The rapid expansion of the low-altitude economy has propelled the increasing cross-domain applications of unmanned aerial vehicles (UAVs). During cross-domain missions, UAVs face challenges in identity management and trajectory privacy protection. The conventional centralized identity management model is susceptible to single-point failures and blockchain-based solutions suffer from performance bottlenecks. Also, the high dynamics of low-altitude networks further exacerbates the risk of trajectory data leakage. To address these problems, this paper proposes a consortium blockchain-based cross-domain authentication and flight trajectory privacy protection scheme, which includes cross-domain authentication, key agreement, dynamic identity changing, mutual authentication and domain notification methods. The proposed blockchain-based method enables trusted cross-domain identity verification, preventing attackers from reconstructing full flight paths with partial information. Formal verification based on the real-or-random model is presented to prove the semantic security of the proposed method. Informal security analysis ensures that the proposed method resists major cyber attacks. The performance of the proposed method is evaluated through simulations. The experimental results show that the proposed method has higher efficiency compared to the existing methods.

While printers are widely regarded as trusted peripherals, their internal mechanical execution reveals subtle vibrational patterns that can leak document structure. We present VibraPrint, a passive mmWave sensing system that infers high-level document attributes—such as page count, content density, and template type—as well as finer-grained structural cues including line count, per-line text amount, and average word-length trends. These properties emerge because layout-induced actuation patterns imprint low-frequency vibrations on the printer chassis, which are remotely captured using a 60 GHz radar without accessing content, print commands, or firmware. To extract meaningful structure from weak and heavily filtered signals, VibraPrint employs a two-stage recovery pipeline that combines global arc fitting with rhythm-aligned segment-wise refinement. Each segment is encoded using hybrid time–frequency features and processed by a structure-aware Transformer for multi-task inference. Evaluated on 500 print jobs across 20 printer models, VibraPrint achieves a mean page-count error of 1.05, over 90% accuracy for density and template prediction, and reliable estimation of per-line structure under distance and alignment variations. These results reveal a previously unrecognized class of structural side-channel leakage inherent to everyday printing workflows.

Recent research has advanced the transmission rate and quality of video streaming in backscatter communications. However, these studies often overlook the challenge of deploying such systems in environments without dedicated excitation signals. To address this, we propose VideoBack, a high-quality video backscatter system using ambient WiFi signals for easy monitoring. VideoBack employs JPEG compression on high-resolution images to reduce transmission payload, enabling adaptation to the lower rates of WiFi backscatter. We design a customized packet structure for tags to backscatter image data using multiple uncontrolled ambient WiFi packets. To ensure power efficiency, we incorporate envelope detection with energy harvesting. To enable single-receiver deployment, we design a pilot-subcarrier-based recovery method to reconstruct the original WiFi signal. Our system maintains video reliability through a low bit error rate (BER) decoding strategy that includes phase error tracking and self-correction with multi-subcarriers. We prototype VideoBack using a commercial WiFi adapter, achieving a transmission rate of nearly 250 kbps with a BER below 0.05% up to 9 meters. VideoBack can send one frame of an image after just 2 seconds of energy harvesting within 3 meters.

In the above article [1], the email address and bio of Muhammad Rizwan are incorrect. The correct information is below:

Accurately monitoring micron-level vibrations with sub-hertz frequency estimation error is critical for early fault detection in industrial equipment. Existing solutions either rely on powered sensors or suffer from limited accuracy in passive operation, restricting scalability and long-term deployment. We presentVibro-Stethos, a fully battery-free sensing system that accurately captures micron-level vibrations with sub-hertz frequency estimation error. It employs a dual-junction fieldeffect transistor (JFET) analog frontend to convert vibration into impedance modulation and encodes this onto passive RFID backscatter. An embedded RFID chip enables selective tag activation and provides path-invariant reference amplitude normalization. A Graph Attention Network (GAT)-based model adaptively fuses features from spatially distributed tags, enabling robust fault classification under tag sparsity and placement variation. Extensive evaluation demonstrates that Vibro-Stethos achieves amplitude measurement errors within 2$\mu$m, frequency estimation errors below 0.1 Hz, and vibration fault classification accuracy of 93.7%. Real-world deployments on transformers further confirm its diagnostic capability. Vibro-Stethos offers a practical, robust, and accurate battery-free solution for pervasive industrial vibration monitoring.

Voice-enabledmobile applications(apps) are exploding in popularity as they could be manipulated with voice commands to achieve convenient man-machine interaction. These voice-enabled apps also raise security and privacy concerns about whether they would maliciously invoke microphones to realize voice eavesdropping. To explore this issue, in this work, we design baleful apps to access the microphone covertly, the results of test studies demonstrate that covert eavesdropping attacks can bypass existing device detection schemes as well as are unnoticeable to human users. To prevent the covert voice eavesdropping attack, we propose a versatilemicrophone icon detection(MicID) scheme inspired by the groundtruth that authorization of the voice function requires the user to touch the specific microphone icon in most of voice-based apps. Specifically, we devise a deep learning model,lightweight YOLO(L-YOLO), to locate the microphone icon on the screen quickly and accurately. By determining whether the located microphone icon is touched by the user, we can judge whether the current microphone access belongs to the app's normal operation or illegal eavesdropping. Finally, we conduct extensive experiments by deploying the scheme on real devices and collecting dataset. The evaluation results show that the proposed MicID scheme achieves more than 99% accuracy with low computation cost.

Currently, a major issue of WiFi-based sensing technologies is how to adapt to changes in the surrounding environment. The extreme sensitivity ofChannel State Information(CSI) makes many WiFi sensing arts frustrated when applied to the complex and unknown real world. To solve this problem, in this paper, we proposefreeEnvdesigned to automatically identify the micro-environmental changes (even tiny movements of the laptop) using WiFi devices, which can coexist with other WiFi sensing tasks with zero effort. To achieve automatic identification of micro-environmental changes, we quantify micro-environmental changes based on the physical propagation laws of WiFi signals and the main factors that affect CSI measurements. Then, we design a micro-environmental changes identification method, which determines whether the environment has changed by calculating theEarth Mover's Distance(EMD) of theProbability Density Function(PDF) of continuous CSI, without requiring training data. To remove the influence of dynamic human behaviors, we design a human dynamic detection scheme, which is achieved by obtaining the average inter-cluster distance of performingGaussian Mixture Model(GMM) clustering on CSI. We evaluatefreeEnvin real-world scenarios with six different hardware, four different scenarios, and twenty-four ways of micro-environmental changes. The results show that our method is robust to different devices and scenarios, and can achieve the average precision of 96.1% and 93.2% for micro-environmental changes identification and human dynamic behavior detection. By testing on a case study of threshold-based human presence detection,freeEnvcan effectively improve the detection performance.

Tearing the anterior cruciate ligament requires repair and rehabilitation to restore lower limb functionality fully. This study outlines a method for monitoring rehabilitation after knee surgery by analyzing footstep sounds and applying deep learning techniques. The process involves examining gait sounds during the initial four weeks of rehabilitation. The suggested system, StrideSense, recognizes walking sounds, enabling seamless and ongoing monitoring of patients’ gait rehabilitation. The research proposes a novel method for event detection by analyzing walking patterns using dynamic time-warping and sequential footstep duration. It leverages physiological data like gait sound and energy descriptors to develop a deep-learning model for gait improvement assessment, evaluated by a physiotherapist through lower extremity functional scores (LEFS). The suggested model, AtdNet, which utilizes Densenet169 and attention mechanisms, achieves 96% accuracy in classifying walking on various post-surgical days. It also predicts LEFS with a mean absolute error of 4.63%. A deeper analysis of bone-conducted footstep sounds enriched the acoustic sensing method. We assessed the suggested acoustic model alongside existing methods, showing that rehabilitation monitoring driven by acoustics outperforms traditional clinic-based approach assessments. Future efforts will focus on validating the model with a larger dataset and integrating it into smart homes.

Nowadays, voice input on earphones has become one of the most paramount human-computer interaction approaches. Traditional voice interaction is built on the air channel, which is highly noise-susceptible and suffers from being falsely triggered by nearby competing users. In our work, we design a noise-resistant voice interaction interface on earphones, namedEarOE, which takes advantage of the narrow-bandwidth body channel to reconstruct high-fidelity audible speech. Although promising, directly taking the body channel as a voice interaction interface is nontrivial since the limited bandwidth of body-channel speech causes the original timbre information and linguistic content to be lost. To address these issues, we employ an electro-acoustic (EA) model for occlusion effect-based cross-channel correlation analysis. Based on that, we carefully design an attention-based encoder-decoder network to embrace cross-channel correlation for high-quality wide-bandwidth spectrum synthesis. To accommodate individual differences and improve model generalization, we implement a physics-based data augmentation strategy to expand the scale of the training dataset. Through extensive real-world experiments with 28 participants,EarOE can achieve an average Mel-cepstral distance of 8.08, an average modulation spectra distance of 0.82, and an average log-spectral distance of 11.16, outperforming existing solutions.

The effectiveness of WiFi-based localization systems heavily relies on the spatial accuracy of WiFi AP. In real-world scenarios, factors such as AP rotation and irregular antenna tilt contribute significantly to inaccuracies, surpassing the impact of imprecise AP location and antenna separation. In this paper, we proposeAnteumbler, a non-invasive, accurate, and efficient system for measuring the orientation of each antenna in physical space. By leveraging the fact that maximum received power occurs when a Tx-Rx antenna pair is perfectly aligned, we build a spatial angle model capable of determining antennas’ orientations without prior knowledge. However, achieving comprehensive coverage across the spatial angle necessitates extensive sampling points. To enhance efficiency, we exploit the orthogonality of antenna directivity and polarization, and adopt an iterative algorithm, thereby reducing the number of sampling points by several orders of magnitude. Additionally, to attain the required antenna orientation accuracy, we mitigate the influence of propagation distance using a dual plane intersection model while filtering out ambient noise. Our real-world experiments, covering six antenna types, two antenna layouts, two antenna separations ($\lambda /2$and$\lambda$), and three AP heights, demonstrate thatAnteumblerachieves median errors below$\text{6}^\circ$for both elevation and azimuth angles, and exhibits robustness in NLoS and dynamic environments. Moreover, when integrated into the reverse localization system,Anteumblerdeployed over LocAP reduces antenna separation error by$10 \,\mathrm{mm}$, while for user localization system, its integration over SpotFi reduces user localization error by more than$1 \,\mathrm{m}$.

WiFi-based gait recognition technologies have seen significant advancements in recent years. However, most existing approaches rely on a critical assumption: users must walk continuously and maintain a consistent body posture. This poses a substantial challenge when users engage in non-periodic or discontinuous behaviors (e.g., stopping, starting, or turning mid-walk), which can disrupt the extraction of gait-related features and degrade recognition performance. To address this issue, we proposefreeGait, a novel approach designed to mitigate the impact of non-gait behaviors in WiFi-based gait recognition systems. Our solution models this problem as domain adaptation, where we learn domain-independent representations to isolate gait features from behavior-dependent noise. We treat human behaviors with labeled user data as source domains and behaviors without user labels as target domains. However, applying domain adaptation directly is challenging due to the ambiguous classification boundaries in the target domains for WiFi signals. To overcome this, we align the posterior distributions between the source and target domains and constrain the conditional distribution within the target domains to enhance gait classification accuracy. Additionally, we implement a data augmentation module to generate data resembling the labeled data, while supervised learning ensures distinctiveness between users. Our experiments, conducted with 20 participants across 3 different scenarios, demonstrate thatfreeGaitcan accurately predict data across 15 domains by labeling only a small subset from 6 source domains, achieving up to a 45% improvement in user classification accuracy compared to existing methods.

Recent studies show that inaudible attacks pose a non-negligible security risk to smart speakers. While several countermeasures have been proposed to detect the occurrence of the inaudible attack passively, accurately locating the attack source in 3D free space remains an unresolved challenge. Arrow is designed to bridge this gap by attempting to detect the occurrence of inaudible attacks and determine their localization simultaneously. Instead of relying on dedicated hardware components, Arrow is implemented with the microphone array widely deployed on COTS (Commercial Off-The-Shelf) smart speakers. Throughout the spatial information captured by the microphone array, Arrow establishes a spatial mapping model and derives orientation-related features to pinpoint the location of the attack source. Furthermore, to improve the robustness against co-channel interference, Arrow adopt carefully-modulated ultrasonic waveforms to achieve noise-robust attack detection. Through the above technical mechanism, Arrow can significantly improve the security level of voice assistants on smart speakers with nearly zero deployment cost. We implement a prototype of Arrow and conduct a comprehensive performance evaluation. The results show Arrow can achieve 2.5 ○ and 7 ○ error in DoA estimation for horizontal and vertical angles, respectively.

In industrial monitoring, the accurate detection of subtle mechanical vibrations is essential for implementing effective preventive maintenance strategies and improving equipment longevity. However, current RFID-based sensing technologies are hindered by environmental noise and the inherent limitations of signal wavelengths. To address these issues, we introduce TagVibra, an innovative RFID-based system enhanced by the Differential Phase Amplification (DPA) algorithm. This algorithm harnesses the modulation characteristics of RFID technology along with the harmonic effects of RFID tags to markedly enhance vibration detection accuracy. TagVibra has been implemented on the USRP platform, and experimental results confirm its ability to detect minute vibrations down to 0.3 mm with an average frequency estimation error of less than 0.9 Hz. This breakthrough significantly advances the capabilities for early fault diagnosis in high-precision industrial settings.

Accurately estimating the velocity (including speed and direction) of moving targets has recently attracted widespread attention in augmented reality, security monitoring and sports health. In particular, the Doppler Frequency Shift (DFS)-based velocity estimation schemes using WiFi devices have shown great potential and have been widely studied. However, previous Fast Fourier Transform (FFT)-based and path-parameter-based arts have inherent limitations in DFS estimation and, worse still, ignore the nonlinear measurement errors caused by the relative orientation between the moving target and the WiFi transceiver. The above limitations make it difficult to meet the requirements for fine-grained velocity estimation in practical applications. To cope with these limitations, in this article, we propose a learning-based velocity estimation framework, named freeDoppler , to achieve fine-grained, multi-target and orientation-independent velocity estimation. Specifically, we construct a WiFi-based Velocity Estimation Network (VEN), which leverages continuous complex-valued Channel State Information (CSI) sequences as input, to fully learn the inherent information of the Doppler effect and accurately predict velocity series. In addition, we adopt the electric field scattering model of Maxwell’s equations to construct a physics-informed CSI Generation Model (CGM), thereby generating large-scale and high-quality simulated CSI samples to improve the generalization of the VEN model. Throughout extensive real-world experiments, freeDoppler can achieve median errors of 7.98 cm/s for speed estimation, 28° for direction estimation and 35 cm for human tracking in one or two moving targets, significantly outperforming the state-of-the-art methods.

WiFi backscatter communication demonstrates significant potential for the upcoming era of low-power wireless networks. Nevertheless, due to the low-power requirement of backscatter tags, there are limitations in their capacity to eliminate conflicts, posing a significant challenge for WiFi backscatter communication in environments with ambient interference. To address that, we introduce SlickScatter, an interference-insensitive WiFi backscatter system that can retrieve WiFi backscatter signals even in the presence of unknown ambient interference. The core strategy of SlickScatter involves designating a portion of the tag data symbols as pilot symbols. This approach enables the detection of uninterfered subcarriers and the estimation of channel state information and phase errors for all symbols within a packet, facilitating the demodulation of packets affected by interference. We have prototyped and evaluated SlickScatter with 802.11g OFDM WiFi signals, demonstrating its robustness and effectiveness against unknown ambient interference. Compared with a state-of-the-art solution, SlickScatter significantly decreases the frame error rate by 50% and improves the throughput by 1.96× at a distance of 12 m.

The performance of WiFi-based localization systems is affected by the spatial accuracy of WiFi AP. Compared with the imprecision of AP location and antenna separation, the imprecision of AP’s or antenna’s orientation is more important in real scenarios, including AP rotation and antenna irregular tilt. In this paper, we propose Anteumbler that non-invasively, accurately and efficiently measures the orientation of each antenna in physical space. Based on the fact that the received power is maximized when a Tx-Rx antenna pair is perfectly aligned, we construct a spatial angle model that can obtain the antennas’ orientations without prior knowledge. However, the sampling points of traversing the spatial angle need to cover the entire space. We use the orthogonality of antenna directivity and polarization and adopt an iterative algorithm to reduce the sampling points by hundreds of times, which greatly improves the efficiency. To achieve the required antenna orientation accuracy, we eliminate the influence of propagation distance using a dual plane intersection model and filter out ambient noise. Our real-world experiments with six antenna types, two antenna layouts and two antenna separations show that Anteumbler achieves median errors below 6 ° for both elevation and azimuth angles, and is robust to NLoS and dynamic environments. Last but not least, for the reverse localization system, we deploy Anteumbler over LocAP and reduce the antenna separation error by 10 mm, while for the user localization system, we deploy Anteumbler over SpotFi and reduce the user localization error by more than 1 m.

Recently, WiFi-based gait recognition technologies have been widely studied. However, most of them work on a strong assumption that users need to walk continuously and periodically under a constant body posture. Thus, a significant challenge arises when users engage in non-periodic or discontinuous behaviors (e.g., stopping and going, turning around during walking). This is because variations of non-gait behaviors interfere with the extraction of gait-related features, resulting in recognition performance degradation. To solve this problem, we propose freeGait, which aims to mitigate the user's non-gait behaviors of WiFi-based gait recognition system. Specifically, we model this problem as domain adaptation, by learning domain-independent representations to extract behavior-independent gait features. We consider human behaviors with labels of users as source domains, and human behaviors without labels of users as target domains. However, directly applying domain adaptation to our specific problem is challenging, because the classification boundaries of the unknown target domains are unclear for WiFi signals. We align the posterior distributions of the source and target domains, and constrain the conditional distribution of the target domains to optimize the gait classification accuracy. To obtain enough source domains data, we build a data augmentation module to generate data similar to the labeled data, and use supervised learning to make the data different between users. We conduct experiments with 20 people and 3 different scenarios, and the results show that accurate predictions of a total of 15 domains data can be achieved by only collecting and labeling a small amount of data from 6 source domains, and user classification accuracy can be improved by up to 45% compared to other existing techniques.

