In this paper, GeoFL develops a hierarchical federated learning (FL) framework to address the unique challenges in large-scale geo-distributed scenarios. The key idea is to deploy multiple aggregators to geo-distributed clients and aggregate the local model and the global model efficiently and effectively. By assigning each aggregator as a relay layer, GeoFL can elaborately aggregate the geo-distributed clients and systematically determine when to upload the model to the central server based on bandwidth to efficiently update the global model under inadequate and heterogeneous WAN bandwidth constraints. GeoFL designs three key components to optimize the inefficient model aggregation and cope with the non-importance model updates. It further addresses the statistical heterogeneity across geo-distributed aggregators by considering the clients’ graph relationship, delivering an end-to-end clien-taggregator- server architecture for large-scale clients. Compared with existing works, our results on large-scale real-life datasets show that GeoFL speeds up the training process by 1.4×–8× and reduces 6%–80% unnecessary communication rounds between the aggregator and the central server.

In this paper, we propose LoRaSeek, a lightweight and reliable LoRa denoising framework that enhances signal quality and robustness for neural-enhanced LoRa decoding. LoRaSeek integrates a hybrid architecture combining Convolutional Neural Networks (CNNs), Transformers, and a hierarchical U-Net to effectively capture multi-scale, multidimensional features of LoRa chirp signals. To maintain efficiency, we integrate a lightweight Transformer block that supports various LoRa configurations while keeping computational overhead low. Additionally, we incorporate dual attention-based skip connections to preserve chirp signal properties across different scales. Experiments across diverse LoRa configurations show that LoRaSeek achieves 2.04–3.86 dB signal-to-noise ratio (SNR) gains over standard decoding methods and up to 3.03 dB improvement over state-of-the-art neural-enhanced LoRa decoding methods while reducing model storage by up to 7.4× and inference time by up to 1.6×.

Accurate leaf wetness detection is essential to understanding plant health and growth conditions. The mmWave radar, with its sensitivity to subtle changes, is well-suited for leaf wetness detection. Existing mmWave-based approaches utilize the Synthetic Aperture Radar (SAR) algorithm to generate image-like inputs and rely on multi-modality fusion with an RGB camera to classify leaf wetness. However, the lack of understanding of SAR-based mmWave imaging limits its accuracy in various environments. This paper presents Proteus, a novel way of understanding mmWave SAR imaging. We design a noise reduction algorithm to reduce speckle noise and improve image clarity for SAR-based mmWave imaging. Then, we incorporate phase angle data to enrich SAR texture information to capture high-resolution surface details, increasing informative features for precise wetness assessment in complex plant structures. Additionally, we introduce a cross-modality Teacher-Student network, using an RGB-based teacher model to guide the mmWave SAR-based student model for feature extraction. This network transfers the explicit knowledge in the RGB image domain to the mmWave image domain. We use commercial-off-the-shelf mmWave radar to prototype Proteus. The evaluation results show that Proteus achieves up to 96.3% accuracy across varied environmental scenarios, outperforming state-of-the-art methods.

The integration of the Internet of Things (IoT) and modern Artificial Intelligence (AI) has given rise to a new paradigm known as the Artificial Intelligence of Things (AIoT). In this survey, we provide a systematic and comprehensive review of AIoT research. We examine AIoT literature related to sensing, computing, and networking & communication, which form the three key components of AIoT. In addition to advancements in these areas, we review domain-specific AIoT systems that are designed for various important application domains. We have also created an accompanying GitHub repository, where we compile the papers included in this survey: https://github.com/AIoT-MLSys-Lab/AIoT-Survey. This repository will be actively maintained and updated with new research as it becomes available. As both IoT and AI become increasingly critical to our society, we believe that AIoT is emerging as an essential research field at the intersection of IoT and modern AI. It is our hope that this survey will serve as a valuable resource for those engaged in AIoT research and act as a catalyst for future explorations to bridge gaps and drive advancements in this exciting field.

In this study, we propose BreathPass, a non-invasive authentication system that characterizes the chest/abdomen movement incurred by human breath to enable unlocking smart devices while wearing various types of face covers, clothing, in different postures, and dynamic status such as walking or running. To capture the breathing pattern, BreathPass uses speakers to emit ultrasound signals. The signals are reflected off the chest wall and abdomen and then back to the microphone, which records the reflected signals. The system then extracts the breathing pattern from the reflected signals, and further extracts fingerprints from the breathing pattern, and use these fingerprints to perform authentication. We carefully design a Deep Neural Network model and explore its capacity for feature abstraction in order to address the challenges associated with tiny position changes resulting in different breathing patterns and the extremely narrow bandwidth of breathing. We implement a prototype and conduct extensive experiments. BreathPass achieves an overall accuracy of 83%, a true positive rate of 73%, and a false positive rate of 5%, according to performance evaluation results.

With the increasing popularity of voice assistants like Amazon Echo, Google Home, and Apple HomePod, user localization via the voice command has become a highly desired feature. However, there are three challenges in achieving accurate user localization via speech. First, voice assistants lack prior knowledge of the speech spectrum, making it difficult for them to perceive distance. Second, the signal captured by microphones are pretty noisy due to hardware noise, environmental noise and multipath effect. Third, voice assistants require a system that incurs no additional deployment costs or prerequisite operations. To solve the challenges, we propose SoundFlower. Our approach extracts Time Difference of Arrival from phase data of cross spectrum to derive 3D location. To mitigate internal and environmental noises, we design a self-adjusting speech detection method to recognize speech-involved phase data. Additionally, robust regression is employed to extract TDoA from phase data against multipath effect. During our experiments, we show SoundFlower is robust and efficient.

