characteristics become suboptimal. Recent application-oriented methods propose dedicated and effective DVFS governors for individual application tasks. Since their approach is only tailored to the targeted task, performance drops significantly when other tasks run concurrently, which is however common on today's mobile devices. In this paper, our key insight is that hardware meta-data, widely used in existing DVFS designs, has great potential to enable capable workload awareness and task concurrency adaptability for DVFS, but they are underexplored. We find that workload characteristics can be described in a hyperspace composed of multiple dimensions derived from these metadata to form a novel workload contextual indicator to profile task dynamics and concurrency. On this basis, we propose a meta-state metric to capture this relationship and design a new solution, GearDVFS. We evaluate it for a rich set of application tasks, and it outperforms state-of-the-art methods.

Mobile phones nowadays are equipped with at least dual microphones. We find when a user is typing on a phone, the sounds generated from the vibration caused by finger’s tapping on the screen surface can be captured by both microphones, and these recorded sounds alone are informative enough to localize the user’s keystrokes. This ability can be leveraged to enable useful application designs, while it also raises a crucial privacy risk that the private information typed by users on mobile phones has a great potential to be leaked through such a recognition ability. In this paper, we address two key design issues and demonstrate, more importantly alarm people, that this risk is possible, which could be related to many of us when we use our mobile phones. We implement our proposed techniques in a prototype system and conduct extensive experiments. The evaluation results indicate promising successful rates for more than 4000 keystrokes from different users on various types of mobile phones.

This article presents SoundSticker, a system for steganographic, in-band data communication over an acoustic channel. In contrast with recent works that hide bits in inaudible frequency bands, SoundSticker embeds hidden bits in the audible sounds, making them more reliably survive audio codecs and bandpass filtering, while achieving a higher data rate and remaining imperceptible to a listener. The key observation behind SoundSticker is that the human ear is less sensitive to the audio phase changes than the frequency and amplitude changes, which leaves us an opportunity to alter the phase of an audio clip to convey hidden information. We take advantage of this opportunity and build an OFDM-based physical layer. To make this PHY-layer design work for a variety of end devices with heterogeneous computation resources, SoundSticker addresses multiple technical challenges including perceivable waveform artifacts caused by the phase-based modulation, bit rate adaptation without channel sounding and real-time preamble detection. Our prototype on both smartphones and ESP32 platforms demonstrates SoundSticker’s superior performance against the state of the arts, while preserving excellent sound quality and remaining unaffected by common audio codecs like MP3 and AAC. Audio clips produced by SoundSticker can be found at https://soundsticker.github.io/ .

The audio adversarial example has been demonstrated to be an effective attack which leads to prediction errors of the intelligent voice control system (e.g., deep neural network based speech recognition service), despite resembling a valid input to our human beings. An ideal adversarial example attack should have four major advantages, including 1) utilizing a universal adversarial perturbation against arbitrary voice commands, 2) tricking a model to get an incorrect and targeted result, 3) imperceptible to users even in a silent place and 4) validating in an over-the-air (OTA) scenario as well. However, existing studies mainly involve several but not all of these criteria. In this paper, we propose UTIO, a universal, targeted, imperceptible and OTA audio adversarial example design, which leverages one perturbation to fool a speech recognition model in OTA scenarios. Moreover, a variety of speeches can be misled to a targeted threat command imperceptibly. To harvest such benefits, we leverage two targeted loss functions to generate adversarial perturbations, and employ the psychoacoustic principle to further conceal the attack. Finally, we actively embed additional distortions, occurred during the physical propagation, in the process of perturbation generation to make UTIO still valid in an OTA scenario. Extensive experiments show that UTIO can perform 94.15% success attack rate locally, i.e., without physical propagation, while retaining 93.44% attack rate in an OTA scenario. In addition, three types of defensive strategies are also introduced to resist against our attack.

