Current touch-based interactions on earphones are limited by hygiene concerns and the small interaction surface. Recent works attempt to bypass these issues with mid-air gesture systems using active acoustic sensing. However, these signals may be audible and pose potential hearing risks. To address this, we propose FingerBar, a mid-air gesture recognition system for earphones that relies solely on microphones without active signal transmission. FingerBar leverages the distinctive friction sounds generated by finger gestures to achieve gesture recognition. We design a gesture filtering pipeline to maintain robustness against daily noise. An adversarial training strategy further enhances user-independent performance. From a set of 16 gestures, we identify the 7 most suitable for FingerBar based on user acceptability. Extensive evaluations demonstrate high accuracy and robustness. Furthermore, a user study confirms the practicality and acceptability of the system. Our findings highlight the promise of passive acoustic sensing as a user-friendly interaction modality for earphones.

The surge in low-altitude economic activities has spurred a significant interest in sensing Unmanned Aerial Vehicles (UAVs). With the widespread deployment of 5G infrastructure and the increasing prominence of integrated sensing and communication, monitoring UAVs via 5G base stations is a natural consideration. However, the rapid Doppler shifts of UAVs and sparse 5G reference signals violate Nyquist sampling requirements. To bridge this gap, we propose LaSen, which merges reference and downlink data signals for sensing. A key challenge stems from the fact that the combination of the periodic reference signals and stochastic data signals constitutes a non-uniform, time-varying measurement matrix. LaSen formulates the tracking of UAVs as a sparse recovery problem, where the target’s kinematics are reconstructed from non-uniform, sub-Nyquist observations. LaSen overcomes the challenges of volatile 5G signal patterns in an iterative way, starting from good measurements as an anchor and progressively refining the suboptimal measurements. Real-world experiments show that LaSen significantly extends the velocity sensing capability, where the measurable speed is up to 20.2 m/s. LaSen can detect drones at a distance of 108 m and can continuously track the distance and velocity of multiple targets, even when the downlink channel is sparsely and dynamically occupied. This work demonstrates the feasibility of high-speed target sensing in next-generation dual-function 5G/6G infrastructures.

Emulation-based laboratory instruction has become an indispensable component of computer networking education, providing a strong practical complement to theoretical coursework. However, existing network emulation platforms are constrained in both usability and scalability, struggling to sustain stable operation and scale effectively under multi-host deployments with classroom-scale, high-concurrency workloads. To address these challenges, we propose Klonet , a scalable network emulation platform tailored for educational settings. Klonet integrates a browser–server (B/S) architecture, an intuitive graphical user interface (GUI), and course-oriented templates to streamline cross-host configuration, automated deployment, and centralized management, thereby reducing the operational burden on instructors and students. To support scalability, Klonet introduces a resource-aware virtual network mapping method that jointly considers CPU and bandwidth constraints, partitions experimental topologies when needed, and maps virtual nodes and links across multiple hosts, where a MaxRemain-based selection criterion effectively avoids resource fragmentation. Comprehensive evaluations under static workloads and dynamic operation demonstrate that Klonet can stably support networks with up to thousands of nodes, maintain a high deployment success ratio and efficient resource utilization across a range of task intensities, and outperform comparable schemes. These results indicate that Klonet strikes a practical balance among usability, manageability, and scalability for modern networking courses.

Millimeter-wave (mmWave) radars are indispensable components of safety-critical advanced driver assistance systems, enabling accurate and weather-resilient environmental sensing for autonomous vehicles. Despite the advanced sensing capabilities, mmWave radars remain susceptible to adversarial attacks, where malicious users attempt to distort the sensing results of victim radars, leading to hazardous driving behaviors. While existing anti-spoofing techniques have been proposed to mitigate specific attacks, they may be ineffective against adaptive adversaries. To address this critical vulnerability, we introduce AttackDeceiver, a novel anti-spoofing system using a phase-shifted interleaving waveform. By comparing range and velocity estimates from two independent virtual channels, our system effectively detects and mitigates the effects of spoofing attacks. In addition, we proactively counter adaptive spoofing attacks by inducing attackers to generate false targets with unrealistic velocity fluctuations. A compact prototype of AttackDeceiver is realized using commercial-off-the-shelf radar kits. Experimental results demonstrate the effectiveness of our system, achieving a remarkable false target recall exceeding$97.9\%$and a significant enhancement in signal-to-interference-plus-noise ratio exceeding$\text{13.46}\,\text{dB}$.