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

Leaf Wetness Duration (LWD), the time that water remains on leaf surfaces, is crucial in the development of plant diseases. Existing LWD detection lacks standardized measurement techniques, and variations across different plant characteristics limit its effectiveness. Prior research proposes diverse approaches, but they fail to measure real natural leaves directly and lack resilience in various environmental conditions. This reduces the precision and robustness, revealing a notable practical application and effectiveness gap in real-world agricultural settings. This paper presents Hydra, an innovative approach that integrates millimeter-wave (mm-Wave) radar with camera technology to detect leaf wetness by determining if there is water on the leaf. We can measure the time to determine the LWD based on this detection. Firstly, we design a Convolutional Neural Network (CNN) to selectively fuse multiple mm-Wave depth images with an RGB image to generate multiple feature images. Then, we develop a transformer-based encoder to capture the inherent connection among the multiple feature images to generate a feature map, which is further fed to a classifier for detection. Moreover, we augment the dataset during training to generalize our model. Implemented using a frequency-modulated continuous-wave (FMCW) radar within the 76 to 81 GHz band, Hydra's performance is meticulously evaluated on plants, demonstrating the potential to classify leaf wetness with up to 96% accuracy across varying scenarios. Deploying Hydra in the farm, including rainy, dawn, or poorly light nights, it still achieves an accuracy rate of around 90%.

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

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

Low Power Wide-Area Network (LPWAN) has shown its long-distance and low-power features for aboveground Internet-of-Things (IoT) communication, presenting a potential to extend to underground cross-soil communication over a wide area, which has not been investigated before. The variation of soil conditions brings significant signal polarization misalignment, degrading communication reliability. We propose Demeter, a low-cost, low-power programmable antenna design to keep reliable cross-soil communication automatically. First, we propose a hardware architecture to enable polarization adjustment on commercial-off-the-shelf (COTS) single-RF-chain LoRa radio. Moreover, we develop a low-power programmable circuit to adjust polarization. We further design an energy-efficient heuristic calibration algorithm to keep signal polarization alignment automatically. We demonstrate Demeter in indoor environments. The antenna is buried in a plastic container filled with gardening soil to simulate the node underground. Meanwhile, we use the COTS LoRa gateway as a receiver to show the RSSI and SNR variations.

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

With the advancement of AI-powered personal voice assistants, speaker authentication via earbuds has become increasingly vital, serving as a critical interface between users and mobile devices. However, existing audio-based speaker authentication methods fail to defend against voice spoofing threats such as replay and deep-fake attacks. To counteract these risks, we introduce PiezoBud, a pioneering multi-modal user authentication system that is truly practical and lightweight for earbuds. PiezoBud uses miniature piezoelectric sensors to detect micro-vibrations on the skin, extracting user-specific biometric data to authenticate legitimate access on the local smartphone and protect against malicious attacks. Our exploratory study, involving 85 participants, demonstrates the effectiveness of PiezoBud in various everyday scenarios, including ambient noise, body movement, and in-ear media playing. Using only 15 seconds of enrollment data, PiezoBud achieves an Equal Error Rate (EER) of 1.05% and attain a mean authentication latency of 0.06 seconds on mobile devices. We also evaluate PiezoBud's effectiveness in countering challenging adaptive attack scenarios and its overall performance in various real-world situations. Our evaluation highlights that PiezoBud stands out as a practical, resilient, responsive, and secure option for earbuds users.

Passive human tracking using Wi-Fi has been researched broadly in the past decade. Besides straightforward anchor point localization, velocity is another vital sign adopted by the existing approaches to infer user trajectory. However, state-of-the-art Wi-Fi velocity estimation relies on Doppler-Frequency-Shift (DFS), which suffers from the inevitable signal noise incurring unbounded velocity errors, further degrading the tracking accuracy. In this article, we present WiVelo, which explores new spatial-temporal signal correlation features observed from different antennas to achieve accurate velocity estimation. First, we use subcarrier shift distribution (SSD) extracted from channel state information (CSI) to define two correlation features for direction and speed estimation, separately. Then, we design a mesh model calculated by the antennas’ locations to enable a fine-grained velocity estimation with bounded direction error. Finally, with the continuously estimated velocity, we develop an end-to-end trajectory recovery algorithm to mitigate velocity outliers with the property of walking velocity continuity. We implement WiVelo on commodity Wi-Fi hardware and extensively evaluate its tracking accuracy in various environments. The experimental results show our median and 90-percentile tracking errors are 0.47 m and 1.06 m, which are half and a quarter of state-of-the-art. The datasets and source codes are published through Github ( https://github.com/research-source/code ).

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

Leaf wetness detection is one of the key technologies for preventing plant diseases in agriculture. In this poster, we propose mmLeaf, leveraging a commercial off-the-shelf millimeter-wave (mmWave) radar to detect actual leaf wetness in diverse environments and lighting conditions. mmLeaf captures mmWave signals reflected by monitored leaves with a two-dimensional (2D) scanning system. Then, we use a multiple-input multiple-output (MIMO) array and synthetic aperture radar (SAR) to reconstruct the signal distribution of different planes of the leaves. A deep learning model takes the fused signal distribution as inputs to classify the leaf wetness. We implement mmLeaf using a frequency-modulated continuous-wave (FMCW) radar and evaluate its performance with a potted plant indoors. By exploring the use of mmWave signals, mmLeaf delivers an end-to-end detection framework that achieves up to 90% accuracy in classifying leaf wetness under different distances.

Recent years have shown substantial interest in revealing vulnerability issues of voice-controllable systems on smartphones and smart speakers. While significant prior works have leveraged inaudible signals to attack these smart devices, smart earbuds present unique challenges and vulnerabilities due to their extreme hardware constraints. In this paper, we present EchoAttack, a practical inaudible attack system for smart earbuds. The primary innovation of EchoAttack is the ability to leverage both indirect and direct paths to attack smart earbuds. To search for the optimal path, we design a path-searching algorithm based on the attenuation model of ultrasound. We also propose a novel approach to remove harmonics noise, which improves the attacking signal's SNR further. Finally, we propose using Zigbee radios to sniff the Bluetooth signal and enable a hidden feedback channel without the victim's awareness. We implement the EchoAttack prototype using off-the-shelf hardware components and evaluate the prototypes in four typical indoor and outdoor scenarios using six smart earbuds. Experimental results show that EchoAttack outperforms the pure direct-path attack by 75.8% on average in terms of attack success rate.