This paper studies an important yet overlooked applicability issue in existing American sign language (ASL) translation systems. With excessive sensing data collected for each ASL word already, current designs treat every to-be-recognized sentence as new and collect their sensing data from scratch, while the amounts of sentences and the data samples per sentence are large usually. It takes a long time to complete the data collection for each single user, e.g., hours to a half day, which brings non-trivial burden to the end users inevitably and prevents the broader adoption of the ASL systems in practice. In this paper, we figure out the reason causing this issue. We present GASLA atop the wearable sensors to instrument our design. With GASLA, the sentence-level sensing data can be generated from the word-level data automatically, which can be then applied to train ASL systems. Moreover, GASLA has a clear interface to be integrated to existing ASL systems for overhead reduction directly. With this ability, sign language translation could become highly lightweight in both initial setup and future new-sentence addition. Compared with around 10 per-sentence data samples in current systems, GASLA requires 2–3 samples to achieve a similar performance.

RFID shows great potentials to build useful sensing applications. However, current RFID sensing can obtain mainly a single-dimensional sensing measurement from each reader-to-tag query, such as phase, RSS, etc. This is sufficient to fulfill the designs that are bounded to the tag’s own movement, e.g., the localization of tags. However, it imposes inevitable uncertainty to many sensing tasks relying on the features extracted from the RFID signals, which limits the fidelity of RFID sensing fundamentally and prevents its broader usage in more sophisticated sensing scenarios. This paper presents RF-Wise to push the limit of the RFID-based sensing, motivated by an insightful observation to customize RFID signals. RF-Wise can enrich the existing single-dimensional feature measure to a channel state information (CSI)-like measure with up to 150 dimensional samples across different frequencies concurrently. More importantly, RF-Wise is a software solution atop the standard EPC Gen2 protocol without using any extra hardware, requires only one tag for sensing and works within the ISM band. RF-Wise, so far as we know, is the first system of such a kind. Extensive experiments show that RF-Wise does not impact underlying RFID communications, while by using the features extracted by RF-Wise, applications’ sensing performance can be improved remarkably. The source codes of RF-Wise are available at https://cui-zhao.github.io/RF-WISE/.

This paper introduces ASGaze, a new gaze tracking system designed using the common RGB camera from mobile phones. In addition to improving the accuracy of existing RGB camera-based gaze tracking methods, a novelty of ASGaze is that it can be configured to track gaze points on various surface areas commonly required in different applications, such as mobile phone screens, computer displays or even non-electronic surfaces like whiteboards or paper - a situation that is difficult for existing RGB camera-based methods to handle. To achieve the design of ASGaze, we revisit the 3D geometric model of the eye, which is widely adopted by high-end and commercial gaze trackers, and it has the potential to achieve our design goals. To avoid the high cost of commercial solutions, we identify three key issues to be addressed when processing the eye model with an RGB camera, including how to first accurately extract eye iris boundary that is the meta-information in our gaze tracking design, and then how to remove gaze ambiguity from iris boundary to gaze point transformation, and finally how to precisely map gaze points to the target tracking surface. In this paper, we propose a series of effective techniques to address these issues. We develop a prototype system and conduct extensive experiments on three different typical tracking surfaces to show promising performance gains compared to the recent solution.

Millimeter-wave (mmWave)-based human motion sensing, such as activity recognition and skeleton tracking, enables many useful applications. However, it suffers from a scarcity issue of training datasets, which fundamentally limits a widespread adoption of this technology in practice, as collecting and labeling such datasets are difficult and expensive. This paper presents SynMotion, a new mmWave-based human motion sensing system. Its novelty lies in harvesting available vision-based human motion datasets, for knowing the coordinates of body skeletal points under different motions, to synthesize mmWave sensing signals that bounce off the human body, so that the synthesized signals could inherit labels (skeletal coordinates and the name of each motion) from vision-based datasets directly. SynMotion demonstrates the ability to generate such labeled synthesized data at high quality to address the training-data scarcity issue and enable two sensing services that can work with commercial radars, including 1) zero-shot activity recognition, where the classifier reads real mmWaves for recognition, but it is only trained on synthesized data; and 2) body skeleton tracking with few/zero-shot learning on real mmWaves. To design SynMotion, we address the challenges of both the inherent complication of mmWave synthesis and the micro-level differences compared to real mmWaves. Extensive experiments show that SynMotion outperforms the latest zero-shot mmWave-based activity recognition method. For skeleton tracking, SynMotion achieves comparable performance to the state-of-the-art mmWave-based method trained on the labeled mmWaves, and SynMotion can further outperform it for the unseen users.