The advent of Ethereum 2.0 has introduced significant changes, particularly the shift to Proof-of-Stake consensus. This change presents new opportunities and challenges for arbitrage. Amidst these changes, we introduce BriDe Arbitrager, a novel tool designed for Ethereum 2.0 that leveragesBribery-driven attacks toDelay block production and increase arbitrage gains. The main idea is to allow malicious proposers to delay block production by bribing validators/proposers, thereby gaining more time to identify arbitrage opportunities. Through analysing the bribery process, we design an adaptive bribery strategy. Additionally, we propose a Delayed Transaction Ordering Algorithm to leverage the delayed time to amplify arbitrage profits for malicious proposers. To ensure fairness and automate the bribery process, we design and implement a bribery smart contract and a bribery client. As a result, BriDe Arbitrager enables adversaries controlling a limited ($\lt 1/4$) fraction of the voting powers to delay block production via bribery and arbitrage more profit. Extensive experimental results based on Ethereum historical transactions demonstrate that BriDe Arbitrager yields an average of 8.78 ETH (16,687.88 USD) daily profits. Furthermore, our approach does not trigger any slashing mechanisms and remains effective even under Proposer Builder Separation and other potential mechanisms will be adopted by Ethereum.

Blockchain sharding improves the scalability of blockchain systems by partitioning the whole blockchain state, nodes, and transaction workloads into different shards. However, existing blockchain sharding systems generally suffer from a small number of shards, resulting inlimited concurrency. The main reason is that existing sharding systems requirelarge shard sizesto ensure security. To enhance the concurrency of blockchain sharding securely, we propose SpiralShard. The intuition is to allow the existence of some shards with a larger fraction of malicious nodes (i.e., corrupted shards), thus reducing shard sizes. SpiralShard can configure more and smaller shards for higher concurrency at the same network size. To ensure security with the existence of corrupted shards, we propose the Linked Cross-shard Endorsement (LCE) protocol. According to our LCE protocol, the blocks of each shard are sequentially verified and endorsed (via intra-shard consensus) by a group of shards before being finalized. As a result, a corrupted shard can eliminate forks with the help of the other shards. We implement SpiralShard based on Harmony and conduct extensive evaluations. Experimental results show that, compared with Harmony, SpiralShard achieves around$19\times $throughput gain under a large network size with 4,000+ nodes.

Atrial fibrillation (AF) is characterized by irregular electrical impulses originating in the atria, which can lead to severe complications and even death. Due to the intermittent nature of the AF, early and timely monitoring of AF is critical for patients to prevent further exacerbation of the condition. Although ambulatory ECG Holter monitors provide accurate monitoring, the high cost of these devices hinders their wider adoption. Current mobile-based AF detection systems offer a portable solution, however, these systems have various applicability issues such as being easily affected by environmental factors and requiring significant user effort. To overcome the above limitations, we present MobileAF , a novel smartphone-based AF detection system using speakers and microphones. In order to capture minute cardiac activities, we propose a multi-channel pulse wave probing method. In addition, we enhance the signal quality by introducing a three-stage pulse wave purification pipeline. What’s more, a ResNet-based network model is built to implement accurate and reliable AF detection. We collect data from 23 participants utilizing our data collection application on the smartphone. Extensive experimental results demonstrate the superior performance of our system, with 98.4% accuracy, 97.6% precision, 95.8% recall, 99.2% specificity, and 96.7% F1 score.

Synchronization-induced congestion is a critical performance bottleneck in modern distributed machine learning (ML) training, where simultaneous gradient exchanges create bursty traffic patterns. Existing solutions, both reactive and proactive, struggle to balance throughput and latency in the presence of synchronized flows. We propose DeSync, a proactive traffic shaping scheme that introduces structured random delay to de-synchronize communication rounds. Evaluations with DCQCN, HPCC, DCTCP, and TIMELY demonstrate that DeSync significantly improves FCT, job completion times, and congestion metrics, enhancing existing CC mechanisms without specialized hardware.

Volumetric videos are essential for immersive applications due to their engaging and realistic experiences. However, streaming them in real time over constrained, fluctuating networks remains challenging. Progressive streaming is an effective method to mitigate this issue by gradually enhancing video quality through incremental data transmission. However, existing progressive volumetric streaming solutions often rely on specific compression algorithms or require codec modifications, leading to poor compatibility with standard codecs. In this paper, we propose P2VS, a progressive partition-based volumetric video streaming framework, to achieve codec-independent progressive streaming. Specifically, P2VS leverages the unique structure of point cloud-based volumetric video to incrementally enhance video quality without being constrained by specific compression algorithms. Moreover, we propose adaptive streaming algorithms under this framework to enhance the quality of experience (QoE). Extensive simulations demonstrate that P2VS improves QoE by 21% on average compared to non-progressive streaming schemes. It also achieves better bandwidth efficiency and full compatibility with standard codecs. A prototype is built to verify the feasibility of P2VS.