For energy constraint wireless IoT nodes, their radios usually operate in duty cycle mode. With low maintenance and negotiation cost, asynchronous duty cycle radio management is widely adopted. To achieve fast network flooding is challenging in asynchronous duty cycle networks. Recently, concurrent flooding, which allows a set of nodes (called concurrent senders ) to immediately broadcast the received packet without any backoff, is a promising approach to improve the flooding speed. We observe that selecting either large or small number of concurrent senders cannot achieve the optimal flooding speed in different deployments. There is a tradeoff between the degradation of concurrent broadcast efficiency and the missing of early receiving chance. In this article, we propose COFlood (Concurrent Opportunistic Flooding), a practical and efficient concurrent flooding protocol in asynchronous duty cycle networks. First, COFlood constructs a concurrent flooding tree in distributed manner. The non-leaf nodes are selected as concurrent senders and they can cover the entire network while reserving the most capacity of concurrent broadcast for later added opportunistic concurrent senders. Moreover, we find that exploiting both early wake-up nodes and long lossy links can speed up the concurrent flooding tree-based network flooding by increasing the early receiving chances. Then, COFlood develops a lightweight method to select the nodes that meet the conditions of these two opportunities as opportunistic concurrent senders. We implement COFlood in TinyOS and evaluate it on two real testbeds. In comparison with state-of-the-art concurrent flooding protocol, completion time and energy consumption can be reduced by up to 35.3% and 26.6%.

In this article, we explore a new design paradigm of duty-cycling mechanism that supports low-power devices to fully turn channel contention into transmission opportunities. To achieve this goal, we propose Concurrent Low-power Listening (CLPL) to enable contention-tolerant and concurrent media access control (MAC) for widely deployed low-power devices. The fundamental principle behind CLPL is that frequency modulated receiver can reliably demodulate the strongest signal even if cochannel interference and noise exist. By using CLPL, a sender inserts a series of tailor-made signals (namely, wake-up signal) between adjacent data frames to awaken appointed receiver, making it capable to receive the next data frame. According to system-defined maximum transmission power level, CLPL adopts an adaptive algorithm to adjust the transmission power of wake-up signals so that its signal strength is above receiver sensitivity and will not interfere with the other data frames in transit. By exploiting the spatial-temporal correlation, we further develop a light-weight wake-up signal detection method to enable a waiting sender to accurately identify the current channel condition. Then, it schedules the sender’s data frame transmissions by overlapping with those wake-up signals, without conflicting with existing data frame transmissions. We have implemented the prototype of CLPL and conducted extensive experiments on a real testbed. In comparison with the state-of-the-art low-power MAC schemes, such as ContikiMAC, A-MAC, BoX-MAC, and opportunistic scheme ORW, CLPL can improve the throughput by 2–6 times and halve the end-to-end transmission delay.

In this paper, we propose NEC (Neural Enhanced Cancellation), a defense mechanism, which prevents unautho-rized microphones from capturing a target speaker’s voice. Compared with the existing scrambling-based audio cancellation approaches, NEC can selectively remove a target speaker’s voice from a mixed speech without causing interference to others. Specifically, for a target speaker, we design a Deep Neural Network (DNN) model to extract high-level speaker-specific but utterance-independent vocal features from his/her reference audios. When the microphone is recording, the DNN generates a shadow sound to cancel the target voice in real-time. Moreover, we modulate the audible shadow sound onto an ultrasound frequency, making it inaudible for humans. By leveraging the non-linearity of the microphone circuit, the microphone can accurately decode the shadow sound for target voice cancellation. We implement and evaluate NEC comprehensively with 8 smartphone microphones in different settings. The results show that NEC effectively mutes the target speaker at a microphone without interfering with other users’ normal conversations.

Internet-of-Things (IoT) aims to connect billions of low-date rate and energy-constrained end-devices in the near future. Although many IoT systems have been commercialized, most of them focus on home and body scale applications. To establish a low-cost IoT at the city scale, LoRa Wide Area Networks (LoRaWAN) have become attractive in recent years due to their desirable kilometer or even longer communication distance with low energy consumption. However, due to the expensive cost of densely deploying end-nodes, the understanding of LoRa link behavior is still coarse-grained, and hard to fully realize the link dynamics, networking coverage, and localization accuracy of LoRaWAN in an urban environment. This paper shows a fine-grained LoRa link-level measurement via mobile end-nodes. We deploy two gateways and six mobile end-nodes and collect data packets over four months at a$6\times 6\ km^{2}$urban area. The evaluation mainly focuses on answering three questions: 1) Does a LoRa link stably perform in both spatial and temporal dimensions? 2) How large area can be covered for reliable communication by each gateway in the urban environment? 3) What accuracy can be achieved to localize an end-node through LoRa links? According to our measurement, our key findings are 1) The spatial and temporal behavior of LoRa links is quite dynamic due to the different types of land covers and the frequent micro-environment changes in the urban areas; 2) Each gateway can cover about 11.3 km2area and marginal SNR gains (e.g., 2 dB) of LoRa links are efficient enough to enlarge 32.6% coverage area of a gateway; and 3). The median localization error is about 400 m. Without densely deployed LoRa gateways, the SOTA LoRa localization can support road-level localization, even when an end node is close to one of the gateways.

