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

Smart grids rely on massive data from networked smart meters to enable fine-grained energy analytics and improve sustainability. As a representative, Non-Intrusive Load Monitoring (NILM) can infer individual appliance usage from aggregated meter readings, offering energy-saving insights without the need for per-appliance sensors. However, practical deployment at scale is hindered by three challenges: (1) limited per-device resources such as on-board computation and mobile data plans; (2) scarce labeled data due to the high cost of manual annotation; and (3) significant variability across households, including temporal changes in user behavior and appliance status.To address these challenges, we propose UNITY, a semi-supervised meta-learning NILM framework that unifies labeled and unlabeled data across diverse households to learn generalizable representations. UNITY minimizes user-side computation and communication overhead by reformulating inference as a lightweight sequence-retrieval task, accelerated by Discrete Haar Wavelet transforms. Only uncertain samples are uploaded to the server for refinement. To overcome label scarcity, UNITY employs entropy-based transductive learning, gradually enhancing model confidence on unlabeled data. Furthermore, to handle household diversity and temporal distribution shift, UNITY adopts a meta-learning approach that treats each household as a distinct task and incrementally adapts to behavioral drift over time, enabling robust long-term deployment. Experiments on public NILM datasets demonstrate that UNITY achieves 95.21% accuracy, outperforming existing methods under real- world resource constraints.

Gesture recognition plays a vital role in human-computer interaction. Most current gesture recognition systems infer gesture categories directly from raw inputs, often missing the semantic information embedded in the gesture execution process. This paper introduces a novel vision-based gesture semantic understanding system, referred as SEGUS. The goal of this system is to retrieve the symbol and path information contained in gestures. It utilizes a side-mounted camera on a wristband or smartwatch to capture video inputs and performs foreground-background separation of the video. The system extract is symbol and path information form the foreground and the background of T and video, respectively. Then, it achieves gesture recognition by comparing sequences of semantic elements. We evaluate the system through extensive experiments. The results indicate an average improvements from 6.68% to 16.39% in recognition accuracy compared to other end-to-end systems. Additionally, scalability tests show that by reorganizing existing semantic elements, the proposed system can accommodate newly added gestures without the need to re-collect sample data. This significantly reduces system overhead, achieving a 64.10% reduction in training time compared to methods requiring a training set for all gestures, with only a modest average accuracy decrease of 6.39%.

Given the increasing number of on-demand delivery, people progressively realize that accurate indoor localization of couriers becomes vital for improving the quality of services. However, existing wireless indoor localization systems suffer from deployment difficulty caused by model migration or extra infrastructure needed, and traditional neural networks fail to get good performance with poor quality data and labels. In this work, we overcome the fundamental challenges in pitiful data to achieve a low-cost indoor localization system using large-scale poor-quality crowdsourcing WiFi data - WiLoc. WiLoc constructs WiFi data into a hypergraph and acquires the topological relationships among APs based on a light-weight graph convolutional network. Then, it utilizes a modified transformer structure to learn the latent global-aware features according to the data quality and task demand. Finally, we implement the prototype of WiLoc in the real on-demand delivery scenario with merchant-level accuracy and evaluate its performance based on real datasets from couriers. Extensive experiments demonstrate that WiLoc achieves an average F1 score of 85.03 % across various shopping malls, outperforming the baseline methods.

mmWave CS (chirp sequence) radar is one of the most widely used automotive sensors for ADAS (Advanced Driving Assistance System) and autonomous driving. It offers various advantages, including high resolution, cost-effectiveness, and robustness in all-weather and ambient light environment. However, as mmWave radar density increases, the potential for inter-radar interference rises, which leads to degraded target detection accuracy. Deep learning technologies have shown significant promise in mitigating inter-radar interference. However, this enhancement often comes with the high computational complexity. In this paper, we propose a ConvMixer based interference suppression method, which is primarily composed of patch embedding and depth-wise separatable convolution. The effectiveness of the proposed ConvMixer-based approach is verified and analyzed using simulated data, demonstrating superior performance in terms of SNR, correlation coefficient, phase difference, detection rate, and processing time compared to the state-of-the-art approaches.

Bluetooth-low-energy (BLE)-based low-power cameras have expanded the applications of battery-powered low-power Internet of Things (IoT) vision systems. The adaptive tuning of connection and image encoding settings plays a vital role in optimizing the efficiency of wireless vision systems. This article introduces a framework for adaptive adjustment in BLE-based low-power vision systems. To reduce power consumption, we model BLE power usage and design a dynamic BLE parameters and resolution adjustment method for low-power IoT vision system. Additionally, we design an edge-end combined approach that delegates the task of key region prediction to the receiving device to achieve adaptive intraframe JPEG encoding. Our approach leverages commercially available BLE devices without requiring physical layer modifications, ensuring compatibility with standard devices. We design a wireless hardware prototype using off-the-shelf CMOS sensors and BLE Systems on a Chip to evaluate our framework. Extensive experiments demonstrate a 66.9% increase in energy efficiency compared to traditional fixed parameter methods.

Symbiotic Internet of Things (IoT) systems built upon existing 5G infrastructure are increasingly adopted due to their high capacity, low latency, and wide coverage. The small cell architecture inherent in 5G networks enables efficient spectrum utilization but also introduces challenges, such as complex intercell interference, particularly in deployments with low-cost and compact IoT base stations. While analog beamforming is effective for interference management, it incurs high hardware costs and energy consumption due to the requirement for RF power amplifiers and phase shifters (PSs). To address this issue, on–off analog beamforming (OABF) has emerged as a cost-efficient alternative, replacing PSs with simple RF on–off switches. OABF offers key advantages, including affordability, compactness, rapid speed, and most importantly, low power consumption. In this article, we propose an energy-efficient hybrid OABF coordination strategy for multicell multiple-input and single-output (MISO) downlink symbiotic IoT systems by leveraging deep reinforcement learning. The goal is to maximize the overall system energy efficiency (EE) by jointly optimizing antenna element activation and transmit power allocation at each IoT base station. Through extensive simulations, we compare the proposed approach against conventional antenna selection and PS-based analog beamforming schemes under various power constraint models. The simulation results unequivocally demonstrate the superiority of our method, exhibiting higher average EE across diverse network configurations.

Hand gesture applications (HGA) are essential for human-machine interaction. Although the existing solutions achieve good performance in specific tasks, they still face challenges when users navigate through different application contexts, i.e., requiring multi-task ability to support newly arrived HGA tasks. In this paper, we propose a novel wrist-worn multi-task HGA system namedXHGA, which can implement modal-domain combination, data-domain adaptation and label-domain extension to ensure the performance in multi-task scenarios. The system introduces a novel two-stage training strategy, i.e., task-agnostic stage to align cross-modal features from unlabeled arbitrary gestures through contrastive learning, and task-related stage to learn modality contributions with limited labeled data in specific tasks through self-attention mechanism, while achieves multi-objective recognition simultaneously by employing an adaptive loss function weighting method. Extensive experiments demonstrate thatXHGAcan achieve an average accuracy of 92.7% with only using 15 labeled data per gesture under three HGA tasks. Compared with the state-of-the-art multi-modal approach,XHGAreduces 82.7% training time, and 47.7% storage, with about 5% improvements in accuracy. Code is available athttps://github.com/htang0/XHGA.

In magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems, receiver (RX) feedback communication is promising to enhance the capability and efficiency of the system. Although some studies have explored in-band implementations with low overhead costs, it has not been comprehensively investigated. In this paper, we propose Relip, a Reliable layer-level in-band parallel feedback communication mechanism for MIMO MRC-WPT systems, which addresses the impact of RX-RX couplings (i.e., non-negligible interference from strong couplings and positive effects of relay phenomenon), and provides a theoretical analysis of communication reliability. Technically, we first devise an On-Off based two-phase modulation mechanism to achieve RX identification and dependency detection under relay phenomenon. Then, we utilize observed channel decomposability to collect group-level power transfer channel conditions for eliminating the interference caused by strong RX-RX couplings. Furthermore, we perform RX selection to optimize the trade-off between communication reliability and time overhead. We design and implement the Relip prototype and conduct extensive experiments. The results validate the effectiveness of our mechanism, i.e., Relip can provide ≥99% average decoding accuracy for concurrent feedback communication of 14 devices, achieving an 18.31% improvement compared to the state-of-the-art solution.