Conventional pulmonary function tests (PFTs) are important but costly. Hence, prior research has proposed IoT sensor-based solutions to facilitate cost-efficient, at-home PFT. However, these solutions require the subject to perform maximal exhalations, a task often challenging without supervision, compromising test accuracy. In response to this challenge, this study introduces EasySpiro that, for the first time, uses non-maximal exhalations to measure PFT indicators. This is challenging since PFT indicators are only defined for maximal exhalations, and there are no guidelines to derive them from submaximal exhalations. To address that, we observe that pulmonary deficiencies affect all types of breathing, where the underlying pulmonary deficiency should be the same under different breathing efforts. Leveraging this insight, we design a reconstruction model to predict the ideal maximal breathing patterns based on submaximal ones and utilize these reconstructions for PFT. Furthermore, since the body dynamics reflect the exhalation effort, we use self-supervised learning techniques to encode body dynamics into breathing effort representations to guide the reconstruction process. We integrate these designs into earphones with microphones to measure breathing patterns and IMUs to measure body dynamics. We collaborate with a hospital and develop a dataset from 50 patients with various diseases to evaluate EasySpiro's performance, which shows an accurate prediction of PFT indicators based on non-maximal exhalations with an error rate of 7%. In addition, we open-source the collected dataset to encourage future research.

This paper introduces a novel spectrum resource sharing framework in Vehicle-to-Everything (V2X) communication networks based on multi-agent reinforcement learning with graph emergent communication (MARLGEC). We formulate resource sharing as a distributed multi-agent reinforcement learning (MARL) problem, where each vehicle acts as an agent, interacting with the environment to optimize spectrum allocation strategies. The introduction of the emergent communication mechanism enhances cooperation among agents in the distributed framework. Meanwhile, the graph attention mechanism effectively reduces the communication overhead incurred by emergent communication. This enables reliable vehicle-to-vehicle (V2V) payload transmission and improves system performance under varying network conditions. The experimental results validate the method’s effectiveness, demonstrating its ability to adapt to the dynamic environment and outperform existing MARL approaches that lack communication mechanisms.

A promising way to overcome the scalability limitations of the current blockchain is to use sharding, which is to split the transaction processing among multiple, smaller groups of nodes. A well-performing blockchain sharding system requires both high performance and high security in both intra-and cross-shard perspectives. However, existing protocols either have issues in protecting security or trade off great performance for security. In this paper, we propose SP-Chain, a blockchain sharding system with enhanced Security and Performance for both intra-and cross-shard perspectives. For the intra-shard aspect, we design a pipelined two-phase concurrent voting scheme to provide high system throughput and low transaction confirmation latency. Moreover, we propose an efficient unbiased leader rotation scheme to ensure high performance under malicious behavior. For the cross-shard aspect, a proof-assisted efficient cross-shard transaction processing mechanism is proposed to guard cross-shard transactions with low overhead. We implement SP-Chain based on Harmony, and evaluate its performance via large-scale deployment. Extensive evaluations suggest that SP-Chain can process more than 10,000 tx/sec under malicious behaviors with a confirmation latency of 7.6s in a network of 4,000 nodes.

Real-time video analytics demand intensive computing resources, often exceeding device capabilities. Heterogeneous computing resources like CPU and GPU, usually work collaboratively to ensure real-time performance. GPU manage data-intensive computing, while CPU handle instruction-intensive tasks. However, analytics accuracy can degrade because of the imbalance between CPU-GPU workloads and resources, and static resources in most systems limit adaptability to dynamic workloads. In addition to CPU-GPU resource control, accuracy is highly dependent on video analytics configuration including resolution, frame rate, and model selection. In this paper, we propose Gemini+, a hardware-accelerated video analytics pipeline empowered by dual-image FPGAs. It enables flexible CPU-GPU resource adaptation by providing near-instantaneous switching between two pre-configured images. We investigate CPU-GPU resource control and video analytics configuration adaptation in a dual-image FPGA-based video analytics pipeline. We study and formulate two problems of practical importance, a single-camera and multi-camera dual computing resource control problem, i.e., SC-DCRC and MC-DCRC. We analyze the problem complexity and develop optimal and sub-optimal algorithms. We evaluate our algorithms through simulation and build a prototype for verification. The results show that Gemini+ can improve analytic accuracy by 25% compared to fixed CPU-GPU resource systems and 88% compared to GPU-dominant systems.

Radar spoofing attacks mislead victim radars by injecting false information. Successful attacks require prior knowledge of the victim radar's mode and parameters, and existing works obtain this critical information with expensive equipment, e.g., software-defined radio or spectrum analyzer. In this paper, we proposeWaston, a low-cost system for radar mode detection and parameter estimation using commercial off-the-shelf (COTS) mmWave radars. To overcome the disadvantage of low sampling frequency of COTS mmWave radars, we design two special local signals to detect frequency points and spectral shapes for radar mode detection. We propose a novel parameter estimation algorithm to estimate frequency- and time-domain parameters for spoofing different radars. We have implemented a prototype on the TI AWR1843 platform and conducted extensive experiments to evaluate the performance ofWaston. Our experimental results demonstrate thatWastonachieves an accuracy of 100$\%$for mode detection and 99$\%$for parameter estimation. Furthermore, we demonstrate that the estimated parameters can be used to launch a successful spoofing attack against the victim radar.