Long Range Wide Area Network (LoRaWAN), using the linear chirp for data modulation, is known for its low-power and long-distance communication to connect massive Internet-of-Things devices at a low cost. However, LoRaWAN throughput is far behind the demand for the dense and large-scale IoT deployments, due to the frequent collisions with the by-default random channel access (i.e., ALOHA). Recently, some works enable an effective LoRa carrier-sense for collision avoidance. However, the continuous back-off makes the network throughput easily saturated and degrades the energy efficiency at LoRa end nodes. In this paper, we propose CurveALOHA, a brand-new media access control scheme to enhance the throughput of random channel access by embracing non-linear chirps enabled quasi-orthogonal logical channels. First, we empirically show that non-linear chirps can achieve similar noise tolerance ability as the linear one does. Then, we observe that multiple nonlinear chirps can create new logical channels which are quasi-orthogonal with the linear one and each other. Finally, given a set of non-linear chirps, we design two random chirp selection methods to guarantee an end node can access a channel with less collision probability. We implement CurveALOHA with the software-defined radios and conduct extensive experiments in both indoor and outdoor environments. The results show that CurveALOHA’s network throughput is 59.6% higher than the state-of-the-art carrier-sense MAC.

Federated learning (FL) is an emerging distributed machine learning (ML) paradigm with enhanced privacy, aiming to achieve a "good" ML model for as many as participants while consuming as little as wall clock time. By executing across thousands or even millions of clients, FL demonstrates heterogeneous statistical characteristics and system divergence widely across participants, making its training suffer when adopting the traditional ML paradigm. The root cause of the training efficiency degradation is the random client selection criteria. Although existing FL paradigms propose several optimization schemes for client selection, they are still coarse-grained due to their under-exploitation on the clients' data and system heterogeneity, yielding sub-optimal performance for a variety of FL applications. In this paper, we propose PyramidFL1 to speed up the FL training while achieving a higher final model performance (i.e., time-to-accuracy). The core of PyramidFL is a fine-grained client selection, in which PyramidFL does not only focus on the divergence of those selected participants and non-selected ones for client selection but also fully exploits the data and system heterogeneity within selected clients to profile their utility more efficiently. Specifically, PyramidFL first determines the utility-based client selection from the global (i.e., server) view and then optimizes its utility profiling locally (i.e., client) for further client selection. In this way, we can prioritize the use of those clients with higher statistical and system utility consistently. In comparison with the state-of-the-art (i.e., Oort), our evaluation on the open-source FL benchmark shows that PyramidFL improves the final model accuracy by 3.68% -- 7.33%, with a speedup of 2.71 x -- 13.66X on the wall clock time consumption.

Passive human tracking via Wi-Fi has been re-searched broadly in the past decade. Besides straight-forward anchor point localization, velocity is another vital sign adopted by the existing approaches to infer user trajectory. However, state-of-the-art Wi-Fi velocity estimation relies on Doppler-Frequency-Shift (DFS) which suffers from the inevitable signal noise incurring unbounded velocity errors, further degrading the tracking accuracy. In this paper, we present WiVelo11Code&datasets are available at https://github.com/liecn/WiVelo_SECON22 that explores new spatial-temporal signal correlation features observed from different antennas to achieve accurate velocity estimation. First, we use sub carrier shift distribution (SSD) extracted from channel state information (CSI) to define two correlation features for direction and speed estimation, separately. Then, we design a mesh model calculated by the antennas' locations to enable a fine-grained velocity estimation with bounded direction error. Finally, with the continuously estimated velocity, we develop an end-to-end trajectory recovery algorithm to mitigate velocity outliers with the property of walking velocity continuity. We implement WiVelo on commodity Wi-Fi hardware and extensively evaluate its tracking accuracy in various environments. The experimental results show our median and 90% tracking errors are 0.47 m and 1.06 m, which are half and a quarter of state-of-the-arts.

User identified gesture recognition is a fundamental step towards ubiquitous WiFi based sensing. We propose WiHF, which first simultaneously enables cross-domain gesture recognition and user identification using commodity WiFi in a real-time manner. The basic idea of WiHF is to derive a domain-independent motion change pattern of arm gestures from WiFi signals, rendering the unique gesture characteristics and the personalized user performing styles. To extract the motion change pattern in real time, we develop an efficient method based on the seam carving algorithm. Moreover, taking as input the motion change pattern, a deep neural network (DNN) is adopted for both gesture recognition and user identification tasks. In DNN, we apply splitting and splicing schemes to optimize collaborative learning for dual tasks. We implement WiHF and extensively evaluate its performance on a public dataset including 6 users and 6 gestures performed across 5 locations and 5 orientations in 3 environments. Experimental results show that WiHF achieves 97.65 and 96.74 percent for in-domain gesture recognition and user identification accuracy, respectively. The cross-domain gesture recognition accuracy is comparable with the state-of-the-art method, but the processing time is reduced by 30×.

The screens of our smartphones and laptops display our private information persistently. The term “shoulder surfing” refers to the behavior of unauthorized people peeking at our screens, easily causing severe privacy leakages. Many countermeasures have been used to prevent naked eye-based peeking by reducing the possible peeking distance. However, the risk from modern smartphones with powerful cameras is underestimated. In this paper, we propose SRPeek, a long-distance shoulder surfing attack method using smartphones. Our key observation is that although a single image captured by smartphone cameras is blurred, the attacker can leverage super-resolution (SR) techniques to recover the information from multiple blurry images. We design an end-to-end system deployed on commercial smartphones, including an innovative deep neural network (DNN) architecture, StARe, for efficient multi-image SR. We implement SRPeek in Android and conduct extensive experiments to evaluate its performance. The results demonstrate we can recognize 90% of characters at a distance of 6m with telephoto lenses and 1.8m with common lenses, calling for the vigilance of the Quietly growing shoulder surfing threat.