We revisit the problem of iris tracking with RGB cameras, aiming to obtain iris contours from captured images of eyes. We find the reason that limits the performance of the state-of-the-art method in more general non-cooperative environments, which prohibits a wider adoption of this useful technique in practice. We believe that because the iris boundary could be inherently unclear and blocked, as its pixels occupy only an extremely limited percentage of those on the entire image of the eye, similar to the stars hidden in fireworks, we should not treat the boundary pixels as one class to conduct end-to-end recognition directly. Thus, we propose to learn features from iris and sclera regions first, and then leverage entropy to sketch the thin and sharp iris boundary pixels, where we can trace more precise parameterized iris contours. In this work, we also collect a new dataset by smartphone with 22 K images of eyes from video clips. We annotate a subset of 2 K images, so that label propagation can be applied to further enhance the system performance. Extensive experiments over both public and our own datasets show that our method outperforms the state-of-the-art method. The results also indicate that our method can improve the coarsely labeled data to enhance the iris contour’s accuracy and support the downstream application better than the prior method.

This paper presents RF-Mehndi, a passive commercial RFID tag array formed identifier. The key RF-Mehndi novelty is that when the user’s fingertip touching on the tag array surface during the communication, the backscattered signals by the tag array become user-dependent and unique. Hence, if we enhance the communication modality of many personal cards nowadays by RF-Mehndi, in case that a card gets lost or stolen, it cannot be used illegally by the adversaries. To harvest such a benefit, we leverage two key observations in designing RF-Mehndi. The first one is when tags are nearby, their interrogated currents can change each other’s circuit characteristics, based on which unique phase features can be obtained from backscattered signals. The second observation is that when the user’s fingertip touches the tag array surface during communication, the phase feature can be further profiled by this user. Based on these observations, the card and its holder can be potentially authenticated at the same time. To transfer the RF-Mehndi idea to a practical system, we further address technical challenges. We implement a prototype system. Extensive evaluations show the effectiveness of RF-Mehndi, achieving excellent authentication performance.

The vast demand for diverse applications raises new networking challenges, which have encouraged the development of a new paradigm of Internet of Things (IoT), e.g., LoRa. LoRa is a proprietary spread spectrum modulation technique that provides a solution for long-range and ultra-low power-consumption transmission. Due to promising prospects of LoRa, significant effort has been made on this compelling technology since its emergence. In this article, we provide a comprehensive survey of LoRa from a systematic perspective: LoRa analysis, communication, security, and its enabled applications. First, we summarize works focusing on analyzing the performance of LoRa networks. Then, we review studies enhancing the performance of LoRa networks in communication. Afterward, we analyze the security vulnerabilities and countermeasures. Finally, we survey the various LoRa-enabled applications. We also present comparisons of existing methods, together with insightful observations and inspiring future research directions.

Secure Device-to-Device (D2D) communication is becoming increasingly important with the ever-growing number of Internet-of-Things (IoT) devices in our daily life. To achieve secure D2D communication, the key agreement between different IoT devices without any prior knowledge is becoming desirable. Although various approaches have been proposed in the literature, they suffer from a number of limitations, such as low key generation rate and short pairing distance. In this paper, we present InaudibleKey, an inaudible acoustic signal based key generation protocol for mobile devices. Based on acoustic channel reciprocity, InaudibleKey exploits the acoustic channel frequency response of two legitimate devices as a common secret to generating keys. InaudibleKey employs several novel technologies to significantly improve its performance. We conduct extensive experiments to evaluate the proposed system in different real environments. Compared to state-of-the-art works, InaudibleKey improves key generation rate by 3 times, extends pairing distance by 3.2 times, and reduces information reconciliation counts by 2.5 times. Security analysis demonstrates that InaudibleKey is resilient to a number of malicious attacks. We also implement InaudibleKey on modern smartphones and resource-limited IoT devices. Results show that it is energy-efficient and can run on both powerful and resource-limited IoT devices without incurring excessive resource consumption.