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

With the development of Ethereum, numerous blockchains compatible with Ethereum's execution environment (i.e., Ethereum Virtual Machine, EVM) have emerged. Developers can leverage smart contracts to run various complex decentralized applications on top of blockchains. However, the increasing number of EVM-compatible blockchains has introduced significant challenges in cross-chain interoperability, particularly in ensuring efficiency and atomicity for the whole cross-chain application. Existing solutions areeither limited in guaranteeing overall atomicity for the cross-chain application, or inefficient due to the need for multiple rounds of cross-chain smart contract execution.To address this gap, we proposeIntegrateX, an efficient cross-chain interoperability system that ensures the overall atomicity of cross-chain smart contract invocations. The core idea is todeploy the logic required for cross-chain execution onto a single blockchain, where it can be executed in an integrated manner.This allows cross-chain applications to perform all cross-chain logic efficiently within the same blockchain.IntegrateXconsists of across-chain smart contract deployment protocoland across-chain smart contract integrated execution protocol.The former achieves efficient and secure cross-chain deployment by decoupling smart contract logic from state, and employing an off-chain cross-chain deployment mechanism combined with on-chain cross-chain verification. The latter ensures atomicity of cross-chain invocations through a 2PC-based mechanism, and enhances performance through transaction aggregation and fine-grained state lock. We implement a prototype ofIntegrateX. Extensive experiments demonstrate that it reduces up to 61.2% latency compared to the state-of-the-art baseline while maintaining low gas consumption.

Benefiting from high flexibility and probability of line-of-sight, deploying unmanned aerial vehicles (UAV s) as aerial access points has emerged as a promising solution for ensuring reliable wireless connectivity in crowded events. This paper introduces a UAV scheduling process adaptive to dynamic mobile network demand, including three phases. In the sensing phase, the user distribution is sensed, and user number thresholds are set to determine whether UAV assistance is needed. The planning phase presents an enhanced mean shift algorithm to find suitable locations to deploy UAVs with a dynamic bandwidth derived from the user distribution, the UAV's maximum capacity, and the UAV's maximum throughput. The deploying phase dispatches and recalls UAV s based on planning results. Comprehensive simulation experiments are conducted on OMNeT ++ using real-world data. Results show that the proposed process shows great adaptivity, with an efficiency increase of 18.7% and a fairness increase of 28.9 % compared to the existing related works on average.

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

Industrial networks require deterministic and reliable communication, which can be achieved by Time-Sensitive Networking (TSN), a set of standards that enable precise timing and synchronization of data transmission. However, TSN is susceptible to unexpected delays caused by device malfunction, interference or cyber attacks, which can have a domino effect and disrupt multiple data flows. To address this challenge, we propose RobustTSN, a framework that protects TSN against the domino effect of delayed frames and tolerates harmless accident frames using Per-Stream Filtering and Policing (PSFP) mechanism. We develop algorithms to calculate ingress filtering schedules based on local-safe delay and global-safe interval concepts, which decide whether to accept or discard out-of-schedule frames. We use a finite state machine to model the interaction between frames and evaluate frame safety. We build a software-defined networking based system to dynamically monitor network states and reconfigure device filtering after out-of-schedule transmission occurs. We conduct experiments on practical scenario topologies and large groups of random flows to demonstrate the effectiveness and efficiency of our framework.

Motion tracking is an important aspect of human-computer interaction (HCI) and recent research focuses on motion tracking using earphones' embedded acoustic sensors. However, these solutions can only be deployed on wired ear-phones, while most of the commercial earphones are wireless ones. This limitation arises because wireless earphones utilize the Bluetooth Low Energy (BLE) protocol for handling audio data, which blocks the usage of existing acoustic sensing solutions. Firstly, the low sampling rate of BLE prevents the system from processing high-frequency ultrasounds. However, the sensing signal for earphones must be ultrasonic to prevent disturbance to the user. Secondly, BLE employs an audio compression process that is applied with different compression rates with different bandwidths. This will break the structure of wideband signals usually used for acoustic sensing. To overcome these challenges, we present BLEAR, the first earphone-tracking system compatible with the BLE audio recording protocol. To let BLE earphones receive ultrasounds, BLEAR utilizes a specially designed bandwidth conversion scheme that uses a mask signal to trigger a non-linear effect that converts high-frequency components to low-frequency ones, thereby overcoming the low audio sampling rate restriction of BLE. Additionally, by strategically designing beacon signals to align with BLE's subband compression pattern, BLEAR mitigates the influence of audio compression and achieves accurate wireless earphone tracking. We implement a wireless earphone prototype for BLEAR and conduct extensive experiments involving 8 subjects to demonstrate its feasibility. The experimental results show that BLEAR achieves a mean distance tracking error of 3.37 cm, an angle tracking error of 5.3 degrees, and an accuracy of 97.14% in recognizing 7 common user activities. This work not only introduces a BLE-compatible earphone tracking solution but also establishes a foundation for broader BLE device tracking applications.