LoRa (Long Range) is an emerging wireless technology that enables long-distance communication and keeps low power consumption. Therefore, LoRa plays a more and more important role in Low-Power Wide-Area Networks (LPWANs), which easily extend many large-scale Internet of Things (IoT) applications in diverse scenarios (e.g., industry, agriculture, city). In lots of environments where various types of land-covers usually exist, it is challenging to precisely predict a LoRa link's path loss. As a result, how to deploy LoRa gateways to ensure reliable coverage and develop precise fingerprint-based localization becomes a difficult issue in practice. In this paper, we propose DeepLoRa, a deep learning-based approach to accurately estimate the path loss of long-distance links in complex environments. Specifically, DeepLoRa relies on remote sensing to automatically recognize land-cover types along a LoRa link. Then, DeepLoRa utilizes Bi-LSTM (Bidirectional Long Short Term Memory) to develop a land-cover aware path loss model. We implement DeepLoRa and use the data gathered from a real LoRaWAN deployment on campus to evaluate its performance extensively in terms of estimation accuracy and model transferability. The results show that DeepLoRa reduces the estimation error to less than 4 dB, which is 2× smaller than state-of-the-art models.

For energy constrained wireless IoT nodes, their radios usually operate in duty cycle mode. With low maintenance and negotiation cost, asynchronous duty cycle radio management is widely adopted. To achieve fast network flooding is challenging in asynchronous duty cycle networks. Recently, concurrent flooding is a promising approach to improve the performance of network flooding. In concurrent flooding, a key challenge is how to select a set of concurrent senders to improve both flooding speed and energy efficiency. We observe that selecting neither large nor small number of concurrent senders can achieve the optimal performance in different deployments. In this paper, we propose COFlood (Concurrent Opportunistic Flooding), a practical and effective concurrent flooding protocol in asynchronous duty cycle networks. The basic idea is based on an energy-efficient flooding tree, COFlood opportunistically selects extra concurrent senders that can speed up network flooding. First, COFlood constructs an energy-efficient flooding tree in distributed manner. The non-leaf nodes are selected as senders and they can cover the entire network with low energy consumption. Moreover, we find that exploiting both early wakeup nodes and long lossy links can speed up the flooding tree based network flooding. Then, COFlood develops a light-weight method to select the nodes that meet the conditions of these two opportunities as opportunistic senders. We implement COFlood in TinyOS and evaluate it on two real testbeds. In comparison with state-of-the-art concurrent flooding protocol, completion time and energy consumption can be reduced by up to 35.3% and 26.6%.

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

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

Due to limited energy supply on many Internet of Things (IoT) devices, asynchronous duty cycle radio management is widely adopted to save energy. Flooding is a critical way to disseminate messages through the whole network. Capture effect enabled concurrent broadcast is appealing to accelerate network flooding in asynchronous duty cycle networks. However, when the flooding payload's size is large, the concurrent broadcast performance is far from efficient due to the frequently unsatisfied capture effect. Intuitively, senders can send a short packet containing partial flooding payload to keep concurrent broadcast efficiency. In practice, we still face two challenges. Considering packet loss, a receiver needs an effective way to recover the entire flooding payload from several received packets as soon as possible. Moreover, considering different channel states of different senders, how a sender chooses the optimal packet length to guarantee high channel utilization is not easy. In this paper, we propose Chase++ a Fountain-code based concurrent broadcast control layer to enable fast flooding in asynchronous duty cycle networks. Chase++ uses Fountain code to alleviate the negative influence of a certain part of the flooding payload's continuous loss. Moreover, Chase++ adaptively selects packet length with the local estimation of channel utilization. Specifically, Chase++ partitions long payload into several short payload blocks, further encoded into many encoded payload blocks by Fountain-code. Then, with temporal and spatial features of the sampled RSS (received signal strength) sequence, a sender estimates the number of concurrent senders. Finally, according to the estimated number of concurrent senders, the sender determines the optimal number of encoded payload blocks in a packet and assembles the encoded payload blocks as lots of packets. Then, the concurrent broadcast layer continuously transmits these packets. Receivers can recover the original flooding payload after several independent encoded payload blocks are collected. We implement Chase++ in TinyOS with TelosB nodes. We further evaluate Chase++ on Local testbed with 50 nodes and Indriya testbed with 95 nodes. The improvement of network flooding speed can reach 23.6% and 13.4%, respectively.

In a large-scale wireless sensor network, hundreds and thousands of sensors sample and forward data back to the sink periodically. In two real outdoor deployments GreenOrbs and CitySee, we observe that some bottleneck nodes strongly impact other nodes’ data collection and thus degrade the whole network performance. To figure out the importance of a node in the process of data collection, system manager is required to understand interactive behaviors among the parent and child nodes. So we present a management tool BOND (BOttleneck Node Detector), which explains the concept of Node Dependence to characterize how much a node relies on each of its parent nodes, and also models the routing process as a Hidden Markov Model and then uses a machine learning approach to learn the state transition probabilities in this model. Moreover, BOND can predict the network dataflow if some nodes are added or removed to avoid data loss and flow congestion in network redeployment. We implement BOND on real hardware and deploy it in an outdoor network system. The extensive experiments show that Node Dependence indeed help to explore the hidden bottleneck nodes in the network, and BOND infers the Node Dependence with an average accuracy of more than 85%.

User identified gesture recognition is a fundamental step towards ubiquitous device-free sensing. We propose WiHF, which first simultaneously enables cross-domain gesture recognition and user identification using WiFi in a real-time manner. The basic idea of WiHF is to derive a cross-domain motion change pattern of arm gestures from WiFi signals, rendering both unique gesture characteristics and the personalized user performing styles. To extract the motion change pattern in realtime, we develop an efficient method based on the seam carving algorithm. Moreover, taking as input the motion change pattern, a Deep Neural Network (DNN) is adopted for both gesture recognition and user identification tasks. In DNN, we apply splitting and splicing schemes to optimize collaborative learning for dual tasks. We implement WiHF and extensively evaluate its performance on a public dataset including 6 users and 6 gestures performed across 5 locations and 5 orientations in 3 environments. Experimental results show that WiHF achieves 97.65% and 96.74% for in-domain gesture recognition and user identification accuracy, respectively. The cross-domain gesture recognition accuracy is comparable with the state-of-the-art methods, but the processing time is reduced by 30×.