Despite the potential for throughput enhancement with multiple tags, existing WiFi backscatter systems have been limited by inband interference among various tags. In response, we propose MultiRider, the first WiFi backscatter system that can tame in-band interference and support multi-tag parallel communication on commercial OFDM protocol. The principle behind MultiRider lies in its ability to demodulate and reconstruct tag data using just one uncorrupted subcarrier in the spectrum domain. To address the inherent challenges of preamble corruption and data collision due to in-band interference, we design three modules: 1) preamble recovery based on a concurrency-driven backscatter packet structure; 2) subcarrier-level demodulation using uncorrupted subcarriers; and 3) iterative interference cancellation for multiple tags. We prototype and evaluate MultiRider under 802.11g OFDM WiFi signals with commercial adapters and software-defined radios. Comprehensive evaluations illustrate that MultiRider can efficiently solve in-band interference. Notably, it can expand the network capacity of WiFi backscatter by 4× and use 8 channels in the 2.4GHz WiFi band for concurrent communication. Further results reveal that MultiRider can gain 10× network capacity in 35MHz bandwidth and reach 2.29 Mbps system throughput.

Due to contactless and convenient experiences, WiFi-based device-free fingerprints localization technologies have extensively attracted research attention. However, they are studied based on an assumption that the user is stationary and face a major challenge in the presence of users motions. That is because users motions induced CSIWiFi variations results in inconsistent location fingerprints during training and prediction, leading to system ineffective. To solve this problem, in this paper, we propose freeLoc, which aims to free users motions (even unseen) while maintaining accurate localization. Specifically, we construct a domain adaptation network that defines different users and motions as different domains, and learns domain-independent representations to extract location fingerprints independent of users motions. Unfortunately, collecting sufficient amounts of WiFi data is difficult. To reduce the cost of labeling data and ensure the performance of domain adaptation network, we utilize adversarial autoencoder to build a data augmentation module to introduce data diversity. We deploy experiments in a real scenario, and the results show that only by labeling three motions of three users, we can achieve accurate localization (the nearest locations are about one meter away) for a total of 36 domains including 6 users and 6 motions. Compared to other existing technologies, freeLoc can improve location prediction accuracy by up to 35%.

With the booming development of fifth-generation network technology and Internet of Things, the number of end-user devices (EDs) and diverse applications is surging, resulting in massive data generated at the edge of networks. To process these data efficiently, the innovative mobile edge computing (MEC) framework has emerged to guarantee low latency and enable efficient computing close to the user traffic. Recently, federated learning (FL) has demonstrated its empirical success in edge computing due to its privacy-preserving advantages. Thus, it becomes a promising solution for analyzing and processing distributed data on EDs in various machine learning tasks, which are the major workloads in MEC. Unfortunately, EDs are typically powered by batteries with limited capacity, which brings challenges when performing energy-intensive FL tasks. To address these challenges, many strategies have been proposed to save energy in FL. Considering the absence of a survey that thoroughly summarizes and classifies these strategies, in this paper, we provide a comprehensive survey of recent advances in energy-efficient strategies for FL in MEC. Specifically, we first introduce the system model and energy consumption models in FL, in terms of computation and communication. Then we analyze the challenges regarding improving energy efficiency and summarize the energy-efficient strategies from three perspectives: learning-based, resource allocation, and client selection. We conduct a detailed analysis of these strategies, comparing their advantages and disadvantages. Additionally, we visually illustrate the impact of these strategies on the performance of FL by showcasing experimental results. Finally, several potential future research directions for energy-efficient FL are discussed.

The growing use of smart devices requires improving privacy and security. Conventional biometrics confront false positives and unauthorized access, stressing cautious user input. We enhance security by analyzing distinctive human physiological characteristics rather than relying on conventional methods susceptible to spoof attacks. Drinking, a common physiological activity, can provide continuous authentication.SipDeep, proposed innovative system, utilizes bone-conducted liquid intake sound, incorporating unique biometrics from bone and pharyngeal characteristics. The system captures these elements in the external auditory canal, offering a novel transparent authentication applicable to a diverse user range. Our noise filtering system eliminates environmental and anatomical interferences during drinking, including subtle body movements. The study introduces a hybrid event detection technique integrating wavelet transform with start/end points detection. Next, we extract physiological features from bone structure, liquid intake sound, and liquid intake pattern. We used the physiological features to train a deep learning algorithm based on a Triplet-Siamese network to classify authentication. The proposed model has been thoroughly compared with advanced models such as DenseNet169, ResNet18, and VGG16. Following extensive experimentation involving multiple users across various environments,SipDeepdemonstrates 96.5% authentication accuracy, coupled with a 98.33% resistance to spoof attacks.

Most existing voice-based user authentication systems mainly rely on microphones to capture the unique vocal characteristics of an individual, which are vulnerable to various acoustic attacks and may suffer high-security risks. In this work, we presentAccuth$^+$+, a novel authentication system on the wrist-worn device that takes advantage of a low-cost accelerometer to verify the user's identity and resist spoofing acoustic attacks.Accuth$^+$+captures unique sound vibrations during the human pronunciation process and extracts multi-level features to verify the user's identity. Specifically, we analyze and model the differences between the physical sound field of human beings and loudspeakers, and extract a novel sound-field-level liveness feature to defend against spoofing attacks.Accuth$^+$+is an effective complement to existing wearable authentication approaches as it only leverages a ubiquitous, low-cost, and small-size accelerometer. In real-world experiments.Accuth$^+$+achieves over 92.85% averaged identification accuracy among 15 human participants and an averaged equal error rate (EER) of 1.91% for spoofing attack detection.

As earable devices have become indispensable smart devices in people's lives, earable-based user authentication has gradually attracted widespread attention. In our work, we explore novel in-ear breathing biometrics and design an earable-based authentication approach, namedBreathSign, which takes advantage of inward-facing microphones on commercial earphones to capture in-ear breathing sounds for passive authentication. To expand the differences among individuals, we model the process of breathing sound generation, transmission, and reception. Based on that, we derive hard-to-forge physical-level features from in-ear breathing sounds as biometrics. Furthermore, to eliminate the impact of breathing behavioral patterns (e.g., duration and intensity), we design a triple network model to extract breathing behavior-independent features and design an online user template update mechanism for long-term authentication. Extensive experiments with 35 healthy subjects have been conducted to evaluate the performance ofBreathSign. The results show that our system achieves the average authentication accuracy of 93.15%, 98.06%, and 99.74% via one, five, and nine breathing cycles, respectively. Regarding the resistance of spoofing attacks,BreathSigncould achieve an average EER of approximately 3.5%. Compared with other behavior-based authentication schemes,BreathSigndoes not require users to perform complex movements or postures but only effortless breathing for authentication and can be easily implemented on commercial earphones with high usability and enhanced security.

The existing RF-based liquid identification systems usually rely on prior knowledge, such as pre-build database or the material and width information of the vessel. Furthermore, existing methods may not work in scenarios where the height of liquid is smaller than that of antenna. In this paper, we proposesLiqRay$^+$, a contactless system which can identify liquids in a fine-grained level without prior knowledge. To remove the effect of vessel, we build a dual-antenna model and craft a relative frequency response factor, exploring diversity of the permittivity in frequency domain. To eliminate the effect of different height, we devise the electric field distribution model at the receiving antenna, solving the unknown heights via spatio-differential model. Among eight different solvents,LiqRay$^+$can identify alcohol solutions with a concentration difference of 1% with 92.9% accuracy. Even if the liquid height is about 4 cm, which is fairly lower than that of most antennas’ heights, the accuracy is more than 85%.

Mobile Edge Computing (MEC) technology can be implemented at cellular base stations, enabling flexible and configurable provisions of services for mobile users to access. Nevertheless, the conventional solutions mainly focus onstaticalservice provisioning, which ignores the dynamic nature of the arriving service requests. In this work, we first conduct comprehensive data-driven observations on over 4 million service requests throughout 9,800 base stations. Our key findings suggest that users’ demands intrinsically exhibit spatial and temporal patterns, which inevitably lead to performance degradation in statical service provisioning. Motivated by that, we design and implement MobiEdge, a predictive service provisioning system with online learning in wireless edge networks. We propose a graph embedding learning-based model for representation learning, thus to achieve accurate request prediction at different base stations. Then, based on the prediction of incoming service requests, we study the service provisioning reconfiguration problem, i.e., how to jointly optimize service placement and corresponding request scheduling across dual timescales, under constraints of network resources and the total budget. By leveraging the submodular technique, we transform the research issue into a submodular function maximization problem under the$q$-independence system constraint, where$q$is a positive constant related to the ratio of coefficients in constraint conditions. On this basis, we propose a$1/(1+q)$approximation algorithm with rigorous theoretical analysis on the bounded maximum utility. Extensive trace-driven evaluations are conducted over networks of different scales, and MobiEdge shows remarkable performance enhancements by achieving the accuracy of up to 98% in service prediction and an average utility of 92.9% to the optimal solution in service provisioning.

Recent works have achieved considerable success in improving the concurrency of backscatter network. However, they do not optimize the balance between throughput and spectrum occupancy, both of which serve as pivotal parameters in concurrent transmissions. Moreover, these works also introduce complex components on tag thereby increasing both power consumption and deployment costs. In this article, we proposeSpray, a tag-lightweight system to achieve high throughput and narrow-band occupancy with low power. The key idea is to incorporate an agile channel allocating and scheduling mechanism into the backscatter network. This approach allows for efficient spectrum utilization and concurrency without the need for energy-intensive components. To optimize throughput in the presence of the challenge of harmonic interference, we introduce a novel algorithm that determines the channels with an optimal combination of central frequencies and bandwidths. Additionally, we propose a fair scheduling strategy to ensure equitable transmission opportunities for all tags. We prototype theSpraytag using commercial off-the-shelf components and implement the excitation and receiver with software-defined radio platform. Our evaluation shows that the system supports 30 parallel tags transmitting in the bandwidth of 600 kHz and the throughput can reach more than 280 kbps.

Recent years have witnessed the emerging development of single-antenna wireless respiration detection that can be integrated into IoT devices with a single transceiver chain. However, existing single-antenna-based solutions are all limited by the short sensing range within 2-4 m due to noise interference, which makes them difficult to be adopted in most room-scale scenarios. To deal with this dilemma, we propose a room-scale, noise-resistance, and accurate respiration monitoring system, named Wi-Cyclops , 1 which captures CSI changes induced by respiratory movements only via one antenna on commercial WiFi devices. To push the limits of effective sensing distance, we innovatively supply a new perspective to review the CSI samples along the sub-carrier dimension. From this dimension, we find that the interrelationship between sub-carriers with different timestamps still shows a high correlation even though the SNR decreases. Based on that, we analyze the noise characteristics along the sub-carrier dimension and correspondingly design a series of denoising schemes. Specifically, we carefully design a PCA-based denoising method to filter out ambient noises. After that, considering the low distribution densities of the AGC-induced noise, we then remove it by optimizing the DBSCAN denoising method with the K-Means-based adaptive radius search. Extensive experiments demonstrate that our system can work effectively in three typical family scenarios. Wi-Cyclops can achieve 98% accuracy even when the person is 7 m away from the transceiver pair. Compared with the start-of-art single-antenna-based approaches in real scenarios, Wi-Cyclops can improve the sensing range from 3 m to 7 m, which can meet the requirements of room-scale respiration monitoring. Additionally, to show the high compatibility with smart home devices, Wi-Cyclops is deployed on seven commercial IoT devices and still achieves a low average absolute error with 0.41 bpm.

Mechanical vibration sensing is one of the most critical issues for advanced industrial IoT applications, such as troubleshooting and working condition analysis. Different from status quo solutions, the approaches based on wireless sensing have the advantages of non-invasive and easy to deploy. However, existing wireless works such as RFID and radar are limited by deployment distance or NLoS working scenarios. In this work, we propose a wireless vibration sensing system using COTS WiFi named Wi-Ear, which could monitor multiple devices in LoS or NLoS scenarios. Moreover, Wi-Ear can distinguish the vibration frequency belonging to which device without utilizing any sensor. Especially, in a weak signal scenario where the vibration signal is buried by noise in the raw CSI, Wi-Ear can also detect the vibration frequency effectively. Finally, we implement Wi-Ear with COTS WiFi devices and evaluate it with commercially available motors. For vibration sensing, Wi-Ear can sense weak vibration with an error of 0.1Hz. For device identification, Wi-Ear can identify 10 vibrating devices of 3 different types with an accuracy of 92%. Comprehensive experiments in various scenarios are conducted to show great robustness and stability.

Recent works have achieved considerable success in increasing the transmission rate of video streaming backscatter communications. However, they do not consider the ease of deployment in real-world environments where these dedicated excitations are not readily available. In addition, these works are transmitted with lower image resolution, and the images received by users are not eye-friendly. In this paper, we propose VideoBack, a high quality video backscatter system with commercial WiFi excitation for easy-to-use monitoring. The key idea is to perform JPEG compression on high-pixel images to adapt to intermittent ambient WiFi transmission. This approach allows ambient commercial routers to be used for excitation. Furthermore, our work enables high-quality video transmissions by devising a low bit error rate decoding scheme. We build a prototype of VideoBack and use a commercial WiFi adapter to excite the tag to realize the transmission and reception of video. The prototype uses RF signals to charge and store energy. Our evaluations show that VideoBack can transmit images at a throughput of nearly 250 kbps with bit error rate below 0.0005 within 9 meters, while the images only have minor distinctions. VideoBack can support sending one frame of image after just 2 seconds of power acquisition within 3m.

Considering the long charging time and limitation of available charging stations in the traditional battery recharging facilities, the construction of battery swapping station (BSS) has become a new paradigm to satisfy the energy demand timely and sufficiently. Previous solutions lack a joint consideration of battery swapping station establishment and request assignment with the long distance subsidy. In this paper, we study the Station Placement and Assignment (SPA) problem to minimize the overall operation cost. Unfortunately, it shows great difficulty due to the infinite candidate locations and the complex coupling relation for request assignments. To address these challenges, we first devise the bundle generation strategy to reduce the infinite candidate location to finite, then propose an efficient algorithm based on the greedy strategy to assign the battery swapping requests. Extensive evaluations are carried out to show the outstanding performance of our proposed algorithms.

In this paper, we present a novel object tracking system, named STABack, that utilizes backscatter tags and accelerometer sensors. The system is designed to support high-speed movement tracking with high accuracy. One of the challenges faced in this system is the instability of the received signal due to the motion and rotation of the backscatter tag. To address this issue, we propose an amplitude stabilization algorithm to eliminate the interference caused by the tag's motion. The algorithm uses envelope detection to remove the impact of high-frequency motion on the signal, and a dynamic threshold output to further reduce the bit error rate of backscatter demodulation. Additionally, we perform outliers removal and interpolation on the three-axis accelerometer data and compute the attitude angle using 3D geometric quadrants. Finally, we implement the prototype of STABack and evaluate its performance. Our method improves BER up to 0.3757 compared with the regular demodulation method. Our evaluation of the STABack prototype shows that it achieves a median distance measurement accuracy of 6.45 cm with a standard deviation of 6.95 cm. The mean error of the attitude angle estimation is less than 7 degrees, and the average relative error of three-axis acceleration tracking is only 0.097. The system's accuracy in detecting the target object's trajectory is as high as 98%, and it can still decode with a bit error rate of no more than 0.034 at a speed of 166 cm/s. The power consumption of backscatter communication is$38.54\ \mu\mathrm{W}$. Based on our experimental results, Overall, our results demonstrate that STABack can accurately estimate the movement and rotation of target objects in unstable backscatter channels.

Recent works have shown that inaudible signals (at ultrasound frequencies) can become audible to the microphone by exploiting the nonlinear effects. With a well-designed inaudible signal, an adversary can control Amazon Echo and Google Homelike devices in people’s rooms silently and remotely. A voice command like “Alexa, open the door“ can be a serious treat. Although recent works design various methods against such inaudible attacks, one important issue remains open: there is no clear solution to locate the attack source accurately. Obviously, the only way to completely eliminate such inaudible threats is to locate and remove the attack source. This paper is an attempt to close this gap. We propose Arrow, an effective method to help users locate the ultrasound attack source in 3D space indoors. Arrow establishes the relationship between inaudible signals and the recorded sounds of the microphone, and then explores the architecture of the embedded microphone array on smart speaker for extracting a 3D direction-specific signature. By learning such directional signature, Arrow can accurately estimate the spatial orientation of the inaudible attack source and help users to locate and remove it. We implement a prototype of Arrow and conduct comprehensive experiments to validate its performance. The results show Arrow can achieve 2.5° and 7° error in DoA(Direction of Arrival) estimation for horizontal and vertical angles, respectively.

In this paper, we present PAssTrack, a Wi-Fi based passive human tracking system which is adapted to the practical antenna spacing of most commodity Wi-Fi access points (APs) and achieves accurate tracking results. We mainly enhance our system in the following four aspects. Firstly, we modify 2D MUSIC algorithm for estimating parameters including angle of arrival (AoA) and relative time of flight (rToF) of dynamic human reflection path. And we analyze the superiority of our algorithm compared with the state-of-the-art solutions. Secondly, we leverage the estimated rToF and the continuity of AoA to resolve the angle ambiguity caused by antenna spacing larger than half of the wavelength. Thirdly, we optimize the mesh model based on Fresnel zone theory to a dual antenna version for fine-grained velocity estimation. Lastly, we introduce hologram for target localization in order to compensate the errors of estimated path parameters using velocity estimates, and reduce the impact of outliers through kernel density estimation (KDE). The experiments are conducted in two real-world indoor environments, and the results demonstrate the advantages of PAssTrack in aspects of better tracking accuracy than the state-of-the-arts, easy and effective calibration, robustness under the case of blocked transceivers and adaptiveness to different antenna spacing.