With the continuous advancement of computer vision, image processing technologies, volumetric video, represented by point cloud videos, holds the potential for extensive applications in areas such as Virtual Reality (VR) and Augmented Reality (AR). Viewport prediction, also referred to as Field of View (FoV) prediction, is a crucial component in emerging VR and AR applications, playing a vital role in the transmission of point cloud videos. Currently, models for viewpoint prediction that integrate feature extraction and FoV information heavily rely on the spatial-temporal features extracted by convolutional neural networks. However, the drawback of 3D convolution lies in its inability to effectively capture long-term spatial-temporal dependencies within videos. Moreover, the temporal contrast layer used for time feature extraction only compares features within each block, leading to matching errors and inaccurate temporal feature extraction, consequently diminishing predictive performance. To address these limitations, we propose a Transformer-based Volumetric Point Cloud Video Viewport Prediction Network (VPFormer) that can efficiently extract spatial-temporal features from point cloud videos. VPFormer constitutes a viewport prediction framework that combines the spatial-temporal features of point cloud videos with user trajectory information. Specifically, we introduce a novel sampling method that effectively preserves spatial-temporal information while reducing computational complexity. Additionally, we incorporate context-aware dynamic positional encoding to capture inter-frame spatial-temporal context information. Subsequently, we introduce a voxel-based temporal contrast layer and partition the point cloud into smaller voxel blocks during feature matching, significantly reducing matching errors and enhancing the analysis and extraction of temporal features. Finally, by combining the spatial-temporal features of point cloud videos with user head trajectory information, we successfully predict future user viewpoints. Experimental results demonstrate that this approach outperforms other solutions in terms of performance.

Magnetic resonant coupling (MRC) enabled wireless power transfer (WPT) systems have shown significant promise in efficiently charging multiple devices simultaneously through beamforming technology. The existing works propose various mechanisms for achieving better charging performance, but they still lack exploration of transmitter (TX) coil orientation adjustment and rarely consider the dynamic deployment of TXs. In this work, we propose the D istributed A daptive E xploration and E xploitation ( DAEE ) algorithm for orientation adjustment in MRC-WPT systems, which includes both hardware and software innovations. The hardware component features a servo motor-based mechanical device that adjusts the TX coil orientation. In the software aspect, we decompose the charging performance optimization problem and devise a distributed orientation control algorithm combining exploration and exploitation mechanisms. We develop a system prototype for the DAEE algorithm and conduct extensive experiments to validate its performance. Specifically, the TX orientation adjustment significantly enhances performance, achieving an average 103% improvement in power-delivered-to-load (PDL) compared to state-of-the-art frequency adjustment-based solutions that do not adjust orientation. Additionally, the combination of exploration and exploitation strategies in the DAEE algorithm proves effective, delivering a 24% performance improvement over the random beamforming (RB)-based exploration method.

Hand gesture recognition (HGR) is essential for human-machine interaction. Although the existing solutions achieve good performance in specific tasks, they still face challenges when users navigate through different application contexts, i.e., demanding multi-task ability to support newly arrived HGR tasks. In this paper, we propose the first IMU-vision based system hosted on wrist-worn devices to support multi-task HGR, denoted as MultiHGR. The system introduces a novel two-stage training strategy, i.e., task-agnostic stage to align cross-modal features from unlabeled arbitrary gesture through contrastive learning, and task-related stage to learn modality contributions with limited labeled data in specific tasks through self-attention mechanism. Since only the second task-related stage should be executed for each new task, MultiHGR could accommodate multiple tasks with significant reduced training cost and storage requirement. The evaluation results on three HGR tasks demonstrates that MultiHGR reduces 64.92% training time, and 24.04% storage as compared with traditional multimodal single-task models, and MultiHGR outperforms unimodal single-task models with 14.37%, 19.28%, and 31% improvements in these three tasks, respectively. As compared with state-of-the-art multimodal single-task model, MultiHGR achieves average 6.35% accuracy improvement, along with 65.74% training time reduction.

Electromagnetic radiation (EMR) safety has always been a critical reason for hindering the development of magneticenabled wireless power transfer technology. People focus on the actual received energy at charging devices while paying attention to their health. Thus, we study this significant problem in this paper, and propose a universal safety guaranteed power-delivered-to-load (PDL) maximization scheme (called SafeGuard). Technically, we first utilize the off-the-shelf electromagnetic simulator to perform the EMR distribution analysis to ensure the universality of the method. Then, we innovatively introduce the concept of multiple importance sampling for achieving efficient EMR safety constraint extraction. Finally, we treat the proposed optimization problem as an optimal boundary point search problem from the perspective of space geometry, and devise a brand-new grid-based multi-constraint parallel processing algorithm to efficiently solve it. We implement a system prototype for SafeGuard, and conduct extensive experiments to evaluate it. The results indicate that our SafeGuard can obviously improve the achieved PDL by up to 1.75× compared with the state-of-the-art baseline while guaranteeing EMR safety. Furthermore, SafeGuard can accelerate the solution process by 29.12× compared with the traditional numerical method to satisfy the fast optimization requirement of wireless charging systems.

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

The significance of intelligent sensing systems is growing in the realm of smart services. These systems extract relevant signal features and generate informative representations for particular tasks. However, building the feature extraction component for such systems requires extensive domain-specific expertise or data. The exceptionally rapid development of foundation models is likely to usher in newfound abilities in such intelligent sensing. We propose a new scheme for sensing model, which we refer to as semi-generalist sensing model (SGSM). SGSM is able to semiautomatically solve various tasks using relatively less task-specific labeled data compared to traditional systems. Built through the analysis of the common theoretical model, SGSM can depict different modalities, such as the acoustic and Wi-Fi signal. Experimental results on such two heterogeneous sensors illustrate that SGSM functions across a wide range of scenarios, thereby establishing its broad applicability. In some cases, SGSM even achieves better performance than sensor-specific specialized solutions. Wi-Fi evaluations indicate a 20% accuracy improvement when applying SGSM to an existing sensing model.

Real-time Bidding (RTB) advertisers wish to know in advance the expected cost and yield of ad campaigns to avoid trial-and-error expenses.However, Campaign Performance Forecasting (CPF), a sequence modeling task involving tens of thousands of ad auctions, poses challenges of evolving user interest, auction representation, and long context, making coarse-grained and static-modeling methods sub-optimal.We propose AdVance, a time-aware framework that integrates local auction-level and global campaign-level modeling.User preference and fatigue are disentangled using a timepositioned sequence of clicked items and a concise vector of all displayed items.Cross-attention, conditioned on the fatigue vector, captures the dynamics of user interest toward each candidate ad.Bidders compete with each other, presenting a complete graph similar to the self-attention mechanism.Hence, we employ a Transformer Encoder to compress each auction into embedding by solving auxiliary tasks.These sequential embeddings are then summarized by a conditional state space model (SSM) to comprehend long-range dependencies while maintaining global linear complexity.Considering the irregular time intervals between auctions, we

While localization has gained a tremendous amount of attention from both academia and industry, much less attention has been paid to equally important orientation estimation. Traditional orientation estimation systems relying on gyroscopes suffer from cumulative errors. In this paper, we propose UWBOrient, the first fine-grained orientation estimation system utilizing ultra-wideband (UWB) modules embedded in smartphones. The proposed system presents an alternative solution that is more accurate than gyroscope estimates and free of error accumulation. We propose to fuse UWB estimates with gyroscope estimates to address the challenge associated with UWB estimation alone and further improve the estimation accuracy. UWBOrient decreases the estimation error from the state-of-the-art 7.6° to 2.7° while maintaining a low latency (20 ms) and low energy consumption (40 mWh). Comprehensive experiments with both iPhone and Android smartphones demonstrate the effectiveness of the proposed system under various conditions including natural motion, dynamic multipath and NLoS. Two real-world applications, i.e., head orientation tracking and 3D reconstruction are employed to showcase the practicality of UWBOrient.