We propose FlexibleBP, a novel cuffless blood pressure monitoring system using a wrist-worn flexible sensor to enhance comfort and accuracy. By capturing pulse wave signals from the radial artery, we develop a personalized estimation framework incorporating a Transformer model with fine-tuning. Experiments with 36 participants confirm FlexibleBP's accuracy, meeting AAMI standards. This work marks a step toward more user-friendly, advanced wearable BP monitoring solutions.

In the domain of Time-Sensitive Networking (TSN), the quest for ultra-reliable low-latency communication is paramount. Current scheduling strategies, which hinge on strict isolation to ensure low latency and jitter, confront the challenges of high overhead in worst-case latency evaluation and consequent limitations in network flow capacity. This paper introduces an innovative framework that transcends traditional isolation constraints, thereby expanding the solution space and augmenting network schedulability. At the heart of this framework lies a novel latency jitter analysis method that assesses the viability of non-isolation scenarios with constant time complexity. This method underpins a heuristic scheduling algorithm that not only boasts the smallest time complexity among existing heuristics but also significantly increases the number of scheduled flows. Complementing this, we integrate a discrete time reference approach to hasten time-intensive scheduling operations, achieving an optimal balance between schedulability and runtime efficiency. The framework further incorporates a workload-shifting technique to enhance online scheduling responsiveness. It adeptly manages the variability in scheduling times caused by disharmonious flow periods, further bolstering the framework’s robustness. Experimental validations demonstrate that our framework can increase the scheduled flows up to 269%. It reduces scheduling runtime by up to 98.44% for medium-scale networks while maintaining a flat runtime growth curve, ensuring predictable performance in online scheduling scenarios.

Head tracking is a technique that allows for the measurement and analysis of human focus and attention, thus enhancing the experience of human-computer interaction (HCI). Nevertheless, current solutions relying on vision and motion sensors exhibit limitations in accuracy, user-friendliness, and compatibility with the majority of commercial off-the-shelf (COTS) devices. To overcome these limitations, we present, an earphone-based system that achieves head tracking exclusively through acoustic signals. employs acoustic sensing to measure the movement of a pair of earphones, subsequently enabling precise head tracking. In particular, a pair of speakers generates a periodically fluctuating sound field, which the user’s two earphones detect. By assessing the distance and angle alterations between the earphones and speakers, we propose a model to determine the user’s head movement and orientation. Our evaluation results indicate a high degree of accuracy in both head movement tracking, with an average tracking error of 2.98 cm, and head orientation tracking, with an average error of 1.83 degrees. Furthermore, in a deployed exhibition scenario, we attained an accuracy of 89.2% in estimating the user’s focus direction.

Gridless methods show great superiority in line spectral estimation. These methods need to solve an atomic$l_{0}$norm (i.e., the continuous analog of$l_{0}$norm) minimization problem to estimate frequencies and model order. Since this problem is NP-hard to compute, relaxations of the atomic$l_{0}$norm, such as the nuclear norm and reweighted atomic norm, have been employed for promoting sparsity. However, the relaxations give rise to a resolution limit, subsequently leading to biased model order and convergence error. To overcome the above shortcomings of relaxation, we propose a novel idea of simultaneously estimating the frequencies and model order using the atomic$l_{0}$norm. To accomplish this idea, we build a multiobjective optimization model. The measurement error and the atomic$l_{0}$norm are taken as the two optimization objectives. The proposed model directly exploits the model order via the atomic$l_{0}$norm, thus breaking the resolution limit. We further design a variable-length evolutionary algorithm to solve the proposed model, which includes two innovations. One is a variable-length coding and search strategy. It flexibly codes and interactively searches diverse solutions with different model orders. These solutions act as steppingstones that helpfully exploring the variable and open-ended frequency search space and provide extensive potentials toward the optima. Another innovation is a model-order pruning mechanism, which heuristically prunes less contributive frequencies within the solutions, thus significantly enhancing convergence and diversity. Simulation results confirm the superiority of our approach in both frequency estimation and model-order selection.

Spatial crowdsourcing leverages the widespread use of mobile devices to outsource tasks to a crowd of users based on their geographical location. Despite its growing popularity, current crowdsourcing systems often suffer from a lack of transparency, centralization, and other security issues. Blockchain technology has revolutionized this sector with its potential for decentralization, security, and transparency. However, existing blockchain-based crowdsourcing systems often overlook efficient task assignment mechanisms and expose users to potential losses due to the obligatory deposit payments to smart contracts, which might be vulnerable or untrustworthy. This article proposes EDF-Crowd, an E fficient and D eposit- F ree blockchain-based spatial crowdsoucing framework, to address these challenges. EDF-Crowd introduces an efficient, customizable task assignment mechanism based on smart contracts, operating periodically and batch-wise. We also design a fair compensation mechanism to compensate users for the extra overhead caused by invoking certain smart contracts. More importantly, we propose a series of linkage protocols. By linking users’ back-and-forth actions, EDF-Crowd can regulate user behavior without requiring users to deposit. The versatility of EDF-Crowd also allows its application to generic crowdsourcing systems with minimal modifications. We implement EDF-Crowd based on the EOS blockchain. Extensive evaluations show that EDF-Crowd achieves high task assignment efficiency and low cost.