WiFi connections are vulnerable to simulated attacks from rogue access points (APs) or devices whose SSID and/or MAC/IP address are the same as legitimate devices. This kind of attack is difficult to counter with traditional network security mechanisms. In this article, we propose a new security mechanism that uses environment-independent features extracted from channel state information (CSI) to detect and identify rogue WiFi devices or APs, and reject their connections. We find that due to the$I/Q$imbalance and imperfect oscillator of each WiFi network card (NIC), the nonlinear phase error of different subcarriers will vary with the NIC. Through our experimental verification, this cross-subcarrier phase feature is invariant to the location and the environment. We deploy systems on two platforms that can extract constant phase errors from the constantly changing CSI in less than 1 s, which is at least$8 \times $faster than that of the state-of-the-art solution. Extensive experiments on commercial routers and end devices in different scenarios show that based on the Industrial Platform Computer (IPC) platform, where only nonencrypted rogue connections can be detected, the detection accuracy rate reaches 96%, and the false alarm rate is less than 2%. Based on the ASUS router platform (a commercial WiFi router), WiFi channels and smart device types are not restricted, which greatly improved universality, and the accuracy of device connection detection can even reach more than 99%. We improve a device-type identification method based on the communication traffic features of the device when connected to WiFi. Experiments show that even if there are multiple similar devices from the same manufacturer, the accuracy of device-type detection exceeds 99%.

Mechanical vibration monitoring plays a critical role in today's industrial Internet of Things (IoT) applications. Existing invasive solutions usually directly attach sensors to the target, which may affect the operations of delicate devices. Non-invasive video-based approaches incur poor performance in low light conditions, and laser-based ones have difficulties to monitor multiple objects simultaneously. In this work, we proposeRF-Ear$^+$+, a contactless vibration sensing system using Commercial off-the-shelf (COTS) RFID.RF-Ear$^+$+could accurately monitor the mechanical vibrations of multiple devices using a single tag: it can clearly tell which object is vibrating at what frequency without attaching tags on any device.RF-Ear$^+$+can measure the vibration with a frequency up to 987 Hz at a mean error rate of$0.4\%$. We further employ each device's unique vibration fingerprint to identify and differentiate devices of exactly the same model. What's more,RF-Ear$^+$+can detect the rotating machinery faults based on the constructed spectrogram, which achieves$98\%$accuracy on 6 types of states. To improve the computation efficiency, we optimize the input of model in both time and frequency domains, and thus enable deployment on low-cost edge devices successfully. Comprehensive experiments conducted in lab and wild demonstrate the effectiveness of our system.

With the rapid advance of wireless sensing techniques, it becomes possible to provide a fine-grained user activity tracking service at home and office. Such a technique is of broad applications in various domains such as personal activity diary, elderly care, and customized services. For example, several radio frequency (RF) based sensing systems were recently proposed for human activity recognition. However, most of them focused on specific scenarios and suffered from interference caused by other users and wireless devices. In this work, we propose Back-Guard, a backscattering-based sensing system that achieves accurate and non-intrusive user activity recognition and further user identification/authentication. Back-Guard carefully examines the backscatter spectrogram data and extracts high-level features from both spatial and temporal domains. Leveraging the parallel attention based deep learning model, our system can discriminate different motions and users accurately and robustly in various situations. We implemented a prototype system and collected data from 25 users for more than 2 months. Extensive experiments demonstrate that Back-Guard achieves 93.4$\%$activity recognition accuracy and 91.5$\%$user identification accuracy, respectively. In particular, Back-Guard can also tackle multiple user scenarios, which has little accuracy reduction when the users are separated, e.g., by around 2 meters.

Thanks to the increasing number and massive coverage, delivery drones, equipped with various sensors, have demonstrated significant but unexplored potentials for large-scale and low-cost urban sensing during package delivery. In this paper, we propose novel studies on the reutilization of such delivery drone resources to fill this void in urban crowdsensing. Accounting for interdependency between flying/sensing and drone delivery weight, we jointly optimize route selection, sensing time, and delivery weight allocation, to maximize delivery and sensing utility under drones’ energy constraints. This problem is formulated as a non-convex mixed-integer non-Linear programming problem, which is proved to be NP-hard. To address this intricate problem, we propose near-optimal algorithms that leverage the equivalent objective function construction, the local search scheme, and the alternating iteration technique. Theoretical analysis indicates that our algorithms can achieve$\frac{1}{4+\varepsilon }$-approximation ratio (where$\varepsilon$is an arbitrarily small positive parameter) and the convergence guarantee in polynomial time, for the scenarios of fixed and adjustable delivery weights, respectively. Extensive trace-based simulations, field experiments, and the real-world application demonstrate that ours can significantly improve the delivery & sensing utility by$124.7\%$and the energy utilization rate by$72.2\%$on average, compared with the drone delivery without reusing.

Magnetic resonant coupling wireless power transfer (MRC-WPT) enables convenient device-charging. When MIMO MRC-WPT system incorporated with multiple relay components, both relayOn-Offstate (i.e.,power routing) and TX current (i.e.,current scheduling) could be adjusted for improving charging efficiency and distance. Previous approaches need the collaboration and feedback from the energy receiver (RX), achieved using side-channels, e.g., Bluetooth, which is time/energy-consuming. In this work we propose, design, and implement a multi-relay MIMO MRC-WPT system, and design an almost optimum joint optimization ofPowerROuting andCurrentScheduling method namedPROCS, without relying on any feedback from RX. We carefully decompose the joint optimization problem into two subproblems without affecting the overall optimality of the combined solution. For current scheduling subproblem, we propose an almost-optimum RX-feedback independent solution. For power routing subproblem, we first design a greedy algorithm with$\frac{1}{2}$approximation ratio, and then design a DQN based method to further improve its effectiveness. We prototype our system and evaluate it with extensive experiments. Our results demonstrate the effectiveness of the proposed algorithms. The achieved power transfer efficiency (PTE) on average is$3.2X$,$1.43X$,$1.34X$, and$7.3X$over the other four strategies: Without relay, with non-adjustable relays, greed based, and shortest-path based ones.

RFID technology has recently been exploited for not only identification but also fine-grained trajectory tracking and gesture recognition. While contact-based (a tag is attached to the target of interest) sensing has achieved promising results, contactless sensing still faces severe challenges such as low accuracy and inability to sense multiple targets simultaneously in proximity, restricting its applicability in real-world deployment. In this work, we present Tamera , a contactless RFID-based sensing system, which significantly improves the tracking accuracy, enables multi-commodity tracking, and even material and shopping behavior recognition. We successfully address multiple technical challenges, and design and implement our prototype on commodity RFID devices. We test the positioning accuracy of Tamera in a 5 m × 6 m laboratory. Tamera achieves a median error of 1.3 cm and 2.7 cm for contactless single- and multi-commodity tracking, respectively. In our laboratory, two shelves commonly found in the supermarket are arranged and the goods are placed on them. Tamera successfully localizes and identifies the material type (metal, plastic, paper, and glass) of the commodities on the shelf with an accuracy higher than 95%. Tamera successfully recognizes four shopping behaviors (taking commodity, replacing commodity, buying commodity, and invoking commodity) with an accuracy higher than 93%.

As a preliminary exploration, based on the historical order data of the battery-swap stations, this paper predicts the order quantity of the stations in the future period, which can be used as a reference for the battery scheduling of battery-swap stations. Compared with the existing studies, we establish the network of battery-swap stations based on the real large-scale order data, and build the graph model combined with multihead attention to predict. The challenges in this paper include similarity analysis, building the adjacency matrix of battery-swap stations, and the model for distributed stations fine-grained order volume prediction. To address the above challenges, we build the GAT model to accurately predict the order quantity of batteryswap stations. Our work consists of two parts. First, we construct three features and calculate the similarity among the features based on the spatio-temporal correlation data of battery-swap stations. The similarity will be used to construct the association matrix of stations and fuse it with the distance adjacency matrix to construct the topological network of battery-swap stations. Second, we build a graph neural network model and input the network structure of battery-swap stations and historical order data for prediction. In order to capture the correlation features of data, we introduce the multi-attention mechanism, which uses attention to capture the features of different graph nodes, and finally achieves better prediction effect through multi-connection. Our model performs well in hourly order prediction, with an average MAE of about 1.07. We believe that this work can provide reference and ideas for enterprises in order balance, battery scheduling and other aspects.

The existing RF-based liquid identification methods commonly require a training network of liquid or the container information, such as material and width. Moreover, status quo methods are inapplicable when the solution height is lower than that of the antenna, which is generally unknown either. This paper proposes LiqRay, an RF-based solution, retaining non-invasive and fine-grained liquid recognition abilities, thus can recognize unknown solutions without prior knowledge. In dealing with the unknown container material and width, we utilize a dual-antenna model and craft a relative frequency response factor, exploring diversity of the permittivity in frequency domain. In tackling the unknown heights of solution and antenna, we devise the electric field distribution model at the receiving antenna, solving the unknown heights via spatio-differential model. Among eight different solvents, LiqRay can identify alcohol solutions with a concentration difference of 1% with 94.92% accuracy. Nevertheless, LiqRay can obtain the relative frequency response factor with a relative error of 6.7% without being affected by the height of the solution. Even if it is merely 4 cm, this is fairly lower than that of most antennas' heights, since the operating frequency is around 2 GHz.

Most existing voice-based user authentication systems mainly rely on microphones to capture the unique vocal characteristics of an individual, which makes these systems vulnerable to various acoustic attacks and suffer high-security risks. In this work, we present Accuth, a novel authentication system that takes advantage of a low-cost accelerometer to verify the user's identity and resist spoofing acoustic attacks. Accuth captures unique sound vibrations during the human pronunciation process and extracts multi-level features to verify the user's identity. Specifically, we analyze and model the differences between the physical sound field of human beings and loudspeakers, and extract a novel sound-field-level liveness feature to defend against spoofing attacks. Accuth is an effective complement to existing authentication approaches as it only leverages a ubiquitous, low-cost, and small-size accelerometer. In real-world experiments, Accuth achieves over 90% identification accuracy among 15 human participants and an average equal error rate (EER) of 3.02% for spoofing attack detection.

We analyze the outage performance of a lossy forwarding (LF) relaying system with the simultaneous wireless information and power transfer (SWIPT) capability. In the system of LF with SWIPT (LF-SWIPT), a source broadcasts its message to both a relay and a destination. A relay node with SWIPT functionality harvests energy and decodes information from the source signal. The energy is split into two parts for information processing and message forwarding, respectively. For information processing, the relay attempts to decode the incoming source signal and forms an estimate. Unlike the existing decode-and-forward SWIPT system (DF-SWIPT), the estimate is always forwarded using the harvested energy. The destination performs joint decoding to recover the message with the signals received from both the source node and the relay node. We derive the outage probability for the LF-SWIPT system based on the theorem ofsource coding with side information. The simulation results demonstrate that the proposed system achieves significant gains (around 1–2 dB) compared to the DF-SWIPT system. We further evaluate the impact of the distance and the power splitting (PS) strategy on the system performance using simulations. Finally, we build an optimization algorithm on the PS ratio by maximizing the admissible region from the theoretical perspective.

The recent breakthroughs in Wireless Power Transfer (WPT) facilitate supporting rechargeable sensors to enrich a series of energy-consuming applications. However, most charging scheduling schemes in rechargeable wireless sensor networks (WSNs) focus on sensing tasks instead of charging utility, which leaves a considerably high performance gap in the optimal result. Moreover, the charging scheduling is usually non-flexible, in which a full or nothing charging policy suffers from relatively low charging coverage as well as low efficiency. In this article, we focus on how to efficiently improve charging utility when introducing charging-oriented sensor placement and flexible scheduling policy. We formulate a general maximization optimization problem under a general routing constraint, which generates great difficulty. We utilize area partition and charging discretization methods to transform into the scope of maximizing a submodular function problem. Thus, a constant approximation algorithm is delivered to construct a near optimal charging tour. We analyze the performance loss from the discretization to guarantee that the output of the proposed algorithm has more than (1-ɛ)(1-1/ e )/4 of the optimal solution, where ɛ is an arbitrarily small positive parameter (0 < ɛ < 1). Both simulations and field experiments are conducted to evaluate the performance of our proposed algorithm.

The global IoT market is experiencing a fast growth with a massive number of IoT/wearable devices deployed around us and even on our bodies. This trend incorporates more users to upload data frequently and timely to the APs. Previous work mainly focus on improving the up-link throughput. However, incorporating more users to transmit concurrently is actually more important than improving the throughout for each individual user, as the IoT devices may not require very high transmission rates but the number of devices is usually large. In the current state-of-the-arts (up-link MU-MIMO), the number of transmissions is either confined to no more than the number of antennas (node-degree-of-freedom, node-DoF) at an AP or clock synchronized with cables between APs to support more concurrent transmissions. However, synchronized APs still incur a very high collaboration overhead, prohibiting its real-life adoption. We thus propose novel schemes to remove the cable-synchronization constraint while still being able to support more concurrent users than the node-DoF limit, and at the same time minimize the collaboration overhead. In this paper, we design, implement, and experimentally evaluate OpenCarrier, the first distributed system to break the user limitation for up-link MU-MIMO networks with coordinated APs. Our experiments demonstrate that OpenCarrier is able to support up to five up-link high-throughput transmissions for MU-MIMO network with 2-antenna APs.

With the proliferation of mobile and Internet of Things (IoT) devices, there has been an unprecedented growth of data consumption and computation requests at the network edge. To support latency-sensitive and resource-intensive mobile services, cellular base stations can be integrated with Mobile Edge Computing (MEC) technologies for service provisioning. MEC prompts flexible and configurable provisions of applications or services to make more efficient responses to mobile users' demands. Nevertheless, the time-varying nature of service demands inevitably becomes a vital challenge for existing service provisioning solutions. In this work, we propose MobiEdge, a multi-frame service provisioning scheme across two distinct timescales for MEC networks under various constraints of computation, communication and storage resources. We show that the large timescale indicated by ‘frame’ is more suitable for adjusting edge server activation and service placement, while the small timescale indicated by ‘time slot’ is more feasible to schedule users' requests. By leveraging the submodular techniques, we formulate a joint optimization problem and further propose an approximation algorithm with theoretical analysis and proofs. Synthetic and trace-driven evaluation results validate that MobiEdge can benefit both service providers and mobile users in MEC with high profits (e.g., 94% of the optimal) and a relatively low complexity.

Wireless power transfer (e.g., based on RF or magnetic) enables convenient device-charging, and triggers innovative applications that typically call for faster, smarter, economic, and even simultaneous adaptive charging for multiple smart-devices. Designing such a wireless charging system meeting these multi-requirements faces critical challenges, mainly including the better understanding of real-time energy receivers' status and the power-transferring channels, the limited capability and the smart coordination of the transmitters and receivers. In this work, we devise Camel, a context-aware MIMO MRC-WPT (magnetic resonant coupling-based wireless power transfer) system, which enables adaptive charging of multiple devices simultaneously with a novel context sensing scheme. In Camel, we craft an innovative MIMO WPT channels' state estimation and collision-aware in-band parallel communication among multiple transmitters and receivers. We design and implement the Camel prototype and conduct extensive experimental studies. The results validate our design and demonstrate that Camel can support simultaneous charging of as many as 10 devices, high-speed context sensing within 50 milliseconds, and efficient parallel communication among transceivers within proximity of ~0.5m.

The breakthrough of Wireless Power Transfer (WPT) technique provides a promising paradigm to tackle the energy limitation problem for end-devices when replenishing energy wirelessly without the need of replacing battery. Existing works seldom consider the mobility of rechargeable devices like miniature sensors on-body or implanted medical devices which may induce great gap between practical energy supply and demand. In this paper, we study the novel issue of Charging on the Move (CM) to optimize the scheduling of transmitting power of static chargers for mobile devices. Unfortunately, solving this problem is non-trivial, because it involves nonlinearity due to time-varying distances caused by movement. Besides, charging scheduling with tunable power level is a variant of budgeted maximum coverage problem, which is NP-hard. To address CM, we approximate the variational charging power as piecewise constant power, and divide the movement trajectories with approximated charging utility. Then, we first consider our problem with fixed power level, where each charger can be scheduled off or on at a fixed power level. We prove the submodularity of the objective function and design a .. approximation algorithm. On this basis, we further bound the performance loss during the problem reformulation, and finally propose a $\frac{{1 - 1/e}}{{2\left( {1 + \varepsilon } \right)T}}$ approximation algorithm for tunable scheduling strategy, where T is the maximum power level. Extensive simulations and trace-driven evaluations are conducted to evaluate the performance of our proposed algorithm.

For large-scale Internet of Things (IoT), backscatter communication is a promising technology to reduce power consumption and simplify deployment. However, due to the variable excitation source (ES) signal strength and time-varying channel condition, backscatter communication lacks stability, along with limited communication range as a few meters. Adaptive date rate (ADR) is beneficial to solve such issues, but is burdensome when implement on the capability limited tags. In this paper, we design a system named FreeBack with rate adaptation in backscatter communication. Our modulation approach is denoted as Adaptive Chirp-OOK where the ES recursively generates chirp signal, and the tags reflect the chirp signal with the On-Off Key modulation. According to channel symmetry, the tags perform rate adaption only based on the received ES signal strength instead of feedback from receiver. Such adaptation method enables the receiver to successfully decode signal through the time-varying channel, even for signal under the noise floor. We have implemented the prototype system based on the USRP platform. Extensive experiment results demonstrate the effectiveness of the proposed system. Our system provides valid ES-tag distance up to 27m, which is 7× as compared with normal backscatter system. FreeBack significantly increases the backscatter communication stability, by supporting data rate adaptation ranges from 0. 33kbps to 1. 2Mbps, and guaranteeing the bit error rate (BER) below 1%.