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

Mobile phones nowadays are equipped with at least dual microphones. We find when a user is typing on a phone, the sounds generated from the vibration caused by finger's tapping on the screen surface can be captured by both microphones, and these recorded sounds alone are informative enough to infer the user's keystrokes. This ability can be leveraged to enable useful application designs, while it also raises a crucial privacy risk that the private information typed by users on mobile phones has a great potential to be leaked through such a recognition ability. In this paper, we address two key design issues and demonstrate, more importantly alarm people, that this risk is possible, which could be related to many of us when we use our mobile phones. We implement our proposed techniques in a prototype system and conduct extensive experiments. The evaluation results indicate promising successful rates for more than 4000 keystrokes from different users on various types of mobile phones.

We propose Magic Wand which automatically recognizes 2D gestures (e.g., symbol, circle, polygon, letter) performed by users wearing a smartwatch in real-time manner. Meanwhile, users can freely choose their convenient way to perform those gestures in 3D space. In comparison with existing motion sensor based methods, Magic Wand develops a white-box model which adaptively copes with diverse hardware noises and user habits with almost zero overhead. The key principle behind Magic Wand is to utilize 2D stroke sequence for gesture recognition. Magic Wand defines 8 strokes in a unified 2D plane to represent various gestures. While a user is freely performing gestures in 3D space, Magic Wand collects motion data from accelerometer and gyroscope. Meanwhile, Magic Wand removes various acceleration noises and reduces the dimension of 3D acceleration sequences of user gestures. Moreover, Magic Wand develops stroke sequence extraction and matching methods to timely and accurately recognize gestures. We implement Magic Wand and evaluate its performance with 4 smartwatches and 6 users. The evaluation results show that the median recognition accuracy is 94.0% for a set of 20 gestures. For each gesture, the processing overhead is tens of milliseconds.

Secure Device-to-Device (D2D) communication is becoming increasingly important with the ever-growing number of Internet-of-Things (IoT) devices in our daily life. To achieve secure D2D communication, the key agreement between different IoT devices without any prior knowledge is becoming desirable. Although various approaches have been proposed in the literature, they suffer from a number of limitations, such as low key generation rate and short pairing distance. In this paper, we present an inaudible acoustic signal based key generation protocol for mobile devices. Based on acoustic channel reciprocity, our system exploits channel frequency response of two legitimate devices as a common secret to generate keys. Extensive experiments are conducted to evaluate the proposed system in different real environments. Evaluation results show that the proposed system can generate the same secret key for two mobile devices with high probability.

Cyber-security issues on adversarial attacks are actively studied in the field of computer vision with the camera as the main sensor source to obtain the input image or video data. However, in modern cyber-physical systems (CPSs), many other types of sensors are becoming popularly used, such as surveillance sensors, microphones, and textual interfaces. A series of recent works investigates the adversarial attacks and the potential defenses in these noncamera sensor-based CPSs. Therefore, this article provides a systematic discussion on these existing works and serves as a complimentary summary of the adversarial attacks and defenses for CPSs beyond the field of computer vision. We first introduce a general working flow for adversarial attacks on CPSs. On this basis, a clear taxonomy is provided to organize existing attacks effectively and indicate where the defenses can be potentially performed in CPSs as well. Then, we discuss these existing attacks and defenses with detailed comparison studies. Finally, we point out concrete research opportunities to be further explored along this research direction.

Crowdsensing tasks are usually described by certain features or attributes, and the task assignment essentially performs a matching with respect to the worker or user's preference on these features. However, the existing matching strategy could lead to a misaligned task coverage problem, i.e., some popular tasks tend to enter workers' candidate task lists, while some less popular tasks could be always unsuccessfully assigned. To ensure task coverage after the assignment, the system may have to increase their biding costs to reassign such tasks, which causes a high operational cost of the crowdsensing system. To address this problem, we propose to migrate certain qualified workers to the less popular tasks for increasing the task coverage and meanwhile, optimize other performance factors. By doing this, other performance factors, such as task acceptance and quality, can be comparably achieved as recent designs, while the system cost can be largely reduced. Following this idea, this article presents cTaskMat, which learns and exploits workers' task preferences to achieve coverage-ensured task assignments. We implement the cTaskMat design and evaluate its performance using both real-world experiments and data set-driven evaluations, also with the comparison with the state-of-the-art designs.