Wireless power transfer (WPT) systems using magnetic resonant coupling (MRC) have made significant progress recently, leading to various optimization methods in scenarios involving multiple-input multiple-output (MIMO) to improve charging performance. Adjusting the coil orientation of the power transmitter (TX) is a simple but effective method due to the directional nature of magnetic field distribution, but existing approaches often require unnecessary coil rotations. In this study, we introduce a Location-Aware Orientation Adjustment algorithm, known as LAORA, to address these inefficiencies. LAORA focuses on solving the problems of charging devices (RX) localization and location-based optimization. We begin by introducing the concept of Equivalent Impedance Distribution Image (EIDI) and transform the RX localization problem into a combined matching process involving EIDI. In addition, we establish a dynamic simulation framework to predict charging performance using RX-related knowledge, enabling us to obtain optimal TX orientations through reinforcement learning without needing to rotate on mechanical devices physically. We then implement the prototype and conduct extensive experiments. The results show that, compared to other existing orientation adjustment methods, LAORA achieves an average improvement of 186 % while reducing the mechanical rotations by 83.3 %.

WiFi-based technology is appealing for indoor localization due to the widely deployed infrastructures. Recently, path separation solutions have been proposed to address the multipath effects and achieve decimeter-level localization accuracy in line-of-sight (LoS) scenarios. However, these solutions experience serious performance degradation in non-line-of-sight (NLoS) scenarios, and couldn't be used for mobile device tracking where continuous LoS/NLoS switching happens. In this paper, we propose SaTrack, a LoS/NLoS state-aware mobile device tracking system. SaTrack identifies LoS/NLoS states based on the diversity of the strongest estimated paths when using different reference antennas. With the observation of spatial aggregation and temporal continuity for the Tx-Rx direct path, SaTrack chooses the direct path through two-step clustering, i.e., clustering in the spatial domain to form candidates and clustering again in the temporal domain to select the winner. Extensive experiments are conducted to evaluate the effectiveness of SaTrack. In a typical indoor environment with abundant multipath, SaTrack achieves 0.64m and 1.27m for the median and 90th percentile tracking errors, outperforming the state-of-the-art (SOTA) solutions.

The diverse advertiser demands (brand effects or immediate outcomes) lead to distinct selling (pre-agreed volumes with an under-delivery penalty or compete per auction) and pricing (fixed prices or varying bids) patterns in Guaranteed delivery (GD) and real-time bidding (RTB) advertising. This necessitates fair impression allocation to unify the two markets for promoting ad content diversity and overall revenue. Existing approaches often deprive RTB ads of equal exposure opportunities by prioritizing GD ads, and coarse-grained methods are inferior to 1) Ambiguous reward due to varied objectives and constraints of GD fulfillment and RTB utility, hindering measurement of each allocation's contribution to the global interests; 2) Intensified competition by the coexistence of GD and RTB ads, complicating their mutual relationships; 3) Policy degradation caused by evolving user traffic and bid landscape, requiring adaptivity to distribution shifts.

This article presents EARFace , a system that shows the feasibility of tracking facial landmarks for 3D facial reconstruction using in-ear acoustic sensors embedded within smart earphones. This enables a number of applications in the areas of facial expression tracking, user interfaces, AR/VR applications, affective computing, and accessibility, among others. Although conventional vision-based solutions break down under poor lighting and occlusions, and also suffer from privacy concerns, earphone platforms are robust to ambient conditions while being privacy-preserving. In contrast to prior work on earable platforms that perform outer-ear sensing for facial motion tracking, EARFace shows the feasibility of completely in-ear sensing with a natural earphone form factor, thus enhancing the comfort levels of wearing. The core intuition exploited by EARFace is that the shape of the ear canal changes due to the movement of facial muscles during facial motion. EARFace tracks the changes in shape of the ear canal by measuring ultrasonic channel frequency response of the inner ear, ultimately resulting in tracking of the facial motion. A transformer-based machine learning model is designed to exploit spectral and temporal relationships in the ultrasonic channel frequency response data to predict the facial landmarks of the user with an accuracy of 1.83 mm. Using these predicted landmarks, a 3D graphical model of the face that replicates the precise facial motion of the user is then reconstructed. Domain adaptation is further performed by adapting the weights of layers using a group-wise and differential learning rate. This decreases the training overhead in EARFace . The transformer-based machine learning model runs on smart phone devices with a processing latency of 13 ms and an overall low power consumption profile. Finally, usability studies indicate higher levels of comforts of wearing EARFace ’s earphone platform in comparison with alternative form factors.

Mobile radio frequency identification (RFID) systems typically experience the continual movement of many tags rapidly going in and out of the interrogating range of readers. Readers that are deployed to maintain a current, real-time list of tags, which are present in the interrogating zone at any moment, must repeatedly execute a series of reading cycles. Each of these reading cycles provides the readers very limited time to identify unknown tags (those newly entering into the reader’s range), and, at the same time, to detect missing tags (those just leaving the reader’s range). In this paper, we study the continuous tag recognition problem, which is critical for mobile RFID systems. First, we obtain a lower bound on communication time for solving this problem. We then design a near-OPTimal protocoL, called OPT-L, and prove that its communication time is approximately equal to the lower bound. Finally, we present extensive simulation and experimental results that demonstrate OPT-L’s superior performance over other existing protocols.

In Mobile Edge Computing, edge servers have limited storage and computing resources that can only support a small number of functions. Meanwhile, mobile applications are becoming more complex, consisting of multiple dependent tasks, modeled as a Directed Acyclic Graph (DAG). When a request arrives, typically in an online manner with a deadline specified, we need to configure the servers and assign the dependent tasks for efficient processing. This work jointly considers the problem of dependent task placement and scheduling with on-demand function configuration on edge servers, aiming to meet as many deadlines as possible. For a single request, when the configuration on each edge server is fixed, we derive FixDoc to find the optimal task placement and scheduling. When the on-demand function configuration is allowed, we propose GenDoc , a novel approximation algorithm, and analyze its additive error from the optimal theoretically. For multiple requests, we derive OnDoc , an online algorithm easy to deploy in practice. Our extensive experiments show that GenDoc outperforms state-of-the-art baselines in processing 86.14% of these unique applications, and reduces their average completion time by at least 24%. The number of deadlines that OnDoc can satisfy is at least 1.9× that of the baselines.

User impression forecasting underpins various commercial activities, from long-term strategic decisions to short-term automated operations. As a representative that involves both kinds, the highly profitable Guaranteed Delivery (GD) advertising focuses mainly on promoting brand effect by allowing advertisers to order target impressions weeksin advance and get allocatedonline at the scheduled time. Such a business mode naturally incurs three issues making existing solutions inferior: 1) Timescale-granularity dilemma of coherently supporting the sales of day-level impressions of the distant future and the corresponding fine-grained allocation in real-time. 2) High dimensionality due to the Cartesian product of user attribute combinations. 3) Stability-plasticity dilemma of instant adaptation to emerging patterns of temporal dependency withoutcatastrophic forgetting of repeated ones facing the non-stationary traffic.

In recent years, receiver (RX) feedback communication has attracted increasing attention to enhance the charging performance for magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems. People prefer to adopt the in-band implementation with minimal overhead costs. However, the influence of RX-RX coupling couldn’t be directly ignored like that in the RFID field, i.e., strong couplings and relay phenomenon. In order to solve these two critical issues, we propose a Robust layer-level in-band parallel communication protocol for MIMO MRC-WPT systems (called Roland). Technically, we first utilize the observed channel decomposability to construct group-level channel relationship graph for eliminating the interference caused by strong RX-RX couplings. Then, we generalize such method to deal with the RX dependency due to relay phenomenon. Finally, we conduct extensive experiments on a prototype testbed to evaluate the effectiveness of the proposed scheme. The results demonstrate that our Roland could provide ≥95% average decoding accuracy for concurrent feedback communication of 14 devices. Compared with the state-of-the-art solution, the proposed protocol Roland can achieve an average decoding accuracy improvement of 20.41%.