For effective crowdsensing, it is essential to incentivize the interactions of participants and platforms. Existing approaches do not tailor users’ bidding to their preferences, i.e., personalized bidding (PB). To meet this need, we design an incentive mechanism, called Picasso, that achieves not only the expressiveness and description efficiency of PB for users, but also minimal social cost, computational efficiency, and strategy proof for platform owners. This design is, however, challenging due to the intrinsic conflicting goals of the platform owner and users. To handle these conflicts, Picasso represents bids in a novel 3-D expression space by orchestrating three logical operations to balance among expressiveness, computational complexity, and description efficiency. Moreover, we equivalently decompose and recombine the complex task dependencies of bids originated from the expressiveness of PB, thus achieving a constant-factor approximation of optimal task allocation with strategy proof in polynomial time. These properties of Picasso are proven theoretically. In addition to a detailed simulation study, our trace-driven evaluations show that, compared to existing approaches, Picasso can enable each user to bid$9.7\times $more tasks, on average, and decrease the description length by 74%, thus encouraging more users’ participation. Picasso also reduces the platform owner’s payment by more than 61%, hence yielding a win–win solution for incentivizing platform–user interactions.

With the rapid development of smart cities, smart buildings are generating a massive amount of building sensing data by the equipped sensors. Indeed, building sensing data provides a promising way to enrich a series of data-demanding and cost-expensive urban mobile applications. In this paper, as a preliminary exploration, we study how to reuse building sensing data to predict traffic volume on nearby roads. Compared with existing studies, reusing building sensing data has considerable merits of cost-efficiency and high-reliability. Nevertheless, it is non-trivial to achieve accurate prediction on such cross-domain data with two major challenges. First, relationships between building sensing data and traffic data are not unknown as prior, and the spatio-temporal complexities impose more difficulties to uncover the underlying reasons behind the above relationships. Second, it is even more daunting to accurately predict traffic volume with dynamic building-traffic correlations, which are cross-domain, non-linear, and time-varying. To address the above challenges, we design and implement BuildSenSys, a first-of-its-kind system for nearby traffic volume prediction by reusing building sensing data. Our work consists of two parts, i.e., Correlation Analysis and Cross-domain Learning. First, we conduct a comprehensive building-traffic analysis based on multi-source datasets, disclosing how and why building sensing data is correlated with nearby traffic volume. Second, we propose a novel recurrent neural network for traffic volume prediction based on cross-domain learning with two attention mechanisms. Specifically, a cross-domain attention mechanism captures the building-traffic correlations and adaptively extracts the most relevant building sensing data at each predicting step. Then, a temporal attention mechanism is employed to model the temporal dependencies of data across historical time intervals. The extensive experimental studies demonstrate that BuildSenSys outperforms all baseline methods with up to 65.3 percent accuracy improvement (e.g., 2.2 percent MAPE) in predicting nearby traffic volume. We believe that this work can open a new gate of reusing building sensing data for urban traffic sensing, thus establishing connections between smart buildings and intelligent transportation.

Conventional vibration sensing systems, equipped with specific sensors (e.g., accelerometer) and communication modules, are either expensive or cumbersome to deploy. Recently research community revisits this classic topic by taking advantage of off-the-shelf RFIDs. However, limited by low reading rate and long wavelength, current RFID based solutions can only sense low-frequency (e.g., below 100 Hz) mechanical vibrations with larger amplitude (e.g., >5 mm). To address the issue, this work presents TagSound, an RFID-based vibration sensing system that explores a tag's harmonic backscattering to recover high-frequency and tiny mechanical vibrations accurately. The key innovations are in two aspects: harmonics based sensingand a newrecovery scheme. We implement TagSound with USRP platforms. Our comprehensive evaluation shows (i) TagSound can achieve a mean error of 0.37 Hz when detecting vibrations at frequencies below 100 Hz, and a mean error of 4.2 Hz even when the vibration frequency is up to 2500 Hz. (ii) TagSound can achieve a Hz-level frequency estimation even when the vibration amplitude is only 2 mm.

Driven by a wide range of real-world applications, several ground-breaking RF-based motion-recognition systems were proposed to detect and/or recognize macro/micro human movements. These systems often suffer from various interferences caused by multiple-users moving simultaneously, resulting in extremely low recognition accuracy. Even if the repetitive motions are fairly well detectable through the wireless signals in theory, in reality they get blended into various other system noises during the motion. Moreover, irregular motion patterns among users will lead to expensive computation cost for motion recognition. To tackle these challenges, we propose a novel wireless sensing system, calledMotion-Fi$\ ^+$+, which marries battery-free wireless backscattering and device-free sensing in one clean sheet.Motion-Fi$\ ^+$+is an accurate, interference tolerable motion-recognition system, which counts repetitive motions without using scenario-dependent templates or profiles and enables multi-user performing certain motions simultaneously because of the relatively short transmission range of backscattered signals and dedicated signal separation method. We implement a backscattering wireless platform to validate our design in various scenarios for over 6 months when different persons, distances and orientations are incorporated. In our experiments, the periodicity in motions could be recognized without any learning or training process, and the accuracy of counting such motions can be achieved within 5 percent count error. With little efforts in learning the patterns, our method could achieve 95.2 percent motion-recognition accuracy for a variety of 7 typical motions. Moreover, by leveraging the periodicity of motions, the recognition accuracy could be further improved to nearly 100 percent with only three repetitions. Our experiments also show that the motions of multiple persons separating by around$ 2$meters cause little accuracy reduction in the counting process.

RFID technology has recently been exploited for not only identification but also for sensing including trajectory tracking and gesture recognition. While contact-based (an RFID tag is attached to the target of interest) sensing has achieved promising results, contactless sensing still faces severe challenges such as low accuracy and the situation gets even worse when the target is non-static, restricting its applicability in real world deployment. In this work, we present TagRay, a contactless RFID-based sensing system, which significantly improves the tracking accuracy, enabling mobile object tracking and even material identification. We design and implement our prototype on commodity RFID device. Comprehensive experiments show that TagRay achieves a high accuracy of 1.3 cm which is a 200% improvement over the-state-of-arts for trajectory tracking. For commonly seen four material types, the material identification accuracy is higher than 95% even with interference from people moving around.

Mechanical vibration sensing/monitoring plays a critical role in today's industrial Internet of Things (IoT) applications. Existing solutions usually involve directly attaching sensors to the target objects, which is invasive and may affect the operations of delicate devices. Non-invasive approaches such as video and laser methods have drawbacks in that, the former incurs poor performance in low light conditions, while the latter has difficulties to monitor multiple objects simultaneously.In this work, we design RF-Ear, a contactless vibration sensing system using Commercial off-the-shelf (COTS) RFID hardware. RF-Ear could accurately monitor the mechanical vibrations of multiple (up to 8) devices using a single tag: it can clearly tell which object is vibrating at what frequency without attaching tags on any device. RF-Ear can measure the vibration frequency up to 400Hz with a mean error rate of 0.2%. Our evaluation results show that RF-Ear can effectively detect 0.2cm screw loose with 90% accuracy. We further employ each device's unique vibration fingerprint to identify and differentiate devices of exactly the same model. We also show that RF-ear can monitor not just the vibrations but also a large range of mechanical motions. Comprehensive experiments conducted in a real power plant demonstrate the effectiveness of our system with outstanding performance.

Magnetic resonant coupling wireless power transfer (MRC-WPT) enables convenient device-charging. When MIMO MRC-WPT system incorporated with multiple relay components, both relay On-Off state (i.e., power routing) and TX current (i.e., current scheduling) could be adjusted for improving charging efficiency and distance. Previous approaches need the collaboration and feedback from the energy receiver (RX), achieved using side-channels, e.g., Bluetooth, which is time/energy-consuming. In this work we propose, design, and implement a multi-relay MIMO MRC-WPT system, and design an almost optimum joint optimization of power routing and current scheduling method, without relying on any feedback from RX. We carefully decompose the joint optimization problem into two subproblems without affecting the overall optimality of the combined solution. For current scheduling subproblem, we propose an almost-optimum RX-feedback independent solution. For power routing subproblem, we first design a greedy algorithm with ½ approximation ratio, and then design a DQN based method to further improve its effectiveness. We prototype our system and evaluate it with extensive experiments. Our results demonstrate the effectiveness of the proposed algorithms. The achieved power transfer efficiency (PTE) on average is 3.2X, 1.43X, 1.34X, and 7.3X over the other four strategies: without relay, with non-adjustable relays, greed based, and shortest-path based ones.

The WiFi backscatter communications offer ultralow power and ubiquitous connections for IoT systems. Caused by the intermittent-nature of the WiFi traffics, state-of-the-art WiFi backscatter communications are not reliable for backscatter link or simple for the tag to do the adaptive transmission. In order to build reliable WiFi backscatter communications, we present GuardRider, a WiFi backscatter system that enables backscatter communications to improve the quality of service (QoS). The key contribution of GuardRider is an optimization algorithm of designing RS codes to follow the statistical knowledge of WiFi traffics and adjust backscatter transmission. With GuardRider, the reliable baskscatter link is guaranteed and a backscatter tag is able to adaptively transmit information without heavily listening to the excitation channel, by taking QoS into account. We built a hardware prototype of GuardRider using a customized tag with FPGA implementation. Both the simulations and field experiments verify that GuardRider could achieve notably gains in bit error rate and frame error rate, which are a hundredfold reduction in simulations and around 99% in filed experiments. Our system is able to achieve around 700 kbps throughput.

With the rapid advance of smart home and office systems, it becomes possible to provide a fine-grained user activity tracking service accurately recognizing user activities and identities in a seamless and non-invasive manner. Such a system can find applications in various domains, such as elder safeguard, customized services, and simply personal activity diary. Recently, several radio frequency (RF) based sensing systems were proposed for human sensing, most of which focus on limited scenarios and suffer from interference caused by other users or wireless devices. To tackle this challenge, we propose Back-Guard, which achieves accurate and non-intrusive user activity recognition and then user identification through battery-free wireless backscattering. Back-Guard carefully examines the backscatter spectrogram data and extracts high-level features from both spatial and temporal domains that can characterize the user behaviors. Leveraging the parallel attention based deep learning models, our system can discriminate different motions and users accurately and robustly in various situations. We implement a prototype system and collect data in actual scenarios from 25 users for over 2 months. Extensive experiments demonstrate the promising performance of our system. In particular, Back-Guard achieves 93.4% activity recognition accuracy and 91.5% user identification accuracy, respectively. Our experiments also demonstrate little accuracy reduction when multiple users are separated, e.g., by around 2 meters.

Falls are the leading cause of fatal and non-fatal injuries for the elderly, and fall detection system is critical for reducing the aid response time. Wearable sensor-based system becomes popular for its convenience and non-invasion of privacy. In this paper, we focus on fall detection system to be integrated into low-cost COTS (Commercial Off-The-Shelf) smart band, which is expected to be worn by the elderly for a long time due to its advantages of low price and low power consumption. Aiming at the low sampling rate property inherent in such devices, we analyze the characteristic of fall signal and propose a system which achieve better accuracy by combining the features of time domain, time-frequency domain and instantaneous frequency. We further apply data augmentation mechanism to tackle the issue of fall data lack. Extensive experiments are conducted to evaluate the proposed system. The results demonstrate that our system can achieve over 98% accuracy on our dataset and 97% accuracy on open source dataset.

To further improve the convenience and effectiveness of human computer interaction (HCI) with smart devices, human activity recognition (HAR) has been widely studied from various aspects. Unfortunately, deep learning based methods often suffer from either expensive labeling efforts or weak generalization ability. Inspired by recently developed domain adaptation strategies, we propose XHAR, a novel adversarial deep domain adaptation framework for HAR using smart devices, providing better device and user adaptation. XHAR first selects the most similar source dataset (with label), then extracts device and user independent spatial-temporal features through the combinations of Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (BiGRU) feature extractors. Moreover, it removes the distribution discrepancy using multiple domain discriminators, and finally performs adaptation on the target dataset (without label) to obtain the predicted labels. We conduct extensive experiments on 50 users (i.e., of different ages, genders, and body shapes) and 4 smart devices with two kinds of datasets (i.e., gesture activities and sport activities). We compare our method with the source-only model and several state-of-the-art domain adaptation models. The results show that XHAR increases the classification accuracy by at least 4.81% (to 74.50%) on the adaptation between different users, and accordingly by at least 9.25% (to 69.23%) between different devices.

Acoustic motion tracking is an exciting new research area with promising progress in the last few years. Due to the inherent low propagation speed in the air, acoustic signals have the unique advantage of fine sensing granularity compared to RF signals. Speakers and microphones nowadays are pervasively available in devices surrounding us, such as smartphones and voice-controlled smart speakers. Though promising, one fundamental issue hindering the adoption of acoustic-based motion tracking is that the positions of microphones and speakers inside a device are fixed, which greatly limits the flexibility of acoustic motion tracking. In this work, we propose a new modality of acoustic motion tracking using earphones. Earphone-based tracking mitigates the constraints associated with traditional smartphone-based tracking. With novel designs and comprehensive experiments, we show earphone-based motion tracking can achieve a great flexibility and a high accuracy at the same time. We believe this is an important step towards "earable" sensing.

Wireless power transfer (WPT) has emerged as a promising paradigm to charge devices due to the high reliability and efficiency of continuous power supply. Recent studies usually focus on relatively general charging patterns and metrics but neglect the collaborated task execution of nodes that incur charging inefficiency. In this article, we respect the energy requirement diversity among nodes to investigate the collaborated and tasks-driven mobile charging problem. Our goal is to maximize the overall task utility that concerns sensor selection and task cooperation. To address this problem, we propose a$(1-1/e)/4$-approximation algorithm. First, we propose a novel energy allocation scheme with a specific theoretical analysis of the submodularity and gap property for the surrogate function. Then, we approximate the traveling cost to transform the formulated problem into an essentially monotone submodular function optimization subject to a general routing constraint and propose a greedy algorithm to address this problem. We conduct extensive simulations to validate our theoretical results and the results show our algorithm can achieve a near-optimal solution covering at least 84.9% of the optimal result achieved by the OPT algorithm. Furthermore, field experiments in an office room and a soccer field environment are implemented, respectively, to validate our proposed algorithm.

This paper considers the fundamental problem of Placement of unmanned Aerial vehicles achieviNg 3D Directional coverAge (PANDA), that is, given a set of objects with determined positions and orientations in a 3D space, deploy a fixed number of UAVs by adjusting their positions and orientations such that the overall directional coverage utility for all objects is maximized. First, we establish the 3D directional coverage model for both cameras and objects. Then, we propose a Dominating Coverage Set (DCS) extraction method to reduce the infinite solution space of PANDA to a limited one without performance loss. Finally, we model the reformulated problem as maximizing a monotone submodular function subject to a matroid constraint and present a greedy algorithm with 1- 1/e approximation ratio to address this problem. We conduct simulations and field experiments to evaluate the proposed algorithm, and the results show that our algorithm outperforms comparison ones by at least 75.4%.

Due to the merit without requiring charging cable, wireless power transfer technology has drawn rising attention as a new method to replenish energy for Wireless Rechargeable Sensor Networks. In this article, we study the mobile charger scheduling problem for multi-node recharging with deadline constraints. Our target is to maximize the overall effective charging utility and minimize the traveling time for moving as well. Instead of charging only once over a scheduling cycle, we incorporate the multi-node charging strategy with deadline constraints, where charging spots and tour are jointly optimized. Specifically, we formulate the effective charging utility maximization problem as a monotone submodular function optimization subject to a partition matroid constraint, and we propose a simple but effective ½-approximation greedy algorithm. After that, we derive the result of global scheduling and present the grid-based skip-substitute operation to further save the traveling time, which can increase the charging utility. Finally, we conduct the evaluation for the performance of our scheduling scheme. The simulation and field experiment results show that our algorithm excels in terms of effective charging utility.

The ever-increasing number of IoT devices in our surrounding environment bring us tremendous amount of opportunities but also challenges including limited battery life, low computational capability and scalability of multiple access. Recent advances in backscatter communication have enabled ubiquitous IoT devices to communicate in a cost-and power-efficient way. However, most of the proposed backscatter solutions nowadays focus on the single tag paradigm, i.e., multiple tags do not transmit simultaneously and thus the solutions have difficulties to scale with a large number of tags. This work presents CBMA, a backscatter system that enables multiple concurrent backscatter tags to communicate reliably and efficiently. For the first time, we demonstrate that multiple tags can backscatter concurrently and efficiently with novel impedance-based power control at the tag, and can be successfully decoded with commodity WiFi devices without affecting the existing WiFi communication. We present the design details of CBMA and build a prototype with off-the-shelf WiFi devices and FPGA. The CBMA system achieves a 10-tag bit rate of 8Mbps while supporting a communication distance up to 10m. Compared to single-tag solutions, CBMA improves the backscatter throughput by more than 10× even in challenging indoor scenarios with rich multipath and interference.

Desktop is a reliable and amicable object carrier that accompanies us in our daily life, while working, eating and even entertaining. In this work, we devise a contactless but accurate object tracking system on desktop with commercial RFIDs. Comparing with conventional vision or acoustic based solutions, our system needs less computational resources and could be mucheasier for deployment. Moreover, ours could record the true positions for each device instead of the relative positions delivered in most of the previous studies. To this end, recording the user's access to the object on the desktop allows the user to interact with the smart device with simple actions. We present RFdesk, a contactless object location system that accurately locates every objects on the desktop. However, compare to tracking object withcontacted tag, several challenges are tackled before we make the system work. First of all, the signal employed for contactless tracking gets reflected twice which is thus much weaker, making the signal more susceptible to environmental noise and multipath. Another well-known challenge for contactless tracking is multi-target tracking as the signals reflected from multiple targets get mixed and interact. Extensive experiments show that RFdesk canflexibly deploy devices and tags, antennas, and localization itemscan be deployed on different planes. A median error of 3cm can be achieved for target tracking without attaching tags to the targets, even if there are 4 positioning objects on the desktop. Moreover, the average localization error can still be kept within 5.6cm outperforming the state-of-the-art systems by 100%.