We revisit a crucial privacy problem in this paper - can the sensitive information, like the numeric passwords and personal data, frequently typed by user on mobile devices be inferred through the motion sensors of wearable device on user's wrist, e.g., smart watch or wrist band? Existing works have achieved the initial success under certain context-aware conditions, such as 1) the horizontal keypad plane, 2) the known keyboard size, and/or 3) the last keystroke on a fixed “enter” button. Taking one step further, the key contribution of this paper is to fully demonstrate, more importantly alarm people, the further risks of typing privacy leakage in much more generalized context-free scenarios, which are related to most of us for the daily usage of mobile devices. We validate this feasibility by addressing a series of unsolved challenges and developing a prototype system aLeak. Extensive experiments show the efficacy of aLeak, which achieves promising successful rates in the attack from more than 500 rounds of different users' typings on various mobile platforms without any context-related information.

In the past decade, unmanned aircraft systems (UASs) have been widely used in various civilian applications, most of which involve only a single unmanned aerial vehicle (UAV). In the near future, more and more UAS applications will be facilitated by the cooperation of multiple UAVs. In such applications, it is desirable to utilize a general control platform for cooperative UAVs. However, existing open-source control platforms cannot fulfill such a demand because (1) they only support the leader-follower mode, which limits the design options for fleet control, (2) existing platforms can support only certain type of UAVs and thus lack compatibility, and (3) these platforms cannot accurately simulate a flight mission, which may cause a big gap between simulation and real-world flight. To address these issues, we propose a general control and monitoring platform for cooperative UAS, namely, CoUAS , which provides a set of core cooperation services of UAVs, including synchronization, connectivity management, path planning, energy simulation, and so on. To verify the applicability of CoUAS, we design and develop a prototype in which an embedded path planning service is provided to complete any task with the minimum flying time while considering the network connectivity and coverage. Experimental results by both simulation and field test demonstrate that the proposed system is viable.

This paper presents RF-Mehndi, a passive commercial RFID tag array formed identifier. The key RF-Mehndi novelty is that when the user's fingertip touching on the tag array surface during the communication, the backscattered signals by the tag array become user-dependent and unique. Hence, if we enhance the communication modality of many personal cards nowadays by RF-Mehndi, in case that a card gets lost or stolen, it cannot be used illegally by the adversaries. To harvest such a benefit, we have two key observations in designing RF-Mehndi. The first observation is when tags are nearby, their interrogated currents can change each other's circuit characteristics, based on which unique phase features can be obtained from backscattered signals. The second observation is that when the user's fingertip touches the tag array surface during communication, the phase feature can be further profiled by this user. Based on these observations, the card and its holder can be potentially authenticated at the same time. To transfer the RF-Mehndi idea to a practical system, we further address technical challenges. We implement a prototype system. Extensive evaluations show the effectiveness of RF-Mehndi, achieving excellent authentication performance.

In this poster, we present our recent work, a wearable system for achieving real-time 3D arm skeleton. We have coped with the major challenge that the skeleton of each arm is determined from the locations of the elbow and wrist, whereas a wearable device only senses a single point from the wrist. Result shows that the potential solution space is huge. This underconstrained nature fundamentally challenges the achievement of accurate and real-time arm skeleton tracking. In this study, we propose Hidden Markov Model (HMM) state reorganization and hierarchical search two methods to improve the heavyweight computation of the state-of-art arm tracking model and achieve real-time tracking even on mobile phone.

This paper presents ArmTroi, a wearable system for understanding and analyzing the detailed arm motions of people primarily by using the motion sensors from wrist-worn wearable devices. ArmTroi can achieve real-time 3D arm skeleton tracking and reliable gesture inference tolerant to missing wearable sensors for enabling numerous useful application designs. We have coped with two major challenges through ArmTroi. First, the skeleton of each arm is determined from the locations of the elbow and wrist, whereas a wearable device only senses a single point from the wrist. We find that the potential solution space is huge. This underconstrained nature fundamentally challenges the achievement of accurate and real-time arm skeleton tracking. Second, wearable sensors may not reliably provide sensory data. For example, devices are not worn by the user, yet the learning tools for gesture inference, such as deep learning, typically have static network structures, which require nontrivial network adaptation to match the input's varying availability and ensure reliable gesture inference. We propose effective techniques to address above challenges, and all computations can be conducted on the user's smartphone. ArmTroi is thus a fully lightweight and portable system. We develop a prototype and extensive evaluation shows the efficacy of the ArmTroi design.