User authentication is essential for threat defense and data protection. Existed authentication systems have some known limitations, such as spoofing attacks, privacy leakage, and user-unfriendliness. In this paper, we propose BackLip, a novel anti-spoofing authentication system based on lip reading. We employ Wi-Fi backscatter-based technology to recognize users lip reading due to its various advantages, e.g. privacy protection, low power consumption, and low cost. Our system is touch-free and passphrase-independent, making it user-friendly, especially for the elderly and disabled. We first filter out irrelevant interference and enhance the signal-to-noise ratio of lip-reading signals by modulating the backscatter tags and constructing a series of suitable filters. Then, we study the energy distribution and steady-state characteristics of the backscattered signal caused by lip-reading movement at different frequencies to extract passphrase-independen lip-reading fingerprints. We build a theoretical model to analyze and prove the feasibility of our method and design an adaptive correction method to resist the interference caused by distance and angle changes. Additionally, we propose an adaptive segmentation algorithm to label lip-reading motions automatically. Extensive experiments demonstrate that BackLip has an average accuracy of 92.1% and is improved to 96.5% when users use the same passphrases.

Search Engines such as Google, Baidu, and Bing have revolutionized the way we interact with the cyber world with a number of applications in recommendations, learning, advertisements, healthcare, entertainment, etc. In this paper, we design search engines for sign languages such as American Sign Language (ASL). Sign languages use hand and body motion for communication with rich grammar, complexity, and vocabulary that is comparable to spoken languages. This is the primary language for the Deaf community with a global population of ≈ 500 million. However, search engines that support sign language queries in native form do not exist currently. While translating a sign language to a spoken language and using existing search engines might be one possibility, this can miss critical information because existing translation systems are either limited in vocabulary or constrained to a specific domain. In contrast, this paper presents a holistic approach where ASL queries in native form as well as ASL videos and textual information available online are converted into a common representation space. Such a joint representation space provides a common framework for precisely representing different sources of information and accurately matching a query with relevant information that is available online. Our system uses low-intrusive wearable sensors for capturing the sign query. To minimize the training overhead, we obtain synthetic training data from a large corpus of online ASL videos across diverse topics. Evaluated over a set of Deaf users with native ASL fluency, the accuracy is comparable with state-of-the-art recommendation systems for Amazon, Netflix, Yelp, etc., suggesting the usability of the system in the real world. For example, the re-call@10 of our system is 64.3%, i.e., among the top ten search results, six of them are relevant to the search query. Moreover, the system is robust to variations in signing patterns, dialects, sensor positions, etc.

Magnetic resonant coupling (MRC) enabled wireless power transfer (WPT) systems can charge multiple devices concurrently and efficiently via beamforming technology. The existing work proposes various mechanisms for achieving better charging performance, but still lacks the exploration about transmitter (TX) coil direction adjustment, and rarely considers the dynamic deployment of TXs. Thus, in this paper, we propose a Distributed Probability-adaptive Direction Adjustment algorithm for MRC-WPT systems (called DPDA). On the one hand, we design and implement a servo motor-based mechanical device to realize the ability of TX coil direction adjustment. On the other hand, we decompose the charging performance optimization problem, and then devise a distributed direction control algorithm based on random beamforming. We implement the system prototype for DPDA, and conduct extensive experiments on it. The experimental results demonstrate the effectiveness of the proposed algorithm, e.g., in case of large TX-RX horizontal misalignment, DPDA can achieve an average 1.98X improvement of power-delivered-to-load (PDL) as compared with the state-of the-art frequency adjustment-based solution.

Existing intelligent piano learning systems mainly assist the player by camera, which generally only consider the fingering that can only reflect the performance problem from a limited perspective. Thus, we propose PianoWatch, a multi-dimensional assistance system based on wrist wearable devices. PianoWatch extracts more accurate patterns by analyzing data from microphone, camera, accelerometers and gyroscopes. Then it gives corresponding playing advices, in addition to pitch, including fingering, depth and even mood, through an evaluation model. We implement the prototype of PianoWatch and evaluate it by 20 volunteers. Extensive experiments show it can achieve 93% F1-Score on the playing pattern extraction, and more than 95% of users would like to try it to assist their piano learning.

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.

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.

Recently, multiple-input multiple-output (MIMO) technology has been introduced into magnetic resonant coupling (MRC) enabled wireless power transfer (WPT) systems for concurrent charging of multiple devices. However, impedance mismatching phenomena caused by strong TX-RX, TX-TX, or RX-RX coupling greatly affect the power delivered to load (PDL) in practical charging systems. To solve this issue, we propose an effective scheduling algorithm for Impedance Matching–enhanced PDL optimization in MIMO MRC-WPT systems (called IMeP ), which integrates the transmitter scheduling together with the impedance matching techniques, i.e., adjusting TX coils for tuning TX-RX/TX-TX coupling and grouping RXs to separate strongly coupled RX pairs. We formulate this as a joint optimization problem and decouple it into three sub-problems, i.e., current scheduling, coil adjustment, and RX grouping. We then solve them through alternating direction method of multipliers–based, randomized beamforming–based, and graph clique cover–based algorithms, respectively. Extensive experiments are performed on a prototype testbed, and the results demonstrate the effectiveness of our solution. Compared with the state-of-the-art power transfer efficiency maximization solution, the proposed algorithm IMeP achieves a 74.7× performance improvement of PDL on average.

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 MRC-WPT channels’ state estimation and collision-aware parallel in-band 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 ms, and efficient parallel communication among transceivers within proximity of ∼0.5 m.

Hand motion capture is essential for emerged augmented reality (AR) and virtual reality (VR) applications. We propose MHMC, a real-time hand motion capture system using millimeter-wave (mmWave) radar with the advantages of lighting independence, privacy protection and good user experience. When implementing such a system, we design a novel vertical neural network architecture to infer complex hand motion from limited resolution data of mmWave radar, a vision-based labeling method to acquire adequate labeled data, and effective loss functions to avoid abnormal output. The experimental results demonstrate that MHMC achieves similar accuracy as visionbased methods, along with fast processing speed, i.e., 7. 3ms per frame, for supporting real-time motion capture.

Feature extraction is important for non-intrusive appliance load monitoring (NILM), because it includes crucial steps which altogether transform raw signals into distinct features of appliances. Although numerous studies have achieved good results using transient harmonic features, their performances degrade in practical scenarios. In this paper, a time-frequency analysis-based framework for transient harmonic feature extraction is proposed, which takes all factors that would eventually influence NILM system into account. More specffically, the framework proposes a new qualitative transient harmonic feature, along with current retrieval and data augmentation methods. The proposed framework is evaluated on two well-known datasets (i.e., Controlled On/Off Loads Library and Home Equipment Laboratory Dataset). As compared with the other state-of-art methods, the proposed framework achieves similar accuracy (i.e., more than 96%) on single-load data, and more than 33% accuracy improvement over multi-load data (e.g., from 59.4% to 93% when six devices are active at the same time).

Magnetic resonant coupling (MRC) wireless power transfer (WPT) is a convenient and potential power supply solution for smart devices. The scheduling problem in the multiple-input multiple-output (MIMO) scenarios is essential to concentrate energy at the receiver (RX) side. Meanwhile, strong TX-RX coupling could ensure better power transfer efficiency (PTE), but may cause lower power delivered to load (PDL) when transmitter voltages are bounded. In this paper, we propose the frequency adjustment based PDL maximization scheme for MIMO MRC-WPT systems. We formulate such joint optimization problem and decouple it into two sub-problems, i.e., high-level frequency adjustment and low-level voltage adaptation. We solve these two sub-problems with gradient descent based and alternating direction method of multipliers (ADMM) based algorithms, respectively. We further design an energy-voltage transform matrix algebra based estimation mechanism to reduce context measurement overhead. We prototype the proposed system, and conduct extensive experiments to evaluate its performance. As compared with the PTE maximization solutions, our system trades smaller efficiency for larger energy, i.e., 361% PDL improvement with respect to 26% PTE losses when TX-RX distance is 10cm.