Conventional vibration sensing systems, equipped with specific sensors (e.g., accelerometer) and communication modules, are either expensive or cumbersome in deployment. In recent years, the community revisits this classic topic by taking advantage of off-the-shelf RFIDs. However, limited by lower reading rate and larger wavelength, current RFID based solutions can only sense low-frequency (e.g. below 100Hz) mechanical vibrations with larger amplitude (e.g. (>) 5mm). To address this issue, this work presents TagSound, an RFID-based vibration sensing system that explores a tag's harmonic backscattering to recover high-frequency and tiny mechanical vibrations accurately. The key innovations are in two aspects: harmonics based sensing and a new recovery scheme. We implement TagSound with USRP platforms. Our comprehensive evaluation shows TagSound can achieve a mean error of 0.37 Hz when detecting vibrations at frequencies below 100Hz, and a mean error of 4.2 Hz even when the vibration frequency is up to 2500Hz.

WiFi has become a pervasive communication medium in connecting various devices of WLAN and IoT. However, WiFi connections are vulnerable to the impersonation attack from rogue access points (AP) or devices, whose SSID and/or MAC/IP address are identical to the legitimate devices. This kind of attack is difficult to countermeasure with traditional network security mechanisms. In this paper, we present a novel security mechanism to detect and identify rogue WiFi devices or AP using environment-independent characteristics extracted from channel state information (CSI), and refuse their connections. We find that nonlinear phase errors of different subcarriers change with WiFi network interface cards (NIC), due to the I/Q imbalance and imperfect oscillator of each WiFi NIC. Validated by our experiments, this phase feature across subcarriers is consistent and invariant to location and external environment, and can be extracted to build an essential signature of the NIC itself. Such signature of the transmitter can be calculated in real-time by the receiver and cannot be forged by rogue devices. Extensive experiments with dozens of WiFi devices demonstrate that the proposed mechanism can reliably detect the rogue WiFi connections and prevent impersonation in various scenarios. The speed of identification is 8× faster than that of the state-of-the-art solution. Moreover, the accuracy of rogue connection detection is up to 96% and false alarm rate is shown below 2%.

The recent breakthrough in Wireless Power Transfer (WPT) provides a promising way to support rechargeable sensors to enrich a series of energy-consuming applications. Unfortunately, two major design restrictions hinder the applicability of rechargeable sensor networks. First, most of the sensor placement schemes are focusing on the sensing tasks instead of the charging utility, which leaves a considerably high performance gap towards the optimal result. Second, the charging scheduling is non-flexible, where full or nothing charging policy suffers from the relatively low charging coverage as well as efficiency. In this paper, we focus on how to efficiently improve the charging utility when introducing charging oriented sensor placement and flexible scheduling policy. To this end, we jointly consider optimizing node positions and charging allocations. In particular, we formulate a general convex optimization problem under a general routing constraint, which generates great difficulty. We utilize area partition and charging discretization methods to reformulate a submodular function maximization problem. Thus a constant approximation algorithm is delivered to construct a near optimal charging tour. To this end, we analyze the performance loss from the discretization to guarantee that the output of the proposed algorithm has more than $(1 -\varepsilon)/4 (1 - 1 /e)$ of the optimal solution, where $\varepsilon$ is an arbitrarily small positive parameter $(0 \leq \varepsilon \leq 1)$. Both simulations and field experiments are conducted to evaluate the performance of our proposed algorithm.

Wireless Power Transfer (WPT) has emerged into an inspiringly commercial and applicable era to charge devices. Existing studies mainly focus on general charging patterns and metrics while overlooking the collaborated task execution, which incurs charging inefficiency among nodes. In this paper we first advocate the collaborated tasks-driven mobile charging and scheduling to respect the energy requirement diversity. Specially, the mobile charging scheduling strategy is considered to maximize the overall task utility which concerns sensor selection and task cooperation. Unfortunately, solving this problem is non-trivial, because it involves solving two coupling NP-hard problems. In tackling with this difficulty, we construct a surrogate function with specific theoretical analysis of its submodularity and gap property. Then, we approximate the traveling cost to transform the formulated problem into an essentially monotone submodular function optimization subject to a general routing constraint, where we propose $a (1-\ 1/e)/4$-approximation algorithm. Extensive simulations are conducted and the results show that our algorithm can achieve a near-optimal solution covering at least S4.9% of the optimal result achieved by the OPT algorithm. Furthermore, field experiments in office room and soccer field environment with 10 and 20 sensors are implemented respectively to validate our proposed algorithm.

In this work, we devise RF-DJ, a contactless music recognition system with the help of COTS RFID device. Since the music is caused by vibration and the vibration can influence the RF signal, our system could accurately recover the frequency of every tone, especially string instruments. Specifically, RF-DJ is immune to noises from the player/instrument motions and the ambient environment. Further more, it can recover the high frequency signal from the relatively low sampling rate data. In our demonstration, we put one tag on the surface of ukulele (not the string) and achieve the overall recognition accuracy of $93%, 90%, 87%, 81%$ when using 1,2,3,4 strings, respectively. Compared to typical machine learning based RF sensing systems, our system is model driven instead of data driven, which requires little training effort and could be applicable across different locations. Last but not the least, our system can also be used for other instruments such as zither, violin and kalimba and shows similarly good performances.

Sign language is a natural and fully-formed communication method for deaf or hearing-impaired people. Unfortunately, most of the state-of-the-art sign recognition technologies are limited by either high energy consumption or expensive device costs and have a difficult time providing a real-time service in a daily-life environment. Inspired by previous works on motion detection with wearable devices, we propose Sign Speaker - a real-time, robust, and user-friendly American sign language recognition (ASLR) system with affordable and portable commodity mobile devices. SignSpeaker is deployed on a smartwatch along with a smartphone; the smartwatch collects the sign signals and the smartphone outputs translation through an inbuilt loudspeaker. We implement a prototype system and run a series of experiments that demonstrate the promising performance of our system. For example, the average translation time is approximately $1.1$ seconds for a sentence with eleven words. The average detection ratio and reliability of sign recognition are 99.2% and 99.5%, respectively. The average word error rate of continuous sentence recognition is 1.04% on average.

We propose a novel motion sensing/recognition system, called DF-Mose, which marries low-power wireless backscattering and device-free sensing in one clean sheet. DF-Mose is an accurate, interference tolerable motion-recognition system that counts repetitive motions without using scenario-dependent templates or profiles within 5% count error and enables multiuser to perform certain motions simultaneously based on the nature of backscattered signals and dedicated signal separation method. With little efforts in learning the patterns, our method could achieve 95.2% motion-recognition accuracy for a variety of 7 typical motions.

In this paper, we propose a system called "RFRecorder" based on COTS RFID system which can inspect the string vibration and recognize the tone and tempo accurately. Our system can recover the music score played by some string instruments such as ukulele. Specifically, the string vibration influences the reflection of RF signal and causes a phase change. This change can be captured and analyzed to recover the frequency of the string vibration. In addition, the RFID tag is attached on the body of instrument but not on the string, which can not affect the music playing. Compared to the recorder, our system is immune to the environmental noise. Furthermore, it also can work on NLoS scenario. For single-string vibration, we use compressive sensing to recover the frequency duo to the low sampling rate of the COTS RFID. For multiple-string vibration, we recognize the music tone using machine learning method. We build a prototype and test the performance using guitar, ukulele and zither, which achieves 90%, 89%, 85% accuracy respectively.

Receiver (RX) feedback and power transfer channel estimation are essential for enhancing the context-aware ability for magnetic resonant coupling (MRC) enabled wireless power transfer (WPT) systems. Solutions are not highly efficient and immature in MIMO scenarios. In this work, we investigate the concurrent feedback communications for multiple RXs equipped with oscillating circuits. We discover an interesting clustering phenomenon, where the TX currents form clusters in corresponding to the combined RX "Open-Short" states. Based on this, we propose a parallel multi-stage decoding scheme by identifying the combined state for each cluster. In symbol clustering stage, we introduce two-layer clustering mechanism to tackle the "dominant RXs" challenge which causes inaccurate classification results. In cluster identification stage, we solve the "ambiguous identification candidates" challenge by calibration based on power transfer channel condition estimation. We have implemented the prototype testbed using off-the-shelf components. Our experiment results demonstrate the effectiveness of the proposed scheme. Our system supports communication within valid charging area even under significant interference from other RX. The simulation results further suggest the scalability of the proposed scheme, and the accuracy for power transfer channel estimation.

In this paper, we propose a system called R# to estimate the number of human objects using passive RFID tags but without attaching anything to human objects. The idea is based on our observation that the more human objects are present, the higher the variation in the RSS values of the tag backscattered RF signals. Thus, based on the received RF signals, the reader can estimate the number of human objects. R# includes an RFID reader and some (say 20) passive tags, which are deployed in the region that we want to monitor the number of human objects, such as the region in front of a supermarket shelf. The RFID reader periodically emits RF signals to identify all tags and the tags simply respond with their IDs via EPCglobal Class 1 Generation 2 protocol. We implemented R# using commercial Impinj H47 passive RFID tags and Impinj reader model R420. We conducted experiments in a simulated picking aisle area of the supermarket environment. The experimental results show that R# can achieve high estimation accuracy (more than 90 percent with up to ten human objects).

Doors as densely-deployed natural landmarks play an important role in improving indoor positioning systems. However, the state-of-the-art door event detection works are based on either vision or infrastructure, thus incurring non-trivial device or management cost. To address these problems, we present a Light-weight Magnetic-based Door Event Detection method, called LMDD. It leverages built-in magnetic sensors of common smartphones to achieve infrastructure-free door event detection. After analyzing the special features of sensors' readings changes caused by the door, we design LMDD scheme with three main components, including data acquisition, events identification and events denoising. Moreover, an improved and robust door event detection framework based on a majority-voting model is proposed to fuse multiple-dimensional sensing data from non-magnetic built-in sensors. We have implemented a prototype of LMDD on Android-based platform. Experimental results show that LMDD with only magnetic sensor achieves door event detection accuracy of around 80 percent on average, ranging from 70 to 87 percent in various typical indoor environments. The enhanced LMDD based on the fusion of heterogeneous sensors can achieve a much higher door event detection accuracy of 90 percent on average.

Mobile advertising has played an important role with the prevalence of smart mobile devices. Most of the previous studies focus on location-based or content-based mobile advertisement propagation and distribution, which are suffered by the low propagation efficiency, because advertisements could not be available to mobile users within limited time span. Conventional offloading schemes could perfectly distribute advertisements according to user's interest, but have not fully respected the time sensitivity in mobile advertisement distribution. In response to this stalemate, we introduce the advertisement platform's expected income maximization problem (EIMP), and prove its NP-hardness. To our knowledge, ours is even harder than conventional 0-1 mutlidimensional and multiple knapsack problem. But inspiringly we find that it could be transformed into a maximizing monotone submodular set function, being subjected to partition matroid constraints. Then a simple but effective greedy algorithm (TIMAO, time-sensitive mobile advertisement offloading)is proposed to solve the EIMP with approximation ratio of 1/3. Finally, the evaluation results show that TIMAO could double the platform's expected income comparing with the random selection method and reach 99.2% of the near optimal values achieved by CPLEX tool-box. At the same time, it increases the time duty cycle by about average 10% compared with the random selection.

This paper presents WordRecorder, an efficient and accurate handwriting recognition system that identifies words using acoustic signals generated by pens and paper, thus enabling ubiquitous handwriting recognition. To achieve this, we carefully craft a new deep-learning based acoustic sensing framework with three major components, i.e., segmentation, classification, and word suggestion. First, we design a dual-window approach to segment the raw acoustic signal into a series of words and letters by exploiting subtle acoustic signal features of handwriting. Then we integrate a set of simple yet effective signal processing techniques to further refine raw acoustic signals into normalized spectrograms which are suitable for deep-learning classification. After that, we customize a deep neural network that is suitable for smart devices. Finally, we incorporate a word suggestion module to enhance the recognition performance. Our framework achieves both computation efficiency and desirable classification accuracy simultaneously. We prototype our design using off-the-shelf smartwatches and conduct extensive evaluations. Our results demonstrate that WordRecorder robustly archives 81% accuracy rate for trained users, and 75% for users without training, across a range of different environment, users, and writing habits.

Recently several ground-breaking RF-based motion-recognition systems were proposed to detect and/or recognize macro/micro human movements. These systems often suffer from various interferences caused by multiple-users moving simultaneously, resulting in extremely low recognition accuracy. To tackle this challenge, we propose a novel system, called Motion-Fi, which marries battery-free wireless backscattering and device-free sensing. Motion-Fi is an accurate, interference tolerable motion-recognition system, which counts repetitive motions without using scenario-dependent templates or profiles and enables multi-users performing certain motions simultaneously because of the relatively short transmission range of backscattered signals. Although the repetitive motions are fairly well detectable through the backscattering signals in theory, in reality they get blended into various other system noises during the motion. Moreover, irregular motion patterns among users will lead to expensive computation cost for motion recognition. We build a backscattering wireless platform to validate our design in various scenarios for over 6 months when different persons, distances and orientations are incorporated. In our experiments, the periodicity in motions could be recognized without any learning or training process, and the accuracy of counting such motions can be achieved within 5% count error. With little efforts in learning the patterns, our method could achieve 93.1% motion-recognition accuracy for a variety of motions. Moreover, by leveraging the periodicity of motions, the recognition accuracy could be further improved to nearly 100% with only 3 repetitions. Our experiments also show that the motions of multiple persons separated by around 2 meters cause little accuracy reduction in the counting process.

Finger gesture input is emerged as an increasingly popular means of human-computer interactions. In this paper, we propose iPand, an acoustic sensing system that enables finger gesture input on the skin, which is more convenient, user-friendly and always accessible. Unlike previous works, which implement gesture input with dedicated devices, our system exploits passive acoustic sensing to identify the gestures, e.g. swipe left, swipe right, pinch and spread. The insight of our system is that specific gesture emits unique friction sound, which can be captured by the microphone embedded in wearable devices. We capture these acoustic signals and extract the features by using bandpass filters and short-time Fourier Transform. The offline convolutional neural network is adopted to recognize the gestures. iPand is implemented and evaluated using COTS smartphones and smartwatches. Experiment results show that iPand can achieve the recognition accuracy of 89%, 83% and 78% in three daily scenarios (i.e., library, lab and cafe), respectively. Particularly, our system supports multi-touch function where 2-4 fingers are enabled for more efficient and expressive gesture input, and its average accuracy for individual finger gesture reaches up to 83% within 12 gestures.

In this work, we study the mobile charger scheduling problem for multi-node charging with deadline constraints. In that, we aim at scheduling the charger to maximize the effective charging utility in dealing with the mismatch between time and spatial constraints. The local charging spots selection and globe traveling path should be jointly optimized, which is APX-hard. Nevertheless, our problem becomes much more complex with deadline constraints. To handle aforementioned challenges, we combine the spatial and temporal relevancy into a bipartite graph, and incorporate the multi-charging strategy instead of serving nodes strictly by the non-soft charging demands. We formulate the effective charging utility maximization problem into a monotone submodular function maximization subjected to a partition matroid constraint, and propose a simple but effective 1/2-approximation greedy algorithm. The results show that our scheme outperforms Early Deadline First (EDF) by 37.5%.

Acoustic Doppler shift estimation is a cost-effective way to implement Human-Computer Interaction applications across existing smart devices such as smart phones and smart spekaers. However, due to the inherent uncertainty principle in the traditional time-frequency analysis, it remains challenging to profile motions accurately and timely. In this paper, phase offset in acoustic OFDM signal is leveraged for developing Phascope, a fine-grained, fast and flexible motion profiling scheme. We evaluate Phascope using simulation and experiment on COTS devices. Sub-millisecond response time is achieved for Phascope in our experiment. Besides, with optimal subcarrier selection and SNR of 30 dB over all subcarriers, Phascope can estimate motion speed of 0.1 m/s with 6.75% root-mean-square error (RMSE) compared to optimized FFT method.

Finger gesture input is emerged as an increasingly popular means of human-computer interactions. In this demo, we propose iPand, an acoustic sensing system that enables finger gesture input on the skin, which is more convenient, user-friendly and always accessible. Unlike past works, which implement gesture input with dedicated devices, our system exploits passive acoustic sensing to identify the gestures, e.g. swipe left, swipe right, pinch and spread. The intuition underlying our system is that specific gesture emits unique friction sound, which can be captured by the microphone embedded in wearable devices. We then adopt convolutional neural network to recognize the gestures. We implement and evaluate iPand using COTS smartphones and smartwatches. Results from three daily scenarios (i.e., library, lab and cafe) of 10 volunteers show that iPand can achieve the recognition accuracy of 87%, 81% and 77% respectively.

In magnetic resonant coupling (MRC) enabled wireless power transfer (WPT) systems, the multiple-input multiple-output (MIMO) based technique, termed as ``magnetic beamforming", is used to enhance the efficiency of simultaneous power transfer to multiple receivers (RXs). In this paper, we study the requirement driven magnetic beamforming design in an MIMO MRC-WPT system, which is formulated as a weighted sum-power maximization (WSPMax) problem. We relax the peak current/voltage constraints, and prove that the optimal solution to the relaxed subproblem is choosing the transmitter current as an eigenvector of a constructed matrix. By discussing the WSPMax problem under special cases with limited power budget, we derive a close- form theoretical bound of the WSPMax problem, and demonstrate that the power transfer efficiency maximization problem can be solved through a transmitter-only method, i.e., without any communication feedback from RXs. More than just evaluation through simulation results, we also verify the proposed algorithm after prototyping the system with off-the-shelf components. Our results suggest the efficiency of the proposed algorithm, and its ability to performing requirement-driven power distribution among receivers.