Sharding is an effective technique to improve the scalability of blockchain. It splits nodes into multiple groups so that they can process transactions in parallel. To achieve higher parallelism and concurrency at large scales, it is desirable to maintain a large number of small shards. However, simply configuring small shards easily results in a higher fraction of malicious nodes inside shards, causing shard corruption and compromising system security. Existing sharding techniques hence demand large shards, at the expense of limited concurrency. To address this limitation, we propose CoChain: a blockchain sharding system that can securely configure small shards for enhanced concurrency. CoChain allows some shards to be corrupted. For security, each shard is monitored by multiple other shards. The latter reach a cross-shard Consensus on the Consensus results of their monitored shard. Once a corrupted shard is found, its subsequent consensus will be taken over by another shard, hence recovering the system. Via Consensus on Consensus, CoChain allows the existence of shards with more fraction of malicious nodes (<2/3) while securing the system, thus reducing the shard size safely. We implement CoChain based on Harmony and conduct extensive experiments. Compared with Harmony, CoChain achieves 35x throughput gain with 6,000+ nodes.

We propose a practical and efficient radar interference mitigation system, Radar-CA. Radar-CA overcomes the limitations of requiring any additional equipment or resources. Radar-CA transfers the access time estimation problem to a frequency estimation problem, enabling interference mitigation through a central controller, and our preliminary result shows that Radar-CA is capable of mitigating interference efficiently in a dense radar network.

Video analytics systems conduct video preprocessing to filter out unnecessary frames and model inference using appropriately selected neural networks for high analytics speed. Video preprocessing is instruction-intensive computing (IIC) executed by CPU, and model inference is data-intensive computing (DIC) executed by GPU. In this paper, we show the analytics accuracy of existing systems can largely vary in fields, caused by thedynamicIIC and DIC workloads of differentcontentsin applications. Unfortunately, cameras havefixedCPU/GPU resources and cannot effectively adapt to workload dynamics. We develop Gemini, a new edge-side real-time video analytics system enhanced by a dual-image FPGA. We take the advantage of negligible image switching time of dual-image FPGAs, pre-configure one CPU image and one GPU image and elastically multiplex the dual CPU-GPU resources intimedimension. Gemini requires both hardware and software revisions. In hardware, we overcome challenges of hardware-dependent application development, low communication efficiency between the microprocessor and FPGA, and high programming complexity by hardware abstraction, asynchronous data transfer mechanism and stub-skeleton middleware. In software, we overcome the challenge of adapting to the dynamic workloads by a bandit learning approach. We implement Gemini and show that Gemini can improve the analytics accuracy to 90.35%.

Millimeter-wave (mmWave) radars have found applications in a wide range of domains, including human tracking, health monitoring, and autonomous driving, for their unobtrusive nature and high range accuracy. These capabilities, however, if used for malicious purposes, could also result in serious security and privacy issues. For example, a user’s daily life could be secretly monitored by a spy radar. Hence, there is a strong urge to develop systems that can detect and locate such spy radars. In this paper, we proposeRadar2, a practical system for passive spy radar detection and localization using a single commercial off-the-shelf (COTS) mmWave radar. Specifically, we propose a novelFrequency Component Detectionmethod to detect the existence of mmWave signals, distinguish between mmWave radar and WiGig signals using a waveform classifier based on a convolutional neural network (CNN), and localize spy radars using triangulation based on the detector’s observations at multiple anchor points. Not only doesRadar2work for different types of mmWave radar, but it can also detect and localize multiple radars simultaneously. Finally, we performed extensive experiments to evaluate the effectiveness and robustness ofRadar2in various settings. Our evaluation results show that the radar detection rate is above 96% and the localization error is within 0.3m. The results also reveal thatRadar2is robust against various environmental factors (e.g., room layout and human activities).

Blockchain sharding has been increasingly used to improve blockchain systems’ performance, in which a blockchain is split into multiple smaller, disjoint shards. In practice, however, sharding can only achieve limited throughput and latency improvement, especially for theuser-perceived transaction confirmation delay.The performance degradation is believed to be caused by the cross-shard transactions. However, we show, through comprehensive system deployment and measurement studies, that the main culprit is theimbalanced transaction loadon different blockchain shards. To address this problem, we propose a novel sharding system, called LB-Chain, whichdynamicallybalances the transaction load on different shards by periodicallymigrating active accountsfrom heavily-loaded shards to less-loaded ones. We have implemented a prototype of LB-Chain, and evaluated its performance through large-scale blockchain deployment using real-world transaction traces. Extensive experiments confirm that LB-Chain significantly boosts sharding performance, reducing the transaction confirmation delays by up to 90% while increasing the transaction throughput by more than 10%. The delay difference between different accounts is also reduced dramatically, leading to improved fairness in the system.