The great popularity of smartwatches leads to a growing demand for smarter interactive systems. Hand gesture is suitable for interaction due to its unique features. However, the existing single-modal gesture interactive systems have different biases in diverse scenarios, which makes it intractable to be applied in real life. In this paper, we propose a multi-modal smartwatch interactive system named Mudra, which fuses vision and Inertial Measurement Unit (IMU) signals to recognize and identify hand gestures for convenient and robust interaction. We carefully design a parallel attention multi-task model for different modals, and fuse classification results at the decision level with an adaptive weight adjustment algorithm. We implement a prototype of Mudra and collect data from 25 volunteers to evaluate its effectiveness. Extensive experiments demonstrate that Mudra can achieve 95.4% and 92.3% F1-scores on recognition and identification tasks, respectively. Meanwhile, Mudra can maintain stability and robustness under different experimental settings.

Recently, the developed techniques such as magnetic resonant coupling (MRC) and multiple-input multiple-output (MIMO) transmission have significantly improved the charging efficiency and distance for wireless power transfer (WPT) systems. However, the electromagnetic radiation (EMR) safety of wireless charging is critical in practice while mostly ignored. In this work, we take the EMR safety into account in MIMO MRC-WPT systems. We propose a safety ensured high-efficient scheduling algorithm for magnetic MIMO wireless power transfer system (called Shield). Technically, we firstly devise a simple but accurate Z-axis rotational symmetrical EMR model along with a magnetic-field-line-based meshing scheme. Further, we express the EMR safety requirement in the continuous physical space with a limited number of constraints via random sampling and rule-based filtering. Finally, we build up a system prototype for Shield and conduct extensive experiments. With the given power budget and resonant frequency, the results reveal that the EMR safety requirement only influences the charging performance of an MRC-WPT system within a certain range. Furthermore, Shield can dramatically improve the payload power transfer efficiency (PTE) by up to 66.60% compared with state-of-the-art baselines while guaranteeing the EMR safety.

The hash table finds numerous applications in many different domains, but its potential for non-coalesced memory accesses and execution divergence characteristics impose optimization challenges on GPUs. We propose a novel hash table design, referred to as Cuckoo Node Hashing, which aims to better exploit the massive data parallelism offered by GPUs. At the core of its design, we leverage Cuckoo Hashing, one of known hash table design schemes, in a closed-address manner, which, to our knowledge, is the first attempt on GPUs. We also propose an architecture-aware warp-cooperative reordering algorithm that improves the memory performance and thread divergence of Cuckoo Node Hashing and efficiently increases the likelihood of coalesced memory accesses in hash table operations. Our experiments show that Cuckoo Node Hashing outperforms and scales better than existing state-of-the-art GPU hash table designs such as DACHash and Slab Hash with a peak performance of 5.03 billion queries/second in static searching and 4.34 billion insertions/second in static building.

WiFi-based gait recognition is an attractive method for device-free user identification, but path-sensitive Channel State Information (CSI) hinders its application in multi-path environments, which exacerbates sampling and deployment costs (i.e., large number of samples and multiple specially placed devices). On the other hand, although video-based ideal CSI generation is promising for dramatically reducing samples, the missing environment-related information in the ideal CSI makes it unsuitable for general indoor scenarios with multiple walking paths.In this paper, we propose WiVi, a WiFi-video cross-modal fusion based multi-path gait recognition system which needs fewer samples and fewer devices simultaneously. When the subject walks naturally in the room, we determine whether he/she is walking on the predefined judgment paths with a K-Nearest Neighbors (KNN) classifier working on the WiFi-based human localization results. For each judgment path, we generate the ideal CSI through video-based simulation to decrease the number of needed samples, and adopt two separated neural networks (NNs) to fulfill environment-aware comparison among the ideal and measured CSIs. The first network is supervised by measured CSI samples, and learns to obtain the semi-ideal CSI features which contain the room-specific ‘accent’, i.e., the long-term environment influence normally caused by room layout. The second network is trained for similarity evaluation between the semi-ideal and measured features, with the existence of short-term environment influence such as channel variation or noises.We implement the prototype system and conduct extensive experiments to evaluate the performance. Experimental results show that WiVi’s recognition accuracy ranges from 85.4% for a 6-person group to 98.0% for a 3-person group. As compared with single-path gait recognition systems, we achieve average 113.8% performance improvement. As compared with the other multi-path gait recognition systems, we achieve similar or even better performance with needed samples being reduced by 57.1-93.7%

Human pulmonary function declines with age. Elders, especially those with lung or cardiovascular diseases, yearn for daily lung function tests for timely diagnosis and treatment. However, current clinical spirometers are cumbersome and ex-pensive while home-use portable ones’ accuracy is questionable. Moreover, both kinds require contact measurements and could cause cross infection, especially hazardous for contagious diseases like COVID-19. To this end, we propose SpiroFi, a contactless system that leverages WiFi Channel State Information (CSI) for convenient yet accurate Pulmonary Function Testing (PFT) out of clinic. The key enabler underlying SpiroFi is a set of algorithms that can extract chest wall movement from WiFi signal variations and interpret such information into lung function indices. We have realized SpiroFi on low-cost commodity WiFi devices and tested it in a home-like site where it achieves 2.55% monitoring error over healthy youths. Then, with the Ethics Committee (EC) approval, we conducted a 2-month clinic study in a city hospital over elders with basic diseases. SprioFi still yields 6.05% monitoring error despite elders’ degenerated pulmonary function and body control. Also, the correlation between lung function and age as well as chronic diseases has been revealed, highlighting the importance of daily PFT for the elderly.

Guaranteed delivery (GD) and real-time bidding (RTB) constitute two parallel profit streams for the publisher. The diverse advertiser demands (brand or instant effect) result in different selling (in bulk or via auction) and pricing (fixed unit price or various bids) patterns, which naturally raises the fusion allocation issue of breaking the two markets' barrier and selling out at the global highest price boosting the total revenue. The fusion process complicates the competition between GD and RTB, and GD contracts with overlapping targeting. The non-stationary user traffic and bid landscape further worsen the situation, making the assignment unsupervised and hard to evaluate. Thus, a static policy or coarse-grained modeling from existing work is inferior to facing the above challenges.

Nowadays, Internet of Things (IoT) based smart home system is equipped with a large number of smart devices, such as smart speakers and cameras, which can greatly facilitate users to control and automate their home environment. However, recent studies have shown that smart home system is at great risk of privacy leakage. Especially, external attackers can infer user privacy information by passively sniffing encrypted smart home network traffic. Traditional methods mainly focus on sniffing WiFi devices but pay less attention to other commodity devices such as Zigbee and Bluetooth (BLE). In this paper, we focus on inferring fine-grained sensitive details about users using diverse commodity devices. We apply deep learning techniques to infer users' behaviors through identifying and localizing smart home devices being used due to the excellent performance of deep learning in many fields. Specifically, we first pre-process encrypted device traffic, select valid features, and use Convolutional Neural Networks (CNN) for device identification. In addition, we extract the Received Signal Strength Indicator (RSSI) from the frame information of traffic packets and employ Sparse Autoencoder (SAE) to extract stable and distinguishable high-dimensional features for RSSI measurement. Features are fed into a Multilayer Perceptron (MLP) to predict the device's localization. In this way, we can infer human activity by identifying and localizing the devices being used. Extensive experiment results show that our work can achieve a mean position estimation error of 1.34m even in an unseen environment, outperforming other common- used localization algorithms based on RSSI fingerprints.