A see-through-wall system can be used in life detection, military fields, elderly people surveillance. and gaming. The existing systems are mainly based on military devices, customized signals or pre-deployed sensors inside the room, which are very expensive and inaccessible for general use. Recently, a low-cost RFID technology has gained a lot of attention in this field. Since phase estimates of a battery-free RFID tag collected by a commercial off-the-shelf (COTS) RFID reader are sensitive to external interference, the RFID tag could be regarded as a battery-free sensor that detects reflections off targeted objects. The existing RFID-based system, however, needs to first learn the environment of the empty room beforehand to separate reflections off the tracked target. Besides, it can only track low-speed metal objects with high-positioning accuracy. Since the human body with its complex surface has a weaker ability to reflect radio frequency (RF) signals than metal objects, a battery-free RFID tag can capture only a subset of the reflections off the human body. To address these challenges, a RFID-based human motion sensing technology, called RF-HMS, is presented to track device-free human motion through walls. At first, we construct transfer functions of multipath channel based on phase and RSSI measurements to eliminate device noise and reflections off static objects like walls and furniture without learning the environment of the empty room before. Then a tag planar array is grouped by many battery-free RFID tags to improve the sensing performance. RF-HMS combines reflections from each RFID tag into a reinforced result. On this basis, we extract phase shifts to detect the absence or presence of any moving persons and further derive the reflections off a single moving person to identify his/her forward or backward motion direction. The results show that RF-HMS can effectively detect the absence or presence of moving persons with 100% accuracy and keep a high accuracy of more than 90% to track human motion directions.

Acoustic sensing has brought forth the advances of prosperous applications such as gesture recognition. Specifically, ambient acoustic sensing has drawn many contentions due to the ease of use property. Unfortunately, the inherent ambient noise is the major reason for unstable gesture recognition. In this work, we propose “SoundWrite II”, which is an improved version of our previously designed system. Compared with our previous design, we utilize the two threshold values to identify the effective signals from the original noisy input, and leverage the MFCC (Mel frequency cepstral coefficient) to extract the stable features from different gestures. These enhancements could effectively improve the noise tolerant performance for previous design. Implementation on the Android system has realized the real time processing of the feature extraction and gesture recognition. Extensive evaluations have validated our design, where the noise tolerant property is fully tested under different experimental settings and the recognition accuracy could be 91% with 7 typical gestures.

Sign Language is a natural and fully-fledged communication method for deaf and hearing-impaired people. In this demo, we propose the first SmartWatch-based American sign language (ASL) recognition system, which is more comfortable, portable and user-friendly and offers accessibility anytime, anywhere. This system is based on the intuitive idea that each sign has its specific motion pattern which can be transformed into unique gyroscope and accelerometer signals and then analyzed and learned by using Long-Short term memory recurrent neural network (LSTM-RNN) trained with connectionist temporal classification (CTC). In this way, signs and context information can be correctly recognized based on an off-the-shelf device (eg. SmartWatch, Smartphone). The experiments show that, in the Known user split task, our system reaches an average word error rate of 7.29% to recognize 73 sentences formed by 103 ASL signs and achieves detection ratio up to 93.7% for a single sign. The result also shows our system has a good adaptation, even including new users, it can achieve an average word error rate of 21.6% at the sentence level and reach an average detection ratio of 79.4%. Moreover, our system performs real time ASL translation, outputting the speech within 1.69 seconds for a sentence of 12 signs in average.

In this work, we leverage the reflection property explored in our experimental results and propose an efficient and effective algorithm for the reconstruction of the layout. The proposed algorithm identifies the reflective path, which is caused by obstacle, and evaluated by the difference of Angle of Arrival (AoA) at each transceiver deployed at the room. Then with the knowledge of AoA corresponding to the affected reflective path, the reflection points can be calculated. These reflection points could be used to outline the surface of obstacle. Moreover, we can estimate the shape, size, and location of the obstacle by using AlphaShape algorithm. The simulation results prove the effectiveness of our algorithm.

Summary This paper investigates the selfish load balancing problem in mobile distributed crowdsourcing networks. Conventional methods heavily relied on cooperation among users to achieve balanced resource utilization in a platform‐centric view. In achieving fairly low communication and computational overhead, this work leverages the d‐choice method based on Ball and Bin theory for effective balancing under limited information and the Proportional Allocation scheme for selfish load balancing, maintaining good load balancing property among selfish users. Even with limited information, the balancing performance could be improved significantly. Moreover, theoretical analysis has been presented in convergence property. Extensive evaluations have been made to show that Chance‐Choice outperforms several existing algorithms. Typically, comparing with Proportional Allocation scheme, it could decrease the load gap between the maximum and the minimal in system by 50% to 80% and reduce the overhead complexity from O(n) to O(1) comparing with the Max‐weight Best Response algorithm, where n denotes the number of mobile users in a crowdsourcing system. Copyright © 2017 John Wiley & Sons, Ltd.

Summary Mobile advertisement distribution effects are vitally important for advertisers as well as users. Status quo studies are focusing on efficient distribution especially when user mobilities are involved. Unfortunately, previous studies have shown the interested area property during mobile advertisement propagation. In achieving efficient and effective mobile advertisement applications, this work advocates the concept of location‐centric mobile crowdsourcing network, where locations are vitally important for advertisement distribution, and mobile users need to be carefully selected for efficiency considerations. Different from traditional user‐centric and platform‐centric crowdsourcing networks, this work focuses on the mobile advertisement user selection problem when interested area coverage is considered. There are several fundamentally important challenges needed to be addressed before developing a location‐centric scheme for mobile advertisement user selection. First of all, we need to deal with the spatio‐temporal features for each user, where the interested area coverage ratio needs to be effectively evaluated. Even worse, budget constraint makes this problem intractable. In tackling aforementioned challenges, this work makes the following efforts: First, a budget‐constrained user selection problem is formulated when location sensitive mobile advertisement applications are considered, which is proved NP‐hard. Second, the submodularity feature is explored, and a simple but efficient heuristic algorithm is presented with guaranteed approximation ratio . Finally, extensive simulation results show that, our scheme could effectively improve the propagation effects for mobile advertisement with 125%. When considering the user's interest to different advertisements, the real user interest data set has also been used to validate that our proposed method could achieve improved performance than the random method.

We propose and implement a novel positioning system, WalkieLokie, which directly calculates the relative position from a smart device to a target. The requirement of the target is simple: it is attached with a “dummy” acoustic speaker, which does not have any other rich capabilities, such as audio recording, communication, or computation. Hence, the proliferation of smart devices, together with the cheap accessory (e.g., dummy speaker) embedded in daily used items (e.g., smart clothes), paves the way for WalkieLokie applications. WalkieLokie leverages the walking motion for locating an acoustic speaker. The key insight is that the distance between the user and the speaker varies in real time when the user walks, and the pattern of the variance implies the relative position. We design a novel algorithm to estimate the position and signal processing methods to support accurate positioning. The experiment results show that the mean errors of ranging and direction estimation are 0.63 m and 2.46°, respectively. Extensive experiments conducted in noisy environments validate the robustness of WalkieLokie.

Due to rapid developments of smart devices and mobile applications, there is an urgent need for a new human-in-the-loop architecture with better system efficiency and user experience. Compared with conventional device-based human-computer interactive (HCI) methods, device-free technology with WiFi provides a new HCI method and is promising for providing better user-perceived quality-of-experience. Being essential for device-free applications, device-free human detection has gained increasing interest, of which through-the-wall (TTW) human detection is of great challenge. Existing TTW detection systems either rely on massive deployment of transceivers or require specialized WiFi monitors, making them inapplicable for real-world applications. Recently, more and more researchers have tapped into the physical layer for more robust and reliable human detection, ever since channel state information (CSI) can be exported with commodity devices. Despite great progress achieved, there have been few works studying TTW detection. In this paper, we propose a novel scheme for robust device-free TTW detection (R-TTWD) of a moving human with commodity devices. Different from the time dimension-based features exploited in the previous works, R-TTWD takes advantage of the correlated changes over different subcarriers and extracts the first-order difference of eigenvector of CSI across different subcarriers for TTW human detection. Instead of direct feature extraction, we first perform a PCA-based filtering on the preprocessed data, since a simple low-pass filtering is insufficient for noise removal. Furthermore, the detection results across different transmit-receive antenna pairs are fused with a majority-vote-based scheme for more robust and accurate detection. We prototype R-TTWD on commodity WiFi devices and evaluate its performance both in different environments and over long test period, validating the robustness of R-TTWD with both detection rates for moving human and human absence over 99% regardless of different wall materials, dynamic moving speeds, and so on.

Regular free-weight exercise helps to strengthen natural movements and stabilize muscles that are important to strength, balance, and posture of human beings. Prior works have exploited wearable sensors or RF signal changes for activity sensing, recognition, and counting, etc.. However, none of them have incorporated three key factors necessary for a practical free-weight exercise monitoring system: recognizing free-weight activities on site, assessing their qualities, and providing useful feedbacks to the bodybuilder promptly. Our FEMO system provides an integrated free-weight exercise monitoring service that incorporates all the essential functionalities mentioned above. FEMO achieves this by attaching passive RFID tags on the dumbbells and leveraging the Doppler shift profile of the reflected backscatter signals for on-site free-weight activity recognition and assessment. The rationale behind FEMO is 1) since each free-weight activity owns unique arm motions, the corresponding Doppler shift profile should be distinguishable to each other. 2) Doppler profile of each activity has a strong spatial-temporal correlation that implicitly reflects the quality of the activity. We implement FEMO with COTS RFID devices and conduct a two-week experiment. The preliminary result from 15 volunteers demonstrates that FEMO can be applied to a variety of free-weight activities, and provide valuable feedbacks for activity alignment.

Recent years have seen major innovations in cooperative wireless networks. Despite the fact that throughput gains have been achieved in packet recovery, hardly any of these technologies could effectively decode signals when the number of signal sources is greater than available antennas in each AP. Thus, conventional cooperative methods rely on adaptive scheduling for interference-free data packets. Deploying scheduling-free cooperative signal recovery requires prompt processing with low overheads. Yet potentially, the concurrent client transmissions could overwhelm any single AP, i.e., the number of concurrent transmissions are more than that of antennas. This paper presents the first step towards breaking this stalemate, by enabling symbol alignment and constellation reinforcement instead of relying on scheduling. We present POLYPHONY, a scheduling-free cooperative design, where decoding process could be coordinated without over-the-airs-cheduling, and coupled signals are decoded promptly after deep cooperations. We implement POLYPHONY prototype with GNURadio/USRP platform, and deploy it witha16-node enterprise network. Particularly, we demonstrate how it manages the complex interactions with scheduling-free signal enforcement, and enables a beyond node-DoF (Degree of Freedom) decoding with AP coordinations. Furthermore, we show how the cooperative decoding process improves radio access among clients. Our results demonstrate a gain of nearly 200 percent for network throughput, which is a significant improvement for heavy contending networks.

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

In this paper, we propose and implement WalkieLokie, a novel acoustic-based relative positioning system. WalkieLokie facilitates a multitude of Augmented Reality (AR) applications: users with smart devices can passively acquire surrounding information in real time, similar to the commercial AR system Wikitude; the surrounding presenters, who want to share information or introduce themselves, can actively launch the function on demand. The key rational of WalkieLokie is that a user can perceive a series of spatial-related acoustic signals emitted from a presenter, which depicts the relation position between the user and the presenter. The proliferation of smart devices, together with the cheap accessory (e.g., dummy speaker) embedded in daily used items (e.g., smart clothes), paves the way for WalkieLokie applications. We design a novel algorithm to estimate the position and signal processing methods to support accurate positioning. The experiment results show that the mean error of ranging and direction estimation is 0.63m and 2.46 degrees respectively. Extensive experiments conducted in noisy environments validate the robustness of WalkieLokie.

Identifying the object that attracts human visual attention is an essential function for automatic services in smart environments. However, existing solutions can compute the gaze direction without providing the distance to the target. In addition, most of them rely on special devices or infrastructure support. This paper explores the possibility of using a smartphone to detect the visual attention of a user. By applying the proposed VADS system, acquiring the location of the intended object only requires one simple action: gazing at the intended object and holding up the smartphone so that the object as well as user's face can be simultaneously captured by the front and rear cameras. We extend the current advances of computer vision to develop efficient algorithms to obtain the distance between the camera and user, the user's gaze direction, and the object's direction from camera. The object's location can then be computed by solving a trigonometric problem. VADS has been prototyped on commercial off-the-shelf (COTS) devices. Extensive evaluation results show that VADS achieves low error (about 1.5° in angle and 0.15m in distance for objects within 12m) as well as short latency. We believe that VADS enables a large variety of applications in smart environments.

In this paper, we consider typical wireless energy harvesting network, where a mobile charging vehicle is scheduled to serve a wireless sensor network. Specifically, for practical considerations such as deployment restrictions, the charging vehicle could not provide full efficiency energy supply to sensor nodes. For wireless energy harvesting, there is an inevitable tradeoff between the charging distance and the angle. We focus on optimizing the charging efficiency when the distance and angle factors are concerned. Scheduling charging vehicle for rechargeable nodes in previous studies has been proved to be NP hard. Even worse, the non-linear property between the charging distance and angle should be carefully considered, which makes the problem even harder. We investigate how to minimize the recharging cycle for all the deployed sensors in network, which contains the traveling time and recharging time. With this problem, we show the charging vehicle is required to move at the shortest Hamiltonian cycle. And we present optimal charging location for each wireless charging incident. Experimental results demonstrate that, our proposed solution could enhance the charging efficiency up to 2 times comparing with the baseline scheme without optimization for angle.

Mobile Advertisement propagation has drawn increasing attention in research and industrial area.In this work, we investigate the mobile advertisement popularization for mobile social networks.Previous studies failed to be applied to mobile social networks because of extremely high overhead and low propagation efficiency.In tackling these difficulties, we propose eMAP, (efficient mobile advertisement popularization), an efficient propagation user selection scheme with local information.Two key technologies enable eMAP to achieve efficient and effective mobile Ads popularization.First, we advocate propagation user selection instead of popular user selection, where mobile users with strong information dissemination ability could be selected.Thus the mobile user could be effectively used.Second, we use local information instead of the global information to achieve near optimal performance for propagation.In that, the information potential is leveraged to find the influential users with local information.With extensive experimental study, we find that, eMAP could effectively improve the mobile Ads delivery ratio.Using the propogation instead of popularization is validated in our experimental studies in different aspects of investigations. Moreover, when the budget is constrained, eMAP could still perform fairly well.

Abstract With the pervasive use of smart mobile devices and increasing wireless networking technologies, collaborations among mobile users are becoming deeper and ubiquitous. Appropriate task collaborations among mobile users could effectively improve the network processing ability with so called ‘mobile cloud’ or ‘cloudlet’. However, task allocations confront with the security issues. The possible collusion or re‐collaborations among the mobile users would possibly merge the allocated tasks of the specific users. Moreover, considering the delivery reliability and task execution efficiency, replications are applied for enhancement, which would also lead to more sever security threat for users. We investigate how to secure the security when task collaborations are allowed for mobile users. Our security scheme is built upon the load balancing scheme, and our intuitive solution is, if the tasks could be effectively balanced among users, the security issues could be guaranteed, because averaging the task assignment could effectively raise the threshold for collusion among potential malicious users. In this work, we propose ‘SAFE‐CROWD’: a secure task offloading and reassignment scheme among mobile users. The basic idea is simple, we leverage the ‘ball and bin’ theory for task assignment, wheredmobile users in contact range are investigated, and we select the least loaded ones among them. It has been proved that such simple cases can effectively reduce the largest queueing length from to . Inspired by this theoretical result, we develop a task reassignment policy for security issues. Simulation and trace‐driven studies have shown that our simple but effective scheme could enhance the security for mobile users, when the tasks are collaboratively executed among mobile devices. Copyright © 2015 John Wiley & Sons, Ltd.

In this work, we address the spectrum utilization problem in cognitive radio (CR) networks, in which a CR can only utilize spectrum opportunities when the channel is idle. One challenge for a CR is to balance exploring new channels and exploiting existing channel, due to the fact that the channel availability and channel quality, potentially heterogeneous and time-dependent, are often unknown in advance due to the large number of channels, and the limited hardware capability of single CR. In this work, we propose joint channel sensing, probing, and accessing schemes for secondary users in cognitive radio networks. Our method has time and space complexity O(N · u) for a network with N channels and u secondary users, while applying classic methods requires exponential time complexity. We prove that, even when channel states are selected by adversary (thus nonstochastic), it results in a total regret uniformly upper bounded by Θ(√TN log N), w.h.p, for communication lasts for T timeslots. Our protocol can be implemented in a distributed manner due to the nonstochastic channel assumption. Our experiments show that our schemes achieve almost optimal throughput compared with an optimal static strategy, and perform significantly better than previous methods in many settings.

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

Recent studies show that Wi-Fi interference has been a major problem for low power urban sensing technology ZigBee networks. Existing approaches for dealing with such interferences often modify either the ZigBee nodes or Wi-Fi nodes. However, massive deployment of ZigBee nodes and uncooperative Wi-Fi users call for innovative cross-technology coexistence without intervening legacy systems. In this work, we investigate the Wi-Fi and ZigBee coexistence when ZigBee is the interested signal. Typically, the duration of transmitting a ZigBee data packet is longer than that of a Wi-Fi packet. Mitigating short duration Wi-Fi interference (calledflash) in long duration ZigBee data (calledsmog) is challenging. To address these challenges, we propose ZIMO: a sink-based MIMO design for harmony coexistence of ZigBee and Wi-Fi networks with the goal of protecting the ZigBee data packets from being interfered by high-power cross-technology signals. The key insight is to properly exploit opportunities resulted from differences between Wi-Fi and ZigBee, and bridge the gap between interested data and cross technology signals. Also, extracting the channel coefficient of Wi-Fi and ZigBee will enhance other coexistence technologies such as TIMO[1]. We implement a prototype in GNURadio-USRP N200, and our extensive evaluations under real wireless conditions show that ZIMO can improve ZigBee network throughput up to 1.9$\times$, with 1.5$\times$in media, and 1.1$\times$to 1.9$\times$for Wi-Fi network as byproduct in ZigBee signal recovery.