Measuring traffic metrics is indispensable in virtual networks as it is the basis for a wide range of applications, such as network diagnostics and performance evaluation of the network algorithms. However, existing measurement schemes fail to have all these excellent characteristics simultaneously: 1) fine-grained, i.e. to obtain per packet level information. 2) lightweight, namely low CPU and bandwidth overhead. 3) network-wide, which means obtaining metrics of the whole network, e.g. per packet path. 4) easy-to-deploy, which refers to deployment without additional modification of Maximum Transmission Units (MTUs). We design vNetRadar, a virtual network measurement system, which has these excellent characteristics simultaneously. Specifically, vNetRadar 1) identifies each packet without increasing the size of each packet, to obtain network-wide metrics without MTU modification, 2) allocates each packet an area in memory, called backpack, and carries metadata in it to largely reduce bandwidth overhead. vNetRadar is implemented based on the extended Berkeley Packet Filter (eBPF) and is mainly in kernel space, avoiding the CPU overhead of copying packets to user space when performing the fine-grained measurement. Evaluation results show that the easy-to-deploy vNetRadar can get fine-grained network-wide metrics with low CPU and bandwidth overhead.

Sharding is a promising way to achieve blockchain scalability, increasing the throughput by partitioning nodes into multiple smaller groups, splitting the workload. However, when tackling the increasingly important smart contracts, existing blockchain sharding protocols do not scale well. They usually require complex multi-round cross-shard consensus protocols for contract execution and extensive cross-shard communication during state transmission, mainly because that each shard stores and executes an isolated, disjoint subset of contracts. In this paper, we present Jenga, a novel sharding-based approach for efficient smart contract processing. Its main idea is to break the isolation between shards by orchestrating the logic storage, state storage, and execution of smart contracts. In Jenga, all shards share the logic for all contracts. Therefore, multiple contracts involved in a smart contract transaction can be executed together by the same shard within one round. Moreover, different shards store distinct states (named state shards), several "orthogonal" execution channels are established based on the state shards, where each channel overlaps with all shards. Each node simultaneously belongs to a shard and an "orthogonal" channel, different channels execute different contracts. Therefore, via the overlapped nodes, the contract states can be directly broadcast between the state shards and the execution channels without additional cross-shard communication. We implement Jenga and evaluation results show that it provides outstanding performance gains in terms of throughput and transaction confirmation latency.

Edge-side real-time video analytics systems recognize spatial or temporal events (e.g., vehicle counting) in a video stream. To meet the delay requirement, existing systems in smart edge cameras conduct video preprocessing to filter out unnecessary frames and model inference using appropriately selected neural network (NN) models. Video preprocessing is instruction-intensive computing (IIC) and executed by the CPU of the edge camera, and model inference is data-intensive computing (DIC) and executed by the GPU of the edge camera. In this paper, we show that the analytics accuracy of existing systems can largely vary in fields. The root cause is that video analytics applications have different contents, which result in dynamic IIC and DIC workloads. Unfortunately, intelligent cameras in fields have fixed CPU and GPU resources and cannot effectively adapt to workload dynamics. We develop Gemini, a new real-time video analytics system enhanced by a dual-image FPGA. The newly developed dual-image FPGAs can be pre-configured with two FPGA images with a key advantage of negligible image switching time. We thus pre-configure one CPU image and one GPU image and elastically multiplex the dual CPU-GPU resources in the time dimension. The Gemini system design requires both hardware and software revisions. We overcame a challenge that the application development on different dual-image FPGAs is hardware-dependent. We develop a new abstraction of hardware functions to make the Gemini system hardware-agnostic. It is also a challenge to adapt to the dynamic workloads and optimize video analytics accuracy. We develop a bandit learning approach to capture content dynamics and conduct dual computing resource control. We implement Gemini and show that Gemini can improve the analytics accuracy to 90.35 %. We further evaluate Gemini by a case study where we use Gemini to support an intrusion detection application, and Gemini shows consistent high analytics accuracy.

The traditional interaction method for smart eyewear is by touching a control panel located at the temple front of the eyeglass. This method can be unnatural since the control panel and the display are not within the same plane. In this paper, we propose a new and natural interaction technology for smart eyewear that allows users to interact with the rim of the eyeglass without adding additional hardware to the rim. This design is based on an observation that a finger touch would slightly alter the channel frequency response (CFR) of the eyeglass. We use one pair of piezoelectric (PZT) transducers to measure the CFR, and we recognize the tiny CFR changes by analyzing the complex representation of the CFR. The system detects five touch locations using a deep learning classifier. We recruit ten subjects to evaluate the system and the result shows that the system can recognize the five touch locations with an F1 score of 0.91.