In recent years, multiple-input multiple-output (MIMO) technology has been imported into magnetic resonance coupled (MRC) enabled wireless power transfer (WPT) systems for concurrent charging of multiple devices. Besides the traditional performance optimization methods (e.g., TX current scheduling, system frequency adjustment, etc.), receiver (RX) grouping will also severely influence the achieved power- delivered-to-load (PDL). In this paper, we investigate the optimal RX grouping issue to maximize the proportional fairness of RX achieved PDL, which is a joint optimization problem involving RX grouping and time-slice allocation among groups. By decoupling the problem, we solve the group generation sub-problem with a impedance-matching based greedy algorithm to generate potential RX group candidates, and we further solve the time slice allocation sub-problem with a genetic algorithm to distribute resources among group candidates. We prototype the proposed system, denoted as IMRG, and conduct extensive experiments to evaluate the performance. The experimental results validate the effectiveness of the proposed algorithm, e.g., IMRG achieves average 59.6% PDL improvement through RX grouping compared to the simultaneous charging scheme.

Magnetic wireless power transfer (WPT) has received widespread attention from both academia and industry, and magnetic resonance coupling (MRC) based WPT systems have a longer charging distance to support the scenarios with multiple transmitters (TXs) and multiple receivers (RXs). In such systems, an in-band reliable TX-RX communication protocol is essential to guarantee to charge performance. In this paper, we devise Onion, a dependency-aware in-band communication protocol for MIMO MRC-WPT systems. Technically, we extend the well-known EPCglobal C1G2 protocol in Radio Frequency Identification (RFID) fielded and make it suitable for mutual inductance based communication links in MRC-WPT systems. Furthermore, we craft an innovative onion-style layer-dependency based communication mechanism to utilize the positive impact of the relay phenomenon. We design and implement the Onion prototype and conduct extensive experiments to evaluate it. The experiment results demonstrate the effectiveness of the proposed protocol, which increases the communication success ratio by an average of 40% as compared to the dependency-unaware scheme.

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.

Fine-grained energy consumption analysis has great potential value in applications of Smart Grids, renewable energy, and Artificial Intelligence of Things. Non-Intrusive Load Monitoring (NILM) is a single-sensor alternative to the conventional one-sensor-for-one-appliance solution due to its ability to deduce individual appliances states from mixed measurements from the main power interface. Despite its advantages of low cost and easy maintenance, a few drawbacks hinders its widespread adoption. To enhance the Quality of Service (QoS) of NILM, four objectives should be achieved by careful designing: high accuracy, user transparency, low response delay, and low data redundancy.Inspired by observations of discriminative yet redundant current waveform and model sparsity, we propose LCL, a lightweight, contactless, plug-and-play solution for real-time load monitoring. The filtering module skips over unchanged input and compresses the measurements of interest using Compressed Sensing. The reconstruction-free inference module runs an attentional multi-label classification and returns all functioning appliance states directly from the compressed input. The compression module leverages model sparsity for real-time processing on edge devices. Evaluations based on our prototype deployed in real-life scenarios attest to the high QoS of LCL with a subset accuracy of 94.2% and a delay reduction of 52.2%. Our solution further filters out 96.8% of the redundant input and attains a Measurement Rate of 0.1 without noticeable impact on the performance.

Recently, multiple-input multiple-output (MIMO) technology has been introduced into magnetic resonant coupling (MRC) enabled wireless power transfer (WPT) systems for concurrent charging of multiple devices. However, impedance mismatching phenomena caused by strong TX-RX or RX-RX coupling greatly affect the power delivered to load (PDL) in practical charging systems. To solve this issue, we propose an effective scheduling algorithm for Impedance Matching enhanced PDL optimization in MIMO MRC-WPT systems (called IMP), which integrates the transmitter scheduling together with the impedance matching techniques, i.e., adjusting TX coils for tuning TX-RX coupling and grouping RXs to separate strongly coupled RX pairs. We formulate this as a joint optimization problem and decouple it into three sub-problems, i.e., current scheduling, coil adjustment, and RX grouping, and solve them through alternating direction method of multipliers (ADMM) based, tabu search (TS) based, and graph clique cover based algorithms, respectively. Extensive experiments are performed on a prototype testbed, and the results demonstrate the effectiveness of our solution. Compared with the state-of-the-art power transfer efficiency (PTE) maximization solution, the proposed algorithm IMP achieves a 74.7X performance improvement of PDL on average.

WiFi-based gait recognition is an appealing device-free user identification method, but the environment-sensitive WiFi signal hinders it from easy deployment for a new environment. On the other hand, the Inertial Measurement Unit (IMU) based method could obtain environment-independent gait features, however, it suffers from uncomfortable experiences due to device wearing. In this paper, we propose IMFi, a novel cross-modal gait recognition system to achieve device-free and easy deployment at the same time. We carefully choose the torso and foot speed curves as common features for cross-modal matching. In the enrollment phase, we extract and store the environment-independent IMU-based gait features with two IMU devices attached to the waist and ankle, respectively. In the recognition phase, we retrieve environment-related CSI-based gait features for user identification, along with the environment adaptive Principal Component Analysis (PCA) selection method for better noise reduction. We perform cross-modal matching between IMU and CSI-based features through a simple Convolution Neural Network (CNN) with a limited number of trained environments. The effectiveness of the proposed system is verified via extensive experiments. The results demonstrate that IMFi could be easily deployed to the new environment without the need for retraining. Specifically, our proposed system achieves 85% binary classification accuracy and 96% top-3 multi-class classification accuracy in the new environment.

Backscatter communication integrated with RF energy harvesting provides a promising solution to prolong the lifetime of wireless sensor networks (WSNs). However, the existing centralized or flooding-based routing protocols can not be applied directly to large-scale backscatter-enabled WSN due to fussy implementation or excessive messages. In this paper, we investigate the routing protocol for such networks to maximize the throughput by arranging the uploading path of each sensor. We first propose a centralized solution by converting the original problem into a maximum flow problem. Then, after inspecting the characteristics of the backscatter-enabled sensors, we propose a flow balancing-based push-relabel algorithm. We conduct extensive experiments to evaluate the proposed algorithms. The results demonstrate the effectiveness of our distributed protocol, which outperforms the other baseline solutions and keeps close approximation to the centralized solution.

The rapid proliferation of Smart Grids calls for a more in-depth understanding of user energy consumption behaviors, based on large data volumes collected by various sources of sensors such as voltmeter and ammeter. Non-Intrusive Load Monitoring (NILM) is a single sensor solution, which can effectively disaggregate individual appliance states from measurements only at the interface to the power source, albeit at the cost of requiring circuit modifications thus introducing suspension of services and potential safety hazards. To overcome the undesirable attribute of NILM and achieve a safe yet highly accurate solution, we devise a contactless sensing system based on inductive current measurements that can conduct load disaggregation without tampering with the power system. Despite using single modality, i.e., the inductive current, our scheme attains state-of-the-art accuracy in existing multi-modality datasets by leveraging modality distillation technique to handle arbitrary input structure. Our main contributions enlist: (1) devising and deploying the first, to the best of our knowledge, purely contactless non-intrusive load disaggregation system; (2) the design of an oracle-apprentice network structure to leverage multi-modality input for training, while operating with single modality; (3) a high estimation accuracy of 95.44% and 96.21%, respectively, is attested on two public datasets, which proves the efficiency of our method.

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%.

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.

By integrating both energy harvesting and backscatter communication technologies, so-called `battery-free tag' emerges as a promising solution to the energy related issues in IoT. However, such tags present new challenges due to the unstable energy supply and excitation signals, which are critical to realize successful backscatter communications. In this paper, we present a novel system, denoted as ecUWB, which enables robust communication for battery-free tags where unstable WiFi signals act as the unified source for both energy supply and excitation signals. At tag side, we propose a charging-transmission division scheme to achieve better signal utilization, and introduce a re-transmission mechanism for possible excitation signal interruption. At receiver side, we implement a simply method which is based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) to predict the uncontrollable signals, and propose an energy-centric tag scheduling method according to the tag energy estimation results. Extensive experiments are carried out on customized tags and NI USRP platform. The results show that ecUWB outperforms the existing ones in terms of performance and efficiency under the unstable WiFi signals.

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.

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.