Physical-layer identification utilizes unique features of wireless devices as their fingerprints, providing authenticity and security guarantee. Prior physical-layer identification techniques on radio frequency identification (RFID) tags require nongeneric equipments and are not fully compatible with existing standards. In this paper, we propose a novel physical-layer identification system, GenePrint, for UHF passive tags. The GenePrint prototype system is implemented by a commercial reader, a USRP-based monitor, and off-the-shelf UHF passive tags. Our solution is generic and completely compatible with the existing standard, EPCglobal C1G2 specification. GenePrint leverages the internal similarity among pulses of tags' RN16 preamble signals to extract a hardware feature as the fingerprint. We conduct extensive experiments on over 10 000 RN16 preamble signals from 150 off-the-shelf RFID tags. The results show that GenePrint achieves a high identification accuracy of 99.68% +. The feature extraction of GenePrint is resilient to various malicious attacks, such as the feature replay attack.

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

Abstract In heterogeneous wireless networks, network selection algorithms provide the user with the optimum network access choice. The optimal network is evaluated according to network parameters. Considering that the network parameters are dynamic and unavailable for the user in realistic heterogeneous wireless network environments, most existing network selection algorithms cannot work effectively. Learning‐based algorithms can address the problem of uncertain network parameters, while they commonly need considerable network handoff, resulting in unbearable handoff cost. In order to tackle the uncertainty of network parameters, we formulate the network selection problem as a multi‐armed bandit problem. Moreover, two online learning‐based network selection algorithms with a special consideration on reducing network handoff cost are proposed. By updating in a block manner, both algorithms achieve optimal logarithmic‐order regret and limited network handoff cost. The simulation indicates that the two algorithms can significantly reduce the network handoff cost and improve the transmission performance compared with existing algorithms, simultaneously. Copyright © 2014 John Wiley & Sons, Ltd.

In this paper, we propose a novel indoor localization scheme, Lightitude, by exploiting ubiquitous visible lights, which are necessarily and densely deployed in almost all indoor environments. Different from existing positioning systems that exploit special LEDs, ubiquitous visible lights lack fingerprints that can uniquely identify the light source, which results in an ambiguity problem that an RLS may correspond to multiple candidate positions. Moreover, received light strength (RLS) is not only determined by device's position, but also seriously affected by its orientation, which causes great complexity in site-survey. To address these challenges, we first propose and validate a realistic light strength model to avoid the expensive site-survey, then harness user's mobility to generate spatial-related RLS to tackle single RLS's position-ambiguity problem. Experiment results show that Lightitude achieves mean accuracy 1.93m and 2.24m in office (720m2) and library scenario (960m2) respectively.

In this paper, we propose a system called R# to estimate the number of human objects using passive RFID tags but without attaching anything to human objects. The idea is based on our observation that the more human objects are present, the higher the variance in the RSS values of the tag backscattered RF signal. Thus, based on the received RF signal, the reader can estimate the number of human objects. R# includes an RFID reader and some (say 20) passive tags, which are deployed in the region that we want to monitor the number of human objects, such as the region in front of a painting. The RFID reader periodically emits RF signal to identify all tags and the tags simply respond with their IDs via C1G2 standard protocols. We implemented R# using commercial Impinj H47 passive RFID tags and Impinj reader model R420. We conducted experiments in a simulated picking aisle area of the supermarket environment. The experimental results show that R# can achieve high estimation accuracy (more than 90%).

Robust Device-free passive (Dfp) detection is an essential primitive for a broad range of applications such as intrusion detection and smart space. Most recent works focus on finer-grained Channel State Information (CSI), instead of the variable Received Signal Strength (RSS). However, existing solutions have some limitations, being feasible only in the line of sight (LOS) or for more than one targeted entities. Moreover, space diversity supported by the MIMO systems hasn't been fully investigated. Motivated by this observation, we propose a novel scheme for Robust Passive Motion Detection (R-PMD). In our scheme, the variance of CSI amplitude feature is extracted as a new metric and the earth mover's distance (EMD) is utilized to determine the detection results. Besides, CSIs across multiantennas are further exploited to improve the detection precision and robustness. We prototype R-PMD on commercial WiFi devices and evaluate it in a typical indoor scenario. Experiment results show R-PMD can achieve great performance in terms of sensitivity and robustness.

With the increasing development of mobile networking technologies, optimization methods for efficient task assignment plays a key role for mobile crowdsourcing process. However, what hiding behind the strategies are solutions to motivate users for participation, which reveals a fundamental problem: the fairness issue of crowdsourcing system. Since the participators are human beings with intensive interest for obtaining benefits, it is reasonable to build a sustainable crowd with guaranteed fairness among users. Thus in this study, we investigate the fairness issue in mobile social network, which could be more complicated when uncontrollable mobile users are concerned. The intuitive solution is, if the tasks could be effectively assigned among users in a balanced way, the fairness could be guaranteed. Unfortunately, there is still a big challenge for this issue, because it's difficult to acquire accurate global information of task loading, which is highly dynamic and distributed. By leveraging the power of two random choices, which is based on the balls and bins theory, we develop a lightweight scheme to allocate tasks. Indeed, we proposed a heuristic algorithm to achieve balanced task allocation effectively with O(1) complexity. To the best of our knowledge, it is the first effort for incorporating fair load balancing in pure distributed mobile crowdsourcing systems. Our extensive evaluation results validate our task offloading algorithm, showing that the proposed scheme outperforms the random choice method.

Regular free-weight exercise helps to strengthen the body's natural movements and stabilize muscles that are important to strength, balance, and posture of human beings. Prior works have exploited wearable sensors or RF signal changes (e.g., WiFi and Blue tooth) for activity sensing, recognition and countingetc.. However, none of them have incorporate three key factors necessary for a practical free-weight exercise monitoring system: recognizing free-weight activities on site, assessing their qualities, and providing useful feedbacks to the bodybuilder promptly. Our FEMO system responds to these demands, providing an integrated free-weight exercise monitoring service that incorporates all the essential functionalities mentioned above. FEMO achieves this by attaching passive RFID tags on the dumbbells and leveraging the Doppler shift profile of the reflected backscatter signals for on-site free-weight activity recognition and assessment. The rationale behind FEMO is 1): since each free-weight activity owns unique arm motions, the corresponding Doppler shift profile should be distinguishable to each other and serves as a reliable signature for each activity. 2): the Doppler profile of each activity has a strong spatial-temporal correlation that implicitly reflects the quality of each performed activity. We implement FEMO with COTS RFID devices and conduct a two-week experiment. The preliminary result from 15 volunteers demonstrates that FEMO can be applied to a variety of free-weight activities and users, and provide valuable feedbacks for activity alignment.

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

Coexistence of Wi-Fi and ZigBee in 2.4 GHz ISM band is a long standing and challenging problem. Previous solutions either require modifications of current ZigBee protocols or Wi-Fi re-configurations, which is not feasible in large-scale wireless sensor networks. In this paper, we present WizBee, a coexistence system using single-antenna sink without changing current Wi-Fi and ZigBee design. WizBee is based on an observation that Wi-Fi signal is about 5 to 20 dB stronger than ZigBee signal in symmetric area, which leaves much room for applying interference cancelation technique to mitigate Wi-Fi interference, and extract ZigBee signals. However, we need to cancel the Wi-Fi interference perfectly for residual ZigBee signal decoding, which needs more accurate channel coefficient across data transmissions in spite of cross technology interference. For robust and accurate Wi-Fi decoding, we use soft Viterbi decoding with weighted confidence value over interfered subcarriers. Consequently, our solution uses decoded data for channel coefficient estimation instead of conventional training symbol based methods. The key insight is that, the signal recovery opportunity for cross technology coexistence, lies in multi-domain information, such as power, frequency and coding discrepancies. Using these information properly will improve the coexistence network throughput effectively. We implemented WizBee in USRP/GNURadio software radio platform, and studied the decoding performance of interference cancelation technique. Our extensive evaluations under real wireless conditions show that WizBee improves ZigBee throughput up to 1.9x, with median throughput gain of 1.2x.

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

Radio resource management (RRM) is crucial for improving resource utilization in heterogeneous wireless networks. Existing work attempts to exploit the network diversity to gain throughput improvement for users, which, however, neglects the impact of user demand on RRM. Armed with the idea that the ultimate goal of communications is to serve users with personalized demand, we introduce another dimension of potential performance gain, user demand diversity gain. This gain derives from the elaborate matching between user demand and radio resource, which can not be directly attained in existing throughput-centric optimization due to users' blindness in maximizing throughput. Aiming at obtaining this gain, we propose the user demand-centric optimization, where users seek to maximize quality of experience (QoE), instead of throughput. This shift enables us to propose a novel game formulation, QoE game. We derive the condition on the existence of the QoE equilibrium, validate the user demand diversity gain and propose a distributed QoE equilibrium learning algorithm. Finally, a cloud assisted learning framework is proposed to accommodate the learning algorithm with significantly reduced cost. Simulation results validate the existence of user demand diversity gain and the effectiveness of the proposed learning algorithm in improving the system efficiency and QoE fairness.

In opportunistic channel access, the user needs to make real time decisions on when and which channel to access with uncertainty. Assuming perfect channel statistics, several studies have applied optimal stopping theory to derive control strategy for sequential sensing/probing based opportunistically accessing (s-SPA), exploiting temporary opportunities among multiple channels. Meanwhile, numerous multi-arm bandit (MAB)-based approaches have been proposed for online learning of channel selection in periodical sensing/accessing system, however, these schemes fail to exploit the opportunistic diversity in short term. In this paper, we investigate online learning of optimal control in s-SPA systems, where both statistics learning and temporary opportunity utilization are jointly considered. An effective and efficient online policy, so called IE-OSP, is proposed, which theoretically guarantees system converges to the optimal s -SPA strategy with bounded probability. Experimental results further show that, the regret of IE-OSP is almost in optimal logarithmic increasing rate over time, and is sub-linear with the increasing number of channels. Compared with existing solutions, our proposed algorithm achieves 25 ~ 30% throughput gain in typical scenarios.

In this work, we investigate the distributed and balanced task reassignment in mobile social networks. Previous studies have shown the power of random choice in load balancing with random walking model. Inspired by the `2-choice' paradigm in `ball and bin' theory, we evaluate this simple but effective scheme with real trace data `MobiClique'. According to the preliminary evaluation results, we find that, social relationship significantly differs from pure random walk model, and will bring challenges in task reassignment in the followings: First, friendships are relatively stable, which will lead to imbalanced task assignment. Second, some users meet quite infrequently, which will lead to intolerable time delay and uneven task distribution. In tackling with these challenges, we propose `iTop-K', leveraging the basic concept, i.e., your friends are more powerful than you, which encourages mobile users to assign tasks among intimate friends instead of pure random assignment. With the selection of `top-K' friends, we can achieve load balancing and guaranteed network performance at the same time. Experimental studies verify our scheme and show the effectiveness. In typical working scenario, where real-trace driven simulation is applied, ours outperforms the conventional random choice up to 15×, and the social relationship assignment without priority method up to 9×.

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

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

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

Compressive Sensing (CS) has been recognized as a promising technique to reduce and balance the transmission cost in wireless sensor networks (WSNs). Existing efforts mainly focus on applying CS to reliable WSNs, namely, each wireless link is 100% reliable. However, our experimental results show that traditional compressive data gathering (CDG) could result in arbitrarily bad recovery performance, when the wireless links are lossy. In this paper, we study the impact of packet loss on compressive data gathering and ways to improve its robustness using sparsest random scheduling (SRS). The key idea of our scheme is to treat each sampling value as one CS measurement, which helps us to reduce the impact of packet loss on the recovery accuracy. Our scheme also outperforms the tradition CDG in reliable WSNs in that our scheme has significantly lowered transmission cost. To achieve this, we present a sparsest measurement matrix where each row has only one nonzero element. More importantly, we propose a representation basis to sparsify the gathering data, and prove that our measurement matrix satisfies the restricted isometric property (RIP) with high probability. Extensive experimental results show our scheme can recover the data accurately with packet loss ratio up to $15\%$, while traditional CDG can hardly recover the data under similar or even better conditions.

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

Compressive sensing (CS)-based in-network data processing is a promising approach to reduce packet transmission in wireless sensor networks. Existing CS-based data gathering methods require a large number of sensors involved in each CS measurement gathering, leading to the relatively high data transmission cost. In this paper, we propose a sparsest random scheduling for compressive data gathering scheme, which decreases each measurement transmission cost from O(N) to O(log(N)) without increasing the number of CS measurements as well. In our scheme, we present a sparsest measurement matrix, where each row has only one nonzero entry. To satisfy the restricted isometric property, we propose a design method for representation basis, which is properly generated according to the sparsest measurement matrix and sensory data. With extensive experiments over real sensory data of CitySee, we demonstrate that our scheme can recover the real sensory data accurately. Surprisingly, our scheme outperforms the dense measurement matrix with a discrete cosine transformation basis over 5 dB on data recovery quality. Simulation results also show that our scheme reduces almost 10 × energy consumption compared with the dense measurement matrix for CS-based data gathering.

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

In this demo, we present a gesture guided moving robot with Microsoft Kinect device. The demo shows two basic operational capabilities: first, the human gestures can be recognized and used to drive the robot moving; second, the moving robot is programmable, and can move according to the instructions sent over serial port; third and most important, the application can leverage the depth data provided by Kinect to keep the relatively stable distance with the operating people. This demo system shows the basic operations when human are interacting with the mobile devices. Further, it can be used for aiding the disabled person or shipping equipments for soldiers.

With widespread usages of smart phones, participatory sensing becomes mainstream, especially for applications requiring pervasive deployments with massive sensors. However, sensors on smart phones are prone to the unknown measurement errors, requiring automatical calibration among uncooperative participants. Current methods need either collaboration or explicit calibration process. However, due to the uncooperative and uncontrollable nature of the participants, these methods fail to calibrate sensor nodes effectively. We investigate sensor calibration in monitoring pollution sources, without explicit calibration process in uncooperative environment. We leverage the opportunity in sensing diversity, where a participant will sense multiple pollution sources when roaming in the area. Further, inspired by EM (Expectation Maximization) method, we propose a two-level iterative algorithm to estimate the source presences, source parameters and sensor noise iteratively. Our algorithm can converge to the optimal estimation of sensor noise, where the likelihood of observations is maximized.

Recent studies show that WiFi interference has been a major problem for low power urban sensing technology ZigBee networks. Existing approaches for dealing with such interferences often modify either the ZigBee nodes or WiFi nodes. However, massive deployment of ZigBee nodes and uncooperative WiFi users call for innovative cross-technology coexistence without intervening legacy systems. In this work we investigate the WiFi and ZigBee coexistence when ZigBee is the interested signal.Mitigating short duration WiFi interference (called flash) in long duration ZigBee data (called smog) is challenging, especially when we cannot modify the WiFi APs and the massively deployed sensor nodes. To address these challenges, we propose ZIMO, a sink-based MIMO design for harmony coexistence of ZigBee and WiFi networks with the goal of protecting the ZigBee data packets.The key insight of ZIMO is to properly exploit opportunities resulted from differences between WiFi and ZigBee, and bridge the gap between interested data and cross technology signals. Also, extracting the channel coefficient of WiFi and ZigBee will enhance other coexistence technologies such as TIMO [1]. We implement a prototype for ZIMO in GNURadio-USRP N200, and our extensive evaluations under real wireless conditions show that ZIMO can improve up to 1.9x throughput for ZigBee network, with median gain of 1.5x, and 1.1x to 1.9x for WiFi network as byproduct in ZigBee signal recovery.

In the participatory sensing network, the sensor noise dominates the quality of sensing data as well as the processing efficiency. Previous works focus on evaluating sensing accuracy with expectations, and fails to quantify the sensor noise with variance estimations, which will inevitably suffer from the dynamics and the incompleteness of the sensing data. In this paper, we propose FSP (Feeling Sensors' Pulse) method, which quantifies the sensor noise using the confidence interval. Specifically, we first use EM (Expectation Maximization) based iterative estimation algorithm to compute the maximum likelihood estimation (MLE) of sensor noise. Second, on the basis of these estimations, we leverage the asymptotic normality of MLE and the Fisher information to compute the confidence interval. The extensive simulations show that, FSP can achieve 90% success rate where the true values of sensor noise fall into the 95% confidence interval, at the cost of the polynomial time complexity only.

Neighbor discovery is the very first step for many mobile applications. Previous neighbor discovery protocols that use single channel may be failed when faced with unreliable channel environment, whereas most of multi-channel schemes do not focus on neighbor discovery in low duty cycle wireless networks. The key challenge for multi-channel slotted neighbor discovery is how to effectively assign the channel during each active slot. We propose McDisc, an energy-efficient and reliable duty-cycle-based neighbor discovery protocol that is built on two basic idea. First, it leverages randomized approach to establish the multi-channel discovery schedule, where the node is assigned to the chosen channel during each active slot randomly. Randomized approach may suffer from extremely low discovery probability in worst case. In tackling this problem, we then employ deterministic channel assignment that can ensure bounded discovery latency. The theoretical analysis and simulation results confirm that McDisc can cope with unreliable channel environment ensuring reliable and low latency discovery efficiently. In terms of 60% packet loss ratio, McDisc outperforms U-Connect [1] by approximate 100% in the worst-case discovery latency.

Leveraging a wireless multipath channel as the source of common randomness, many key generation methods have been proposed according to the information-theory security. However, existing schemes suffer a low generation rate and a low entropy, and mainly rely on nodes' mobility. To overcome this limitation, we present a key generation protocol with known artificial interference, named SmokeGrenade, a new physical-layer approach for secret key generation in a narrowband fading channel. Our scheme utilizes artificial interference to contribute to the change of measured values on channel states. Our theoretical analysis shows that the key generation rate increases with the increment of the interference power. Particularly, the achievable key rate of SmokeGrenade gains three times better than that of the traditional key generation schemes when the average interference power is normalized to 1. Simulation results also demonstrate that SmokeGrenade achieves a higher generation rate and entropy compared with some state-of-the-art approaches.