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.

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

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

Concurrent transmission (CT) has been widely adopted to optimize the throughput of various data transmissions in wireless networks, such as bulk data dissemination and high-rate data collection. In CT, besides the possible data frame collision at receivers, we observe that acknowledgment frame (ACK) collision at senders can also significantly diminish concurrency opportunities. In this article, to avoid the potential ACK collision in CT, we propose ALIGNER which develops a new transmission pattern to coordinate concurrent senders in a distributed manner. The key idea is to align the silent periods of concurrent transmitters. To achieve this goal, we align the end of data frames concurrently transmitted by several senders. Therefore, the potentially arriving ACKs can avoid a collision with ongoing data transmissions because the concurrent senders are in a listening state to wait for receivers’ ACKs for a short and fixed period. ALIGNER can be applied for both deterministic and opportunistic forwarding protocols. It optionally uses a random back-off and slotted ACK mechanism to avoid a potential collision among simultaneously arrived ACKs in opportunistic forwarding. In addition, ALIGNER adopts a tailor-made metrics to analyze the throughput benefit of concurrent transmission for both deterministic and opportunistic data collection protocols. We have implemented ALIGNER in TinyOS and conducted extensive experiments on a real testbed. Experimental results show that ALIGNER can significantly increase the concurrency opportunities in both deterministic (up to 105%) and opportunistic (up to 89.7%) forwarding compared with the state-of-the-art CT methods.

Taxi-sharing allows occupied taxis to pick up new passengers on the fly, promising to reduce waiting time for taxi riders and increase productivity for drivers. However, it becomes more difficult to strike the balance between a driver’s profit and a passenger’s quality of service (QoS). In this article, we propose QA-Share, a QoS-aware taxi-sharing system, by addressing two important challenges. First, QA-Share maximizes driver profit and user experience at the same time. Second, QA-Share optimizes these two metrics by dynamically adapting its schedule as new requests arrive. To address these two challenges, we formulated the optimization problem using integer linear programming and derived the optimal solution under a small system scale. Moreover, we also designed a heuristic algorithm to deal with the situation where more passenger requests for taxi service come at the same time. We evaluate our approach with a real-world dataset in a Chinese city—Zhenjiang—that contains the GPS traces recorded by more than 3,000 taxis during a period of 3 months. The results show that both QoS and profit increase by 38% compared to the current schemes. Moreover, as the first study that has conducted simulations with real traces with a population of 3 million and 3,000 taxis, we prove that taxi-sharing is a viable approach in a medium-size city.

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

The energy efficiency and delivery robustness are two critical issues for low duty-cycled wireless sensor networks. The asynchronous receiver-initiated duty-cycling media access control (MAC) protocols have shown their effectiveness through various studies. In receiver-initiated MACs, packet transmission is triggered by the probe of receiver. However, it suffers from the performance degradation incurred by packet collision, especially under bursty traffic. Several protocols have been proposed to address this problem, but their performance is restricted by the unnecessary backoff time and long negotiation process. In this article, we present CD-MAC, an energy-efficient and robust contention-detectable mechanism for addressing the collision-catching problem in receiver-initiated MACs. By exploring the temporal diversity of the acknowledgments, a receiver recognizes the potential senders and subsequently polls individual senders one by one. On that basis, CD-MAC can successfully avoid packet collision even though multiple senders have data packets to transmit to the same receiver. We implement CD-MAC in TinyOS and evaluate its performance on an indoor testbed with single-hop and multi-hop network scenarios. The results show that CD-MAC can significantly improve throughput by 1.72 times compared with the state-of-the-art receiver-initiated MAC protocol under bursty traffic loads. The results also demonstrate that CD-MAC can effectively mitigate the influence of hidden terminal problem and adapt to network dynamics well.

Due to limited energy supply of Internet of Things (Zhao et al. 2018) (IoT) devices, asynchronous duty cycle radio management is widely adopted to save energy. Since the sleep schedules of nodes are unsynchronized, a sender has to repeatedly send frames to coordinate with its receiver or keep sleeping until the receiver’s wake-up time will come according to receiver’s sleep-wake schedule. In such contexts, opportunistic forwarding, which takes the earliest forwarding opportunity instead of a deterministic forwarder, shows great advantage in utilizing channel resource for duty-cycled IoT networks. The multiple forwarding choices with temporal and spatial diversity increase the chance of collision tolerance in opportunistic forwarding, potentially enhancing the overall performance of duty-cycled multi-hop networks. However, since the current channel contention mechanisms mainly focus on collision avoidance, it is too conservative to exploit concurrency. To address this problem, in this article, we propose COF to fully exploit the potential Concurrency for Opportunistic Forwarding in duty-cycled IoT networks. COF achieves concurrent transmission by: (i) measuring conditional link quality under the interference of on-going transmissions, and then (ii) further modeling the benefit of potential concurrency opportunities. According to the expected benefit of concurrency, COF decides whether or not to transmit in concurrent way. COF also adopts concurrency flag and signal features to avoid data collision caused by disordered concurrent transmissions and enhance the accuracy of conditional link quality estimation. COF can be easily integrated into the conventional unsynchronized and duty-cycled protocols. We have implemented COF and evaluated its performance on a 40-node testbed. The results show that COF can effectively exploit potential concurrency in opportunistic forwarding and COF outperforms the state-of-art protocols under diverse traffic load and network density.