In this paper, we investigate online distributed job dispatching in an edge computing system residing in a Metropolitan Area Network (MAN). Specifically, job dispatchers are implemented on access points (APs) which collect jobs from mobile users and distribute each job to a server at the edge or the cloud. A signaling mechanism with periodic broadcast is introduced to facilitate cooperation among APs. The transmission latency is non-negligible in MAN, which leads to outdated information sharing among APs. Moreover, the fully-observed system state is discouraged as reception of all broadcast is time consuming. Therefore, we formulate the distributed optimization of job dispatching strategies among the APs as a Markov decision process with partial and outdated system state, i.e., partially observable Markov Decision Process (POMDP). The conventional solution for POMDP is impractical due to huge time complexity. We propose a novel low-complexity solution framework for distributed job dispatching, based on which the optimization of job dispatching policy can be decoupled via an alternative policy iteration algorithm, so that the distributed policy iteration of each AP can be made according to partial and outdated observation. A theoretical performance lower bound is proved for our approximate MDP solution. Furthermore, we conduct extensive simulations based on the Google Cluster trace. The evaluation results show that our policy can achieve as high as 20.67% reduction in average job response time compared with heuristic baselines, and our algorithm consistently performs well under various parameter settings.

Millimeter wave (mmWave) signal-based localization is a promising scheme due to high precision in the lineof-sight (LoS) scenarios. However, even small obstacles would hinder mmWave localization by blocking the LoS paths, and environment reflection-based schemes under non-LoS (NLoS) scenarios couldn't guarantee the precision. In this paper, we investigate mmWave NLoS localization scheme based on recent fast-growing research for specialized mmWave reflectors. Our system uses multiple reflectors in the environment to act as anchors for target localization. Firstly, we propose a two-phase reflector discovery mechanism to let transmitter identify the reflectors and estimate their positions as well. Secondly, we introduce a hashing-based beam alignment method with logarithmic complexity to obtain the relative relationship among reflectors and the target. Lastly, we put forward a triangulation-based method for target localization, along with credit-based fusing mechanism to handle the possible imprecise estimated reflector positions. Extensive experiments are carried out to evaluate the proposed scheme. The results demonstrate the effectiveness of the proposed system, which achieves 50% average improvement over environment reflection-based methods. Furthermore, the proposed hashing-based beam alignment process dramatically reduces the time-consumption as compared with baseline using exhaustive search, e.g., 18x, 37x and 40x reductions are observed with 1, 4 and 7 reflectors, respectively.

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.

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.

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.

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.

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%.

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.

Interference management is one of the most important issues in heterogeneous cellular networks with multiple macro and pico cells. The enhanced inter cell interference coordination (eICIC) has been proposed to protect downlink pico cell transmissions by mitigating interference from neighboring macro cells. Therefore, the adaptive eICIC configuration problem is critical, which adjusts the parameters including the ratio of almost blank subframes (ABS) and the bias of cell range expansion (RE). This problem is challenging especially for the scenario with multiple coexisting network services, since different services have different user scheduling strategies and different evaluation metrics. By using a general service model, we formulate the eICIC configuration problem with multiple coexisting services as a general form consensus problem with regularization and solve the problem by proposing an efficient optimization algorithm based on the alternating direction method of multipliers. In particular, we perform local RE bias adaptation at service layer, local ABS ratio adaptation at BS layer, and coordination among local solutions for a global solution at a network layer. To provide the service scalability, we encapsulate the service details into the local RE bias adaptation subproblem, which is isolated from the other parts of the algorithm, and we also introduce some implementation examples of the subproblem for different services. The extensive simulation results demonstrate the efficiency of the proposed algorithm and verify the convergence property.

Heterogeneous networks (HetNets) are intended to offer wide area coverage and high data rate transmission by deploying small cells besides macrocells. Device-to-device (D2D) communication as an underlay of cellular network enriches local service and offloads base station. In this paper, we target the video transmission demanded by picocell edge users (PEUEs), who suffer from low quality channel due to the inter-cell interference from macrocell and long physical distance from picocell. Moreover, the wireless channels are burst-loss prone for upper layer applications such as video on demand (VoD), which makes the traditional channel coding such as forward error correction (FEC) insufficient. In this paper, we address these issues and propose a cooperative video transmission scheme to improve PEUEs' received video quality by constructing two transmission paths from picocell to each PEUE. The two transmission paths are the direct transmission from pico-eNB (i.e., base station) to PEUE and a relay-assisted path by means of D2D communication for frame recovery, respectively. Reference frame selection and unequal error protection are adopted to further improve the overall performance. Extensive simulations are conducted and results demonstrate that the proposed scheme outperforms state-of-the-art scheme in Config.4b scenarios defined by 3GPP.

Natural disasters can result in severe damage to communication infrastructure, which leads to further chaos to the damaged area. After the disaster strikes, most of the victims would gather at the evacuation sites for food supplies and other necessities. Having a good communication network is very important to help the victims. In this paper, we aim at recovering the network from the still-alive mobile base stations to the out-of-service evacuation sites by using multi-hop relaying technique. We propose to reconstruct the post-disaster network in a capacity-aware way based on prize collecting Steiner tree. The purpose of the proposed scheme is to achieve high capacity connectivity ratio in a cost efficient way. To provide more accurate evaluation results, we evaluate the proposed scheme by using the real evacuation site and base station data in Tokyo area, and utilizing the big data analysis based post-disaster service availability model.

Dynamic spectrum reallocation, under which the spectrum owners temporarily share the underutilized spectrum to secondary users for economic profit, is an important approach to improve the spectrum utilization ratio. Auction is believed to be a natural marketing tool to incentivize the spectrum owners, and thus redistribute the idle spectrum efficiently. Extensive researches have been done in the problem of truthful spectrum auction, in which the bidders bid based on their true valuations of the spectrum. The true valuation of the individual bidder, however, is a private information which should be protected against exposure. In this paper, we propose a privacy preserving truthful spectrum auction scheme by utilizing homomorphic encryption. The proposed scheme reveals the group bids but hides the users' bids even from the auctioneer. The evaluation results show that the proposed scheme achieves good spectrum utilization efficiency with low communication and computation overheads.

Network virtualization (NV) is a promising solution for higher resource utilization, improved system performance, and lower investment capitals for network operators. Spectrum sharing is an important issue for NV in the wireless networks. Meanwhile, the scheme of Almost Blank Subframe (ABS) causes new challenge for NV in the heterogeneous cellular networks (HetNet). This paper aims at investigating the joint optimization problem of spectrum sharing and ABS adaptation, and the optimization target is represented through general utility functions of logical virtual operators (LVOs). We formulate the problem, and decouple it into two subproblems. We propose a dynamic programming based algorithm for the spectrum sharing subproblem, and an alternating direction method of multipliers (ADMM) based algorithm for the ABS adaptation subproblem. The simulation results demonstrate the efficiency of the proposed algorithm.

Interference management is one of the most important issues in the heterogeneous cellular networks (HetNet) with macro and pico cells. The enhanced inter cell interference coordination (eICIC) has been proposed to protect downlink pico cell transmissions by mitigating interference from neighboring macro cells. The adaptive eICIC configuration problem is studied in this paper to adjust the parameters including the ratio of Almost Blank Subframes (ABS) and the bias of cell range expansion (RE). We formulate the problem as a general form consensus problem with regularization, and solve the problem by providing an efficient distributed optimization framework. Our algorithm is based on the alternating direction method of multipliers (ADMM) in which the solutions to local subproblems on each macro cell and pico cell are coordinated to find a solution to the global problem for the whole network. We also propose the dynamic programming based algorithms to solve the local subproblems on macro cell or pico cell. The simulation results demonstrate the efficiency of the proposed algorithm compared with existing approaches, and verify the convergence properties of the proposed algorithm.

This paper investigates the secure information transfer issue for cognitive radio networks that have multiple non-altruistic primary users, secondary users and eavesdroppers. The design objective is to improve the secrecy rates of the primary users, and create the transmission opportunities for the secondary users. To achieve this goal, we propose to incentivize the non-altruistic users to cooperate by a barter-like exchange. Specifically, the primary users leverage the assist of the secondary users in the form of cooperative transmitting or friendly jamming, and in return, yield certain licensed spectrum accessing time to the aided secondary users. We propose a truthful Double Auction mechanism for Secure Information transfer in cognitive radio networks, namely DASI, to jointly formulate the cooperator/jammer assignment and the corresponding resource allocation problems. We prove that DASI preserves nice economic properties that are critical for the auction design, including truthfulness, individual rationality and budget balance. We also evaluate DASI in terms of aggregated throughput and spectrum utilization ratio by simulations.