Mobile crowdsourcing (MCS) becomes an emerging paradigm for various useful urban sensing application designs by assigning the crowdsourcing tasks to the participants with rich-sensor equipped mobile devices. To effectively assign MCS tasks, many research efforts have been made in the literature. However, most prior schemes mainly optimize certain performance metrics in the assignment, yet overlooking other metrics, which thus cannot guarantee the overall system performance. This also limits the applicability of the proposed solution dedicated to the targeted performance metrics only. In this paper, we present UniTask, a unified task assignment design to address these issues. UniTask jointly considers the representative MCS performance metrics, including coverage, latency, and accuracy, to optimize the overall system utility. We mathematically formulate this problem and prove its NP-hardness. To efficiently schedule tasks, we also propose a utility-aware heuristic algorithm in UniTask. Moreover, a set of optimization techniques are further designed to enhance UniTask. Extensive evaluations are performed on two real-world datasets. Experimental results demonstrate that utility is an effective indicator of the system's overall performance. With an optimization on the system utility, UniTask can outperform the baseline methods on these individual performance metrics.

This paper presents BodyPIN, which is a continuous user authentication system by contactless wireless sensing using commodity Wi-Fi. BodyPIN can track the current user's legal identity throughout a computer system's execution. In case the authentication fails, the consequent accesses will be denied to protect the system. The recent rich wireless-based user identification designs cannot be applied to BodyPIN directly, because they identify a user's various activities, rather than the user herself. The enforced to be performed activities can thus interrupt the user's operations on the system, highly inconvenient and not user-friendly. In this paper, we leverage the bio-electromagnetics domain human model for quantifying the impact of human body on the bypassing Wi-Fi signals and deriving the component that indicates a user's identity. Then, we extract suitable Wi-Fi signal features to fully represent such an identity component, based on which we fulfill the continuous user authentication design. We implement a BodyPIN prototype by commodity Wi-Fi NICs without any extra or dedicated wireless hardware. We show that BodyPIN achieves promising authentication performances, which is also lightweight and robust under various practical settings.

The ability to accurately track pedestrians is valuable for various application designs. Although pedestrian tracking has been investigated extensively and owns a well-suited sensing platform, the proposed solutions are far from being mature yet. Pedestrian tracking contains step counting and stride estimation two components. Step counting already has commercial products, but the performance is still unreliable and less trustworthy in practice. Stride estimation even stays in the research stage without ready solutions released on the market. Such a non-negligible gap between the long-term research investigation and technique's actual usage exists due to a series of crucial applicability issues unsolved, including design's vulnerability to interfering activities, extracting purely body's movement from mixed sensor signals, and parameter training without user's intervention. In this paper, we deeply analyze human's gait cycles and obtain inspiring observations to address these issues. We incorporate our techniques into existing pedestrian tracking designs and implement a prototype, PTrack, on LG smartwatch. We find that PTrack effectively enhances the system applicability and achieves promising performance under very practical settings.

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.

Mobile sensing is a promising sensing paradigm in the era of Internet of Things (IoT) that utilizes mobile device sensors to collect sensory data about sensing targets and further applies learning techniques to recognize the sensed targets to correct classes or categories. Due to the recent great success of deep learning, an emerging trend is to adopt deep learning in this recognition process, while we find an overlooked yet crucial issue to be solved in this paper - The size of deep learning models should be sufficiently large for reliably classifying various types of recognition targets, while the achieved processing delay may fail to satisfy the stringent latency requirement from applications. If we blindly shrink the deep learning model for acceleration, the performance cannot be guaranteed. To cope with this challenge, this paper presents a compact deep neural network architecture, namely cDeepArch. The key idea of the cDeepArch design is to decompose the entire recognition task into two lightweight sub-problems: context recognition and the context-oriented target recognitions. This decomposition essentially utilizes the adequate storage to trade for the CPU and memory resource consumptions during execution. In addition, we further formulate the execution latency for decomposed deep learning models and propose a set of enhancement techniques, so that system performance and resource consumption can be quantitatively balanced. We implement a cDeepArch prototype system and conduct extensive experiments. The result shows that cDeepArch achieves excellent recognition performance and the execution latency is also lightweight.