Due to limited energy supply on many Internet of Things (IoT) devices, asynchronous duty cycle radio management is widely adopted to save energy. Flooding is a critical way to quickly disseminate system parameters to adapt diverse network requirements. Capture effect enabled concurrent broadcast is appealing to accelerate network flooding in asynchronous duty cycle networks. However, when the length of flooding payload is long, due to frequently unsatisfied capture effect construction, the performance of concurrent broadcast is far from efficient. Intuitively, senders can send short packet that contains partial flooding payload to keep the efficiency of concurrent broadcast. In practice, we still face two challenges. Considering packet loss, a receiver needs an effective way to recover entire flooding payload from several received packets as soon as possible. Moreover, considering diverse channel state of different senders, how a sender chooses the optimal packet length to guarantee high channel utilization in a light-weight way is not easy. In this paper, we propose Chase++ a Fountain code based concurrent broadcast control layer to enable fast flooding in asynchronous duty cycle networks. Chase++ uses Fountain code to alleviate the negative influence of the continuous loss of a certain part of flooding payload. Moreover, Chase++ adaptively selects packet length with the local estimation of channel utilization. Specifically, Chase++ partitions long payload into several short payload blocks, which are further encoded into many encoded payload blocks by Fountain code. Then, with temporal and spatial features of the sampled RSS (received signal strength) sequence, a sender estimates the number of concurrent senders. Finally, according to the estimated number of concurrent senders, the sender determines the optimal number of encoded payload blocks in a packet and assembles the encoded payload blocks as lots of packets. Then, concurrent broadcast layer continuously transmits these packets. Receivers can recover original flooding payload after several independent encoded payload blocks are collected. We implement Chase++ in TinyOS with TelosB nodes. We further evaluate Chase++ on local testbed with 50 nodes and Indriya testbed with 95 nodes. The improvement of network flooding speed can reach 23.6% and 13.4%, respectively.

Device-to-device (D2D) communication is widely used for mobile devices and Internet of Things (IoT). Authentication and key agreement are critical to build a secure channel between two devices. However, existing approaches often rely on a pre-built fingerprint database and suffer from low key generation rate. We present GeneWave, a fast device authentication and key agreement protocol for commodity mobile devices. GeneWave first achieves bidirectional initial authentication based on the physical response interval between two devices. To keep the accuracy of interval estimation, we eliminate time uncertainty on commodity devices through fast signal detection and redundancy time cancellation. Then we derive the initial acoustic channel response (ACR) for device authentication. We design a novel coding scheme for efficient key agreement while ensuring security. Therefore, two devices can authenticate each other and securely agree on a symmetric key. GeneWave requires neither special hardware nor pre-built fingerprint database, and thus it is easy-to-use on commercial mobile devices. We implement GeneWave on mobile devices (i.e., Nexus 5X and Nexus 6P) and evaluate its performance through extensive experiments. Experimental results show that GeneWave efficiently accomplish secure key agreement on commodity smartphones with a key generation rate 10x faster than the state-of-the-art approach.

Though outdoor navigation has been used for a long time, indoor navigation has not yet been put into practice. Recent works based on geomagnetic information require a user to follow the exact path of previous users, which limits its applicability. Meanwhile, the optimal navigation path may not be provided given a large number of paths with different sources and destinations. We present MeshMap, an efficient indoor navigation system by merging geomagnetic information of crowdsourcing paths. MeshMap combines geomagnetic information from multiple users' paths to construct a global navigation map. Then, MeshMap supports to find the optimal navigation path on the navigation map. Finally, MeshMap proposes a real-time tracking method to map user's position on the navigation path and provides real-time navigation hints. We implement MeshMap on Android and evaluate its performance in different environments including campus building, parking area and shopping mall. The evaluation results show the effectiveness of MeshMap.

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

Asynchronous duty cycle is widely used for energy constraint wireless nodes to save energy. The basic flooding service in asynchronous duty cycle networks, however, is still far from efficient due to severe packet collisions and contentions. We present Chase, an efficient and fully distributed concurrent broadcast layer for flooding in asynchronous duty cycle networks. The main idea of Chase is to meet the strict signal time and strength requirements (e.g., Capture Effect) for concurrent broadcast while reducing contentions and collisions. We propose a distributed random inter-preamble packet interval adjustment approach to constructively satisfy the requirements. Even when requirements cannot be satisfied due to physical constraints (e.g., the difference of signal strength is less than a 3 dB), we propose a lightweight signal pattern recognition-based approach to identify such a circumstance and extend radio-on time for packet delivery. We implement Chase in TinyOS with TelosB nodes and extensively evaluate its performance. The implementation does not have any specific requirement on the hardware and can be easily extended to other platforms. The evaluation results also show that Chase can significantly improve flooding efficiency in asynchronous duty cycle networks.

Link estimation is a fundamental component of forwarding protocols in wireless sensor networks. In low power forwarding, however, the asynchronous nature of widely adopted duty-cycled radio control brings new challenges to achieve accurate and real-time estimation. First, the repeatedly transmitted frames (called wake-up frame) increase the complexity of accurate statistic, especially with bursty channel contention and coexistent interference. Second, frequent update of every link status will soon exhaust the limited energy supply. In this paper, we propose meter, which is a distributed wake-up frame counter. Meter takes the opportunities of link overhearing to update link status in real time. Furthermore, meter does not only depend on counting the successfully decoded wake-up frames, but also counts the corrupted ones by exploiting the feasibility of ZigBee identification based on short-term sequence of the received signal strength. We implement meter in TinyOS and further evaluate the performance through extensive experiments on indoor and outdoor test beds. The results demonstrate that meter can significantly improve the performance of the state-of-the-art link estimation scheme.

Asynchronous duty cycle is widely used for energy constraint wireless nodes to save energy. The basic flooding service in asynchronous duty cycle networks, however, is still far from efficient due to severe packet collisions and contentions. We present Chase, an efficient and fully distributed concurrent broadcast layer for flooding in asynchronous duty cycle networks. The main idea of Chase is to meet the strict signal timing and strength requirements (e.g., Capture Effect) for concurrent transmission while reducing contentions and collisions. We propose a distributed random inter-preamble packet interval adjustment approach to constructively satisfy the requirements. Even when requirements cannot be satisfied due to physical constraints (e.g., the difference of signal strength is less than a 3 dB), we propose a light-weight signal pattern recognition based approach to identify such a circumstance and extend radio-on time for packet delivery. We implement Chase in TinyOS and TelosB platform and extensively evaluate its performance. The implementation does not have any specific requirement on the hardware and can be easily extended to other platforms. The evaluation results also show that Chase can significantly improve flooding efficiency in asynchronous duty cycle networks.