Scalable video coding (SVC) is attractive technology for multicasting video to users with different available transmission capacities. In this paper, we investigate the joint optimization of resource allocation and user association problems for SVC multicast over heterogeous cellular networks (HetNet) employing the schemes of cell range expansion (RE) and almost blank subframe (ABS). We solve the joint optimization problem by decoupling it into two problems, namely, resource allocation (RA) subproblem and user association (UA) master problem. For the RA subproblem, we propose a dynamic programming based algorithm to optimally set the transmission profile. For the UA master problem, we propose a similarity-based negotiation protocol (SBNP) based algorithm to obtain the Pareto-optimal range expansion bias. The simulation results demonstrate the efficiency of these algorithms.

This paper investigates the secure information transfer issue for cognitive radio networks by exploiting the spectrum leasing technique. The design objective is to improve the secrecy rate of the primary user, and meanwhile, create the transmission opportunities for the secondary users. To achieve this goal, we consider a system model where the primary user harnesses the assist of the secondary users in the form of cooperative transmitting. And in return, the primary user provides certain transmission opportunities over licensed spectrum for the cooperating secondary users. We propose an auction-based spectrum leasing scheme to jointly formulate the optimal cooperator selection and resource allocation problems. By analyzing and solving the dominant strategy equilibrium for the proposed scheme, we present reliable predictions for the system behavior and the achievable performances. Simulation results reveal that the proposed scheme could provide substantial gains for both the primary user and the cooperating secondary user.

This paper investigates the joint optimization problem of user scheduling, user association, and resource partition in heterogeneous cellular networks (HetNet) with a general concave utility function used as the performance metric. We formulate the joint optimization problem, and decouple the problem into three sub problems. After proving the sub problems belong to the set of problems that maximizes a monotone sub modular set function with mastoid constraint, we solve them by the proposed greedy based algorithms with theoretical approximation factors. Extensive simulation results demonstrate the efficiency of the proposed algorithms in terms of system utility. In addition, we evaluate some assumptions and results in the related work to show their impacts and correctness.

Device-to-device (D2D) communication underlaying a cellular infrastructure has been proposed as a means of facilitating rich local services and offloading the base station traffic. However, D2D communication presents a challenge in radio resource management due to the potential interference it may cause to the cellular network. In this paper, the joint optimization problem of D2D mode selection, modulation and coding schemes (MCSs) assignment, radio resources and power allocation is formulated to minimize the overall power consumption under minimum required rate guarantee. The problem is decoupled into two sub-problems which are solved by Lagrangian relaxation and tabu search methods, respectively. Simulation results show its performance superiority over other schemes, especially in the scenarios with high required rate and limited resources.

The resource allocation problem to support scalable-video multicast for wireless relay networks is complex due to the existence of the relay station. In this paper, we consider the resource allocation for SVC multicast over two-hop wireless relay networks to maximize the total system utility of all users where the system utility can be a general non-negative, non-decreasing function. We model the problem in three-layer structure (choice elements, action elements, and user elements) to cope with the joint dependency and overlapping phenomena. We formulate the problem as Cascaded Coverage Problem (CCP) and propose a greedy algorithm with polynomial time complexity. Simulation results show that our algorithm keeps good performance as compared with the optimal result. We also evaluate the influence of different user distribution types and the number of relay stations.

Future 4G cellular networks are featured with high data rates and improved coverage, which will enable real-time video multicast and broadcast services. Scalable video coding with different modulation and coding schemes (MCSs) applied to different video layers is very appropriate for wireless multicast services because it can provide different video quality to different users according to their channel conditions and light-weight feedback on how many packets they have received. It is important to choose an appropriate MCS for each layer, decide how many parity packets in one layer should be transmitted, and determine the resources allocated to multiple video sessions to apply scalable video coding to wireless multicast streaming. This paper proposes an optimal algorithm that finds the optimal total system utility of all users where the utility can be a generic nonnegative, non-decreasing function of the received rate. The results from simulations revealed that our algorithm offer significant improvements to video quality over an optimal algorithm without feedback from users and a naïve algorithm especially in scenarios with multiple video session and limited resources.

Video multicast over wireless networks has its own challenges when facing the heterogeneity of networks and end-user capabilities, along with packet losses. Scalable video coding using different modulation and coding schemes (MCSs) applied to different video layers can provide different video qualities to different users according to their channel conditions. A layered hybrid NC/ARQ scheme is proposed in this paper to handle packet losses together with a structure network coding (SNC) technique to encode the SVC stream. It is important to choose an appropriate MCS for each layer, decide how many SNC packets in one layer should be transmitted, and determine the resources allocated to multiple video sessions to apply scalable video coding to wireless multicast streaming. We prove that such a resource allocation problem is NP-hard and propose an optimal algorithm with a pseudo-polynomial run time under a reasonable assumption. We also discuss the phenomenon of unexpected packet losses caused degradation of the layered hybrid NC/ARQ schemes in a high packet loss ratio environment, and propose a solution for overcoming such a problem. Our algorithm can attain the optimal transmission configuration for maximizing the expected utility for all receivers. The results from simulations revealed that our algorithm offers significant improvements to the video quality over an optimal algorithm without needing feedback from the receivers and an algorithm using a layered hybrid FEC/ARQ scheme, and it has the outstanding ability to combat unexpected packet losses.

Video multicast over broadband wireless networks suffers from packet losses induced by fading wireless channels and user heterogeneity in channel conditions within a multicast group. A promising solution to these problems is the use of layered hybrid FEC/ARQ for scalable video multicast. However, how to allocate the radio resources to multiple video layers and how to address the cross-layer combination of application layer hybrid FEC/ARQ and physical layer MCS (modulation and coding schemes) for each video layer, is not a trivial issue. We prove that this problem is NP-hard and propose an optimal Error-resilient Video Multicast (ERVM) framework in infrastructure-based broadband wireless networks. To avoid user heterogeneity and feedback implosion, we use a weighted designated user group to send light-weight feedback messages. The ERVM algorithm is based on the dynamic programming method with a pseudo-polynomial run time, and can get the optimal transmission configuration to maximize the expected utility for the designated user group, which is a probabilistic function of the received video rate. Simulation results show that our ERVM algorithm offers significant improvements over the conventional single-layer FEC/ARQ scheme and the layered FEC scheme. Furthermore, our weighted designated user group with light-weight and accurate feedbacks is better than other user feedback schemes.

In this work, we study a link-weighted and distance-constrained channel assignment problem in multi-channel mesh networks with stationary router nodes, such as community wireless networks. A good channel assignment is given as the one which can minimize interference of each link from its neighboring links and subsequently improve the network throughput. In response to it, we introduce an interference metric, namely, min-max i-value of an edge (MMIE), to explicitly accounts for interference among links that are at distance one. In addition, we further show that the link-weighted and distance-constrained channel assignment problem with respect to the interference metric is NP-hard in computation. This guides us to propose a new heuristic channel assignment algorithm called LD-CA algorithm. We study the performance of our algorithm by implementing it in a wireless simulation environment consisting of 25 nodes, each equipped with one 802.11 wireless card. The ns-2 simulation results show that in a multi-channel environment, the LD-CA algorithm significantly outperforms previously proposed channel assignment schemes by minimizing MMIE of all links.

With the growing prevalence of wireless sensor networks comes a new need for location-based access control mechanisms. Thus we need a method to perform location verification to enforce location-based access to information resources. In this work, a statistic location verification algorithm in randomly deployed sensor networks is proposed. The nodes in the networks are classified to three roles. The claimer broadcasts the position message. The witnesses rebroadcast it and report the distance and lowest hop information to the verifier. The verifier makes use of hypothesis tests to make a decision. At last the simulation results confirm the validation of the algorithm.