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.

In this article, we propose a mobile system, Aware , which turns our wearable or mobile device into a ruler. It can estimate the size of objects that could be large in size and not directly touchable by the user. Such a design will enable a rich set of applications that count on the size information of surrounding environments/objects. Aware purely utilizes the motion sensors on the device for object size measures. It can also integrate with the crowdsourcing feature for both performance improvement and result sharing. We propose a series of key techniques to address three major challenges in the Aware design: (1) user’s angle of line-of-sights to the object is used in the size measure but motion sensors track only the angle of arm’s waving, (2) motion sensors are noisy that require novel and effective data processing techniques, otherwise the errors could easily overwhelm the final result, and (3) in the crowdsourcing mode, Aware needs to identify vicinal objects of similar sizes and effectively fuse the measured sizes that correspond to the same object. We consolidate the above designs and implement Aware on Android platforms. Extensive experiments with four users show that Aware can achieve accurate measurement performance for the objects of various sizes in both indoor and outdoor environments.

We revisit a crucial privacy problem in this paper - can the sensitive information, like the passwords and personal data, frequently typed by user on mobile devices be inferred through the motion sensors of wearable device on user's wrist, e.g., smart watch or wrist band? Existing works have achieved the initial success under certain context-aware conditions, such as 1) the horizontal keypad plane, 2) the known keyboard size, 3) and/or the last keystroke on a fixed “enter” button. Taking one step further, the key contribution of this paper is to fully demonstrate, more importantly alarm people, the further risks of typing privacy leakage in much more generalized context-free scenarios, which are related to most of us for the daily usage of mobile devices. We validate this feasibility by addressing a series of unsolved challenges and developing a prototype system aLeak. Extensive experiments show the efficacy of aLeak, which achieves promising successful rates in the attack from more than 300 rounds of different users' typings on various mobile platforms without any context-related information.

Mobile sensing is a promising sensing paradigm that utilizes mobile device sensors to collect sensory data about sensing targets and further applies learning techniques to recognize the sensed targets to correct classes or categories. Due to the recent great success of deep learning, an emerging trend is to adopt deep learning in this recognition process, while we find an overlooked yet crucial issue to be solved in this paper - The size of deep learning models should be sufficiently large for reliably classifying various types of recognition targets, while the achieved processing delay may fail to satisfy the stringent latency requirement from applications. If we blindly shrink the deep learning model for acceleration, the performance cannot be guaranteed. To cope with this challenge, this paper presents a compact deep neural network architecture, namely cDeepArch. The key idea of the cDeepArch design is to decompose the entire recognition task into two lightweight sub-problems: context recognition and the context-oriented target recognitions. This decomposition essentially utilizes the adequate storage to trade for the CPU and memory resource consumptions during execution. In addition, we further formulate the execution latency for decomposed deep learning models and propose a set of enhancement techniques, so that system performance and resource consumption can be quantitatively balanced. We implement a cDeepArch prototype system and conduct extensive experiments. The result shows that cDeepArch achieves excellent recognition performance and the execution latency is also lightweight.

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.

The ability to accurately track pedestrians is valuable for variant application designs. Although pedestrian tracking has been investigated excessively and owned a well-suited sensing platform, the proposed solutions are far from being mature yet. Pedestrian tracking contains step counting and stride estimation two components. Step counting already has commercial products, but the performance is still unreliable and less trustworthy in practice. Stride estimation even stays in the research stage without ready solutions released on the market. Such a non-negligible gap between long-term research investigation and technique's actual usage exists due to a series of crucial applicability issues unsolved, including design vulnerability to interfering activities, extracting purely body's movement from additive sensor signals, and parameter training without user's intervention. In this paper, we deeply analyze human's gait cycles and obtain inspiring observations to address these issues. We incorporate our techniques into existing pedestrian tracking designs and implement a prototype, PTrack, on LG smartwatch. We find PTrack effectively enhances the system applicability and achieves promising performance under very practical settings.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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

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

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.

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.

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.

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.

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

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 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.

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.

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.