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

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

We propose joint channel sensing, probing, and accessing schemes for secondary users in cognitive radio networks. Our method has time and space complexity O(N·k) for a network with N channels and k secondary users, while applying classic methods requires exponential time complexity. We prove that, even when channel states are selected by adversary (thus non-stochastic), it results in a total regret uniformly upper bounded by Θ(√TN logN), w.h.p, for communication lasts for T timeslots. Our protocol can be implemented in a distributed manner due to the nonstochastic channel assumption. Our experiments show that our schemes achieve almost optimal throughput compared with an optimal static strategy, and perform significantly better than previous methods in many settings.

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

Multichannel accessing is a basic and important function in wireless sensor networks, where intelligent and low-cost methods are needed for energy-efficient spectrum utilization. Unfortunately, for energy constrained nodes, methods from cognitive radio technologies are not applicable in WSN multichannel systems due to computation and communication overhead, as well as extremely large energy consumptions. In this work, we propose a spectrum learning and utilizing paradigm, where sensor nodes can sense, probe, access and switch among channels automatically without prior knowledge. Under this basic and important learning method, we present a two-stage learning and utilization algorithm, which constitutes of two interleaving parts: `transmitting while learning' and `learning while transmitting'. Building these two parts is a non-trivial work, which need efficient channel learning and utilizing scheme working together seamlessly. In the first `transmitting while learning' stage, we propose the `non-call-back' policy and leverage the Lagrangian method for the additional energy cost considerations. Under this heuristic policy, an improved version for the traditional Gittins Index model is provided. After that, the heuristic algorithm is proved `2-approximate' to the optimal result. Furthermore, leveraging the channel correlations, the learning complexity is reduced from O(T) stages to O(log T) stages, where T is the number of time slots for channel learning. Secondly, we make a structural analysis on the `learning while transmitting' stage, leveraging the 2-dimensional optimal stopping theory. Notably, we use the a 16-node TelosB sensor network and an 8-node USRP network for performance evaluation. The USRP nodes are playing as interfering and monitoring nodes for controllable network states design, making network performance evaluation convincible. The proposed algorithm is designed in real implementations with experimental results, showing the efficiency of the proposed scheme.

In multichannel system, user could keep transmitting over an instantaneous “on peak” channel by opportunistically accessing and switching among channels. Previous studies rely on constant transmission duration, which would fail to leverage more opportunities in time and frequency domain. In this paper, we consider opportunistic channel accessing/releasing scheme in multichannel system with Rayleigh fading channels. Our main goal is to derive a throughput-optimal strategy for determining when and which channel to access and when to release it. We formulate this real-time decision-making process as a two-dimensional optimal stopping problem. We prove that the two-dimensional optimal stopping rule can be reduced to a simple threshold-based policy. Leveraging the absorbing Markov chain theory, we obtain the optimal threshold as well as the maximum achievable throughput with computational efficiency. Numerical and simulation results show that our proposed channel utilization scheme achieves up to 140 percent throughput gain over opportunistic transmission with a single channel and up to 60 percent throughput gain over opportunistic channel access with constant transmission duration.

A critical challenge in Cognitive Radio Networks (CRN) is to make decision in real-time on accessing and releasing available channels that maximize the spectrum utilization and the overall system throughput. In this work, we make investigations on optimal action point to explore and exploit the frequency-temporal diversity in addition to spectrum availability. By modeling the Rayleigh fading channel under primary user (PU) activity to be a finite state Markov channel (FSMC) with an absorbing state, we formulate this difficulty into a 2-Dimension optimal stopping problem. Further, we've proved that, the complexity of the 2D optimal stopping rule can be reduced to one threshold policy, where the optimal character still holds. After properly constructing multi-absorbing-states Markov chain for dynamic analysis, we get the throughput of our strategy accurately. Numerical and simulations results have verified that, our threshold based access/switch strategy gains much more throughput than conventional idle/busy based access/switch strategy at the cost of access delay in most cases.

We study the general scaling laws of the capacity for random wireless networks under the generalized physical model. The generality of this work is embodied in three dimensions denoted by (λ ∈ [1, n], nd∈ [1, n], ns∈ (1, n]). It means that: (1) We study the random network of a general node density λ ∈ [1, n], rather than only study either random dense network (RDN, λ = n) or random extended network (REN, λ = 1) as in the literature. (2) We focus on the multicast capacity to unify unicast and broadcast capacities by setting the number of destinations for each session as a general value nd∈ [1, n]. (3)We allow the number of sessions changing in the range ns∈ (1, n], rather than assume that ns= Θ(n) as in the literature.We derive the general lower bounds on the capacity for the arbitrary case of (λ, nd, ns). Particularly, we show that for the special cases (λ = 1, nd∈ [1, n], ns= n) and (λ = n, nd∈ [1, n], ns= n), our schemes achieve the highest multicast throughputs proposed in the existing works.

In multichannel system, user could keep transmitting over an instantaneous "on peak" channel by opportunistically accessing and switching among channels, so as to exploit link layer time-frequency diversity. In this paper, we consider an opportunistic channel accessing/releasing scheme for maximizing system throughput in multichannel system under Rayleigh fading environment. The time-dependence of Rayleigh fading is accurately characterized by finite-state Markov channel (FSMC) model. The main goal of this paper is to devise throughput-optimal strategy for determining when to access (which) channel and when to release it. The chanllenge of the problem comes from the fact that user can never know the instantaneous quality of all channels. In fact, it has to make real time decisions purely depending on the quality of current channel and the statistics of candidate channels. We formulate this real time decision making process as a two dimension optimal stopping problem. We prove that the complexity of the two dimensional optimal stopping rule can be reduced to a simple threshold-based policy. The dynamic data transmission process under threshold-based opportunistic channel access/release strategy is then analyzed by properly constructing absorbing Markov chain. Leveraging the absorbing Markov chain theory, we attain the optimal threshold as well as maximum achievable throughput with computational efficiency. Numerical results show that our proposed channel utilization scheme achieves up to 120% throughput gain over opportunistic transmission with a single channel and up to 70% throughput gain over opportunistic channel access with constant transmission duration.

Neighbor discovery is an essential step for the self-organization of wireless sensor networks. Many algorithms have been proposed for efficient neighbor discovery. However, most of those algorithms need nodes to keep active during the process of neighbor discovery, which might be difficult for low-duty-cycle wireless sensor networks in many real deployments. In this paper, we investigate the problem of neighbor discovery in low-duty-cycle wireless sensor networks. We give an ALOHA-like algorithm and analyze the expected time to discover all n - 1 neighbors for each node. By reducing the analysis to the classical K Coupon Collector's Problem, we show that the upper bound is ne(log2n + (3 log2n - 1) log2log2n + c) with high probability, for some constant c, where e is the base of natural logarithm. Furthermore, not knowing number of neighbors leads to no more than a factor of two slowdown in the algorithm performance. Then, we validate our theoretical results by extensive simulations, and explore the performance of different algorithms in duty-cycle and non-duty-cycle networks. Finally, we apply our approach to analyze the scenario of unreliable links in low-duty-cycle wireless sensor networks.

There has been much research focusing on the routing problem in delay tolerant networks (DTNs). Much of the work has mainly focused on coding schemes for message distribution, while other work has been done on the probabilistic forwarding. Coding schemes achieve higher delivery rate via redundancy before forwarding, while probabilistic forwarding efficiently limits the abuse of the store and forward scheme, maintaining relatively high performance. Providing a reliable and efficient forwarding scheme proves to be challenging as coding and forwarding schemes should be jointly considered. In our paper, we present an optimal probabilistic forwarding scheme using fountain code, which we name as CFP (Coded Forwarding Protocol), where reliability and efficiency can be achieved at the same time. In CFP, We use fountain codes to encode messages and provide the forwarding rule to decide whether to forward messages to another node. The probabilistic forwarding problem is modeled as an optimal stopping problem, and our forwarding rule also considers the influence of fountain codes. We perform trace-driven simulations and compare CFP with other protocols. Simulation results show that, considering the delivery rate, delay, and number of forwardings, CFP performs better than other implemented protocols - Epidemic, which is the most original protocol, and OPF, which represents the optimal probabilistic forwarding protocol - in our simulation.

Network lifetime is crucial to 3D Under Water Sensor Networks (UWSNs) because it decreases more seriously than in 2D scenarios as the radius of the monitored region grows. We utilize sink mobility to solve the problem intuitively because the sink deployed in a vehicle is controllable while sensors are hard to be retrieved and recharged. It is hard to extend the results in 2D scenarios, i.e., a circular motion, to 3D UWSNs directly because there are more factors influencing network lifetime. However there is little literature on utilizing and analyzing sink mobility in 3D UWSNs. To simplify 3D sink mobility, we convert any motion to a combination of circular motions through mapping. After discussing the characteristics of circular motions, we propose MOSS, an optimal MObile Sink Strategy, to maximize the network lifetime in 3D UWSNs. Simulation results show that MOSS outperforms other motion strategies and improves the network lifetime at least in the order of 800% when R ≥ 5r, where R is the network radius and r is the transmission range of sensors.

The main challenge in DTNs is how to deal with path uncertainty in achieving a reliable routing scheme. All Erasure coding based routing algorithms make the assumption that the underlying path probabilities are known previously and remain constant, which is unpractical. On the other hand, the overall behavior of path probability tends to be stable with the increasing number of paths, which can be used to increase the stability of Erasure coding based schemes. Bearing this in mind, we present Inter-Coding: Inter-Coding is designed to fully combine the reliability of erasure coding, and the stability of interleaving to cope with uncertainties. We evaluate our approach in terms of delivery ratio under different level of uncertainty as well as different interleaving policy, and validate that Inter-Coding offers reliable and stable performance even the path uncertainty and dynamic is high.

In extremely low-duty-cycle wireless sensor networks, a sender has to wait for a certain period of time to forward a packet until its receiver becomes active, which will result in longer end-to-end delay than ever. Many works have been done to improve delivery ratio but lack of the consideration on energy efficient delivery delay. In addition, unreliable links is another challenge in wireless sensor networks. Redundancy and multiple paths can be used to cope with unreliability, but neither of them is energy efficient. Even worse, both of them have poor performance on delivery delay. In this work, we introduce a novel way of allocating erasure coded blocks over multiple paths to improve energy efficient delivery delay while achieving comparably high delivery ratio. We evaluate our algorithm with extensive simulations. Evaluations show that our design decreases delivery delay greatly with slight decrease in delivery ratio.

In wireless sensor networks, there are many link quality measurement metrics such as RSSI (Received Signal Strength Indicator), LQI (Link Quality Indicator) and PRR (Packet Reception Rate), which can be used under different channel quality and application scenarios. As in time synchronization, channel quality would be essential to the clock synchronization. The broadcast nature of time synchronization algorithm makes channel quality measurement complex. Moreover, the measurement on channel quality would be costly. We make an experimental study on time synchronization in wireless sensor network. Firstly, we use the RSSI and PRR with different packet lengths, secondly, we find that, the PRR would be accurate but costly, and the RSSI is not accurate enough for time synchronization algorithm evaluation. In the end, we propose a channel clustering and categorization mechanism in dealing with the channel measurement difficulties. And we find that, in dealing with the lossy links, the compensation model for synchronization errors is more important than reliable transmissions. Also, the proposed measurement would be helpful to the time synchronization algorithm, especially the FTSP (Flooding Time Synchronize Protocol).

WANETs is a recent network architecture where the nodes are spread all over the world but behave exactly as if they are part of a single-hop or multi-hop wireless networks at the PHY and MAC layers. Without distinguishing data packets and noise, the Software Defined Access Point (SoDA) samples the wireless channel for the uplink and multicasts the sampled data via Internet to other SoDAs. This leads to tremendous traffic load on the Internet. In this paper, we use energy detection to address this issue. Specifically, we propose EDDD to aim at reducing the traffic load on the Internet under the condition that dropping data packet as few as possible. Through extensive experiments on IEEE 802.11 and IEEE 802.15.4, we validate the feasibility and effectiveness of EDDD.

In wireless networks, routing algorithms need to be tolerable to network dynamics. Existing route selection mechanisms suffer from a lack of considerations of stability and its induced routing overhead. Stabilities on route selection, traffic engineering and transmission schedule are fundamental issues in achieving a mobility-tolerable wireless network. In this study, the authors propose a mobility-tolerable paradigm (named ‘MOTOROLA’) in building a stable route level coordination algorithm for dynamic routing and scheduling. In MOTOROLA, mobility-awareness modules explore the mobility parameters and the link duration time, purely by adaptive beacon messages. Transitory links are mitigated based on threshold value of link duration. The route level resource allocation algorithm is also tolerable to network topology changes and the rescheduling costs are minimised in time scale. Analytical and simulation results show that because of mobility-awareness ability and route stability, MOTOROLA could improve network efficiency by transitory links' mitigation and coordinative route restoration.

Bloom filter is effective, space-efficient data structure for concisely representing a data set and supporting approximate membership queries. Traditionally, researchers often believe that it is possible that a Bloom filter returns a false positive, but it will never return a false negative under well-behaved operations. By investigating the mainstream variants, however, we observe that a Bloom filter does return false negatives in many scenarios. In this work, we show that the undetectable incorrect deletion of false positive items and detectable incorrect deletion of multiaddress items are two general causes of false negative in a Bloom filter. We then measure the potential and exposed false negatives theoretically and practically. Inspired by the fact that the potential false negatives are usually not fully exposed, we propose a novel Bloom filter scheme, which increases the ratio of bits set to a value larger than one without decreasing the ratio of bits set to zero. Mathematical analysis and comprehensive experiments show that this design can reduce the number of exposed false negatives as well as decrease the likelihood of false positives. To the best of our knowledge, this is the first work dealing with both the false positive and false negative problems of Bloom filter systematically when supporting standard usages of item insertion, query, and deletion operations.

Spectrum access scheme is a fundamental component in building efficient wireless networks. Conventional methods such as proactive channel assignment is costly due to large amount of protocol overhead. Also, those algorithms suffer from its inability in dealing with channel dynamics. The opportunistic methods however, spend more time on probing, and suffer from the myopic decisions as well. We present a decision based dynamic spectrum access algorithm (DDSA), which is built upon the Markov decision process (MDP), and could adaptively handle the DSA process for higher throughput. We employ quiet probing and dynamic controlling mechanisms in DDSA, so as to achieve a reduced protocol overhead and improved adaptivity. Different from previous methods, the DDSA is a model driven method, and we use the modeling technique on the IEEE 802.11 DCF for virtual channel state probing. The modeling technique could help us improve the accuracy on channel state, and reduce protocol overhead. Using a heuristic and adaptive algorithm named `hindsight optimization', we solve the hardness in computing the MDP. Moreover, under the feasibility testing and scaling processes, the validated decision can be confidentially applied for a congestion-free DSA.

In distributed wireless mesh networks, highly reliable channels are typically preferred and thus suffer from heavy contentions; on the other hand, unreliable channels are often discarded, leading to bandwidth waste. Indeed, each node, in contributing to overall network performance, should balance between utilization on reliable channels and unreliable channels. Stability plays an important role, either on each node or the whole network. We propose a distributed multi-phase maximum weighted matching algorithm, making use of both reliable and un-reliable channels. We prove that the algorithm will achieve maximized overall network throughput with relatively high stability in a distributed manner. We also apply channel bundles to effectively improve stability in the network, and the problem proves to be NP-hard. The approximate ratio and complexity of the algorithm are also analyzed in a structural manner. The time complexity is O(Delta 3 + (log * n) 2 ), and overhead complexity is O(Delta times (Delta + n) + n alpha + log n), with Delta denoting maximum number of node degree in a n nodes network. Simulation results show that p-stable design effectively improves network stability, especially upon the existence of large number of unreliable channels.

Most previous localization and tracking systems in wireless sensor networks are based on RF signals, ultrasounds, and UWB. However, systems using RF signals suffer accuracy fluctuations and systems using ultrasound or UWB need extra hardware. In our paper, we propose to utilize light, an easily accessible and pervasive resource in our daily life, and off-the-shelf TelosB Motes to achieve the goal of stability and high accuracy in localization and tracking. The iTracking system is a mobile light source location-tracking system based on light intensity. Our main contribution is the first demonstration to track mobile light sources based on light intensity with high accuracy, which may provide an alternative method for localization and tracking or inspire game designers. We examine point light based tracking and flashlight based tracking in our demonstrations, which proves these two main sources of light in our daily life can achieve centimeter-level location-tracking requirements. Our future work will focus on enabling the iTracking system to writing Chinese characters in flashlight.

The performance of wireless sensor networks (WSNs) is tightly coupled with the geometric environment in which sensors are deployed. In a practical environment, bottleneck regions, for example bridges, may exist due to the existence of physical obstacles or energy depletion. In this paper, we propose a load-balancing based optimal routing protocol (LORP). By finding the boundaries of holes in a sensor network with bottlenecks, LORP first identifies the bridges in the sensor field using our MACB algorithm. A centralized routing algorithm, "balance-first" routing is then employed to prolong the lifetime of a WSN with bottlenecks. Theoretical analysis prove that LORP can improve the load distribution among different bridges, increase the lifetime of a WSN, and enhance the quality of network services. This conclusion is reinforced in our simulation results.

In highly dynamic wireless mesh networks, channel quality variations will affect network performance seriously. Channel assignment and scheduling algorithms have been applied in wireless mesh networks (WMN), so as to maximize network resource utilization. However, existing seminar works are mainly focusing on given set of channel quality values, which have not considered the channel quality variations in time scale. With highly dynamic channel quality, scheduling algorithm might possibly executed time to time, which will eventually deteriorate network performance. In this paper, we propose a stochastic programming model in order to reduce highly dynamic scheduling overhead and improve network utilization in heterogeneous wireless mesh networks. Since it has been proved that, achieving the optimal result is NP-hard. A heuristic solution with two-stage maximum rematching algorithm is proposed, and simulation results show that, our two-stage dynamic scheduling algorithm is efficient as channels in network are highly dynamic on communication quality.