Link estimation is a fundamental component of forwarding protocols in wireless sensor networks. In low power forwarding, however, the asynchronous nature of widely adopted duty-cycled radio control brings new challenges to achieve accurate and real- time estimation. First, the repeatedly transmitted frames (called wake-up frame) increase the complexity of accurate statistic, especially with bursty channel contention and coexistent interference. Second, frequent update of every link status exhausts the limited energy supply due to long duration of beacon broadcast. In this paper, we propose meter (Distributed Frame Counter), which takes the opportunities of link overhearing to update link status in real time. Furthermore, meter does not only depend on counting the successfully decoded wake-up frames, but also counts the corrupted ones by exploiting the feasibility of ZigBee identification based on short-term sequence of the received signal strength. We implement meter in TinyOS and further evaluate the performance through extensive experiments on indoor and outdoor testbeds. The results demonstrate that meter can significantly improve the performance of the state-of-the-art link estimation schemes.

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

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

On-air access of individual sensor node (called remote control) is an indispensable function in operational wireless sensor networks, for purposes like network management and real-time information delivery. To realize reliable and efficient remote control in a wireless sensor network (WSN), however, is extremely challenging, due to the stringent resource constraints and intrinsically unrealizable wireless communication. In this paper, we propose TeleAdjusting, a ready-to-use protocol to remotely control any individual node in a WSN. We develop a coding scheme for addressing on the cost-optimal reverse routing tree. In the address of each node, all its upstream relaying nodes are implicitly encoded. Then through a distributed prefix matching process between the local address and the destination address, a packet used for remote control is forwarded along a cost-optimal path. Moreover, TeleAdjusting incorporates opportunistic forwarding into the addressing process, so as to improve the network performance in terms of reliability and energy efficiency. We implement TeleAdjusting with TinyOS and evaluate its performance through extensive simulations and experiments. The results demonstrate that compared to the existing protocols, TeleAdjusting can provide high performance of remote control, which is as reliable as network-wide flooding and much more efficient than remote control through a pre-determined path.

Due to the constraint of energy resource, the radio of sensor nodes usually works in a duty-cycled mode. Since the sleep schedules of nodes are unsynchronized, a sender has to send preambles to coordinate with its receiver(s). In such contexts, opportunistic forwarding, which takes the earliest forwarding opportunity instead of a deterministic forwarder, shows great advantage in utilizing channel resource. The multiple forwarding choices with temporal and spatial diversity increase the chance of collision tolerance in concurrent transmissions, potentially enhancing end-to-end network performance. However, the current channel contention mechanism based on collision avoidance is too conservative to exploit concurrency. To address this problem, we propose COF, a practical protocol to exploit the potential Concurrency for low power Opportunistic Forwarding. COF determines whether a node should concurrently transmit or not, by incorporating: (1) a distributed and light-weight link quality measurement scheme for concurrent transmission and (2) a synthetic method to estimate the benefit of potential concurrency opportunity. COF can be easily integrated into the conventional unsynchronized sender-initiated protocols. We evaluate COF on a 40-node testbed. The results show that COF can reduce the end-to-end delay by up to 41% and energy consumption by 18.9%, compared with the state-of-the-art opportunistic forwarding protocol.

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

The energy efficiency and delivery robustness are two critical issues for low duty cycled wireless sensor networks. The asynchronous receiver-initiated duty cycling media access control (MAC) protocols have shown the effectiveness through various studies. In receiver-initiated MACs, packet transmission is triggered by the probe of receiver. However, it suffers from the performance degradation incurred by packet collision, especially under bursty traffic. Several protocols have been proposed to address this problem, but their performance is restricted by the unnecessary backoff time and long negotiation process. In this paper, we present Contention Detectable MAC (CD-MAC), an energy efficient and robust duty-cycled MAC for general wireless sensor network applications. By exploring the temporal diversity of the acknowledgements, a receiver recognizes the potential senders and subsequently polls individual senders one by one. We further design efficient algorithm to avoid the possible acknowledgement collision. We implement CD-MAC in TinyOS and evaluate the performance on an indoor testbed with single-hop and multi-hop networks. The results show that CD-MAC can significantly improve throughput by 1.72 times compared with the state-of-the-art receiver-initiated MAC protocol under bursty traffic loads. The results also demonstrate that CD-MAC can effectively mitigate the influence of hidden terminal problem and adapt to network dynamics well.

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

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

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

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

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

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

In large scale wireless sensor networks, retransmission strategies are widely adopted to guarantee the reliability of multi-hop forwarding. However, keeping retransmission over a bursty link may fail consecutively. Moreover, the retransmission will also be useless over those back-up links which are spatial correlated with the failed link. Thus, it is necessary to design an unified retransmission strategy, which considers both temporal and spacial link properties, to further improve network reliability and efficiency. In this paper, we propose RxLayer, a practical and general supporting layer of data retransmission. Without inducing noticeable overhead, RxLayer captures the temporal and spatial link properties by conditional probability models. A sender will retransmit data over the candidate link with the highest delivery probability while failures occur. RxLayer can be transparently integrated with most of the existing forwarding protocols. We implement RxLayer and evaluate it on both indoor and outdoor testbeds. The results show that RxLayer improves networks reliability and energy efficiency in various scenarios. The network reliability is improved by up to 7.82%, and the total number of transmissions is reduced by up to 36.3%.

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

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

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

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

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.

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

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

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.