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

Due to the proliferation of WiFi devices and the high verification precision, researchers have shown interests in WiFi-based multilateration location verification (WMLV), where multiple WiFi APs (also known as verifiers) verify the location information claimed by a prover. However, it is a high expenditure for any single location-based service provider to deploy densely covered WiFi facilities. Incentivizing independent WiFi owners to corporately verify location information is thus a feasible solution to this plight, yet none of the previous research has taken this into consideration. To this point, we design a double auction-based incentive mechanism for WMLV, which motivates the participation of both provers and verifiers. More importantly, we consider practical situations, where the provers have various verification precision requirements, and different number of verifiers are required by different provers. The proposed double auction mechanism achieves desirable economical properties, includingtruthfulness, individual rationality, computational efficiency, budget balance,andnonnegative social welfare.The desired properties are validated through both theoretical analysis and extensive simulations.

Miniature radar has demonstrated its great potential in smart homes, such as understanding the wellness of the residents and providing ubiquitous interactions. While it has many promising applications, it also results in congested RF (radio frequency) environments as there is an unprecedented amount of traffic in a smart home. To ease the strain on the limited spectrum, we ask the question that, can we reuse the sensing signals for data communication? With such a capability, we can improve the spectrum utilization by sharing the spectrum between sensing and communication systems. However, radar signals are customized for the sensing purpose and are incompatible with legacy communication standards. To address this challenge, we have an observation that, non-linearity effect in RF circuits can convert wideband radar signals into a LoRa signal. Based on this observation, in this paper, we present LoRadar, which enables an FMCW (Frequency-Modulated Continuous Wave) radar to carry LoRa signals in sensing waves. We present both the downlink and uplink design, enabling a LoRadar device to communicate with LoRa nodes in a bi-directional way. We implement LoRadar and evaluation results show that LoRadar can achieve home-level coverage with 3.4kbps data rate while it preserves the sensing resolution of the radar.

Federated learning (FL) has attracted more and more attention recently. The integration of FL and edge computing makes the edge system more efficient and intelligent. FL usually uses the server to actively select certain edge devices to participate in the global model training. However, the selected edge devices may be stragglers, or even crash during training. Meanwhile, the unselected idle edge devices cannot be fully utilized for training. Therefore, besides the widely studied communication efficiency and data heterogeneity issues in FL, we also take the above time efficiency into consideration, and propose a time-efficient asynchronous federated learning protocol, TEA-Fed, to solve these problems. With TEA-Fed, idle edge devices actively apply for training tasks and participate in model training asynchronously once assigned tasks. Considering that there may be a huge number of edge devices in edge computing, we introduce control parameters to limit the number of devices participating in training the identical model at the same time. Meanwhile, we also introduce caching mechanism and weighted averaging with respect to model staleness in the model aggregation step to reduce the adverse effects of model staleness and further improve the accuracy of the global model. Finally, the experimental results show that the protocol can accelerate the convergence of model training, improve the accuracy, and has robustness to heterogeneous data.

With the proliferation of human-carried mobile devices, spatial crowdsourcing has emerged as a transformative system, where requesters outsource their spatiotemporal tasks to a set of workers who are willing to perform the tasks at the specified locations. However, in order to make efficient assignments, the existing spatial crowdsourcing system usually requires workers and/or tasks to expose their locations, which raises a significant concern of compromising location privacy. In addition, traditional spatial crowdsourcing systems employ a centralized server to manage the information of workers and tasks. Such a centralized design does not scale to a large number of workers/tasks, making the server easily a bottleneck. In this article, we present an online framework for assigning tasks to workers without compromising the location privacy in a fully distributed manner. Our system protects the location privacy of both workers and tasks through homomorphic encryption. We further propose a novel wait-and-decide mechanism and a proportional-backoff mechanism to increase the number of assigned tasks. Extensive experiments on real-world data sets illustrate that our proposed system achieves a large number of task assignments in an efficient and privacy-preserving manner.

Recent works have shown that acoustic signals can be leveraged to perform respiration monitoring with high accuracy and low energy consumption. Since smartphones, smart speakers, and many other IoT devices are already equipped with microphones and speakers, it is convenient to implement the acoustic sensing solutions on those devices. However, the existing technologies require the speaker to transmit certain ultrasonic signals to detect respiration. Although these signals are inaudible to adults, they are audible to children and pets and they may even have negative impacts on plants. In this article, instead of using ultrasonic signals, we are trying to leverage audible signals in daily lives, e.g., music or broadcasting audios, to detect human respiration. We design a respiration detection system which derives the respiration rate by continuously estimates the channel impulse response (CIR) using music and broadcast signals. We study the intersymbol interference (ISI) brought by the randomness of music and broadcast signal and give our strategy to minimize the interference. We also propose several techniques to resolve some practical issues, such as the multipath effect and sampling frequency offset between the speaker and the microphone. Extensive experiments are conducted to demonstrate the feasibility of our system. The result shows that our system can achieve high respiration detection accuracy with the mean error of less than 0.5 BPM when different audio signals are used.

Face authentication systems have gained widespread popularity because of their user-friendly usage and increasing recognition accuracy. Unfortunately, the boom in mobile social networks has bought with it media-based facial forgery; a critical threat where an adversary forges or replays the victim's photograph/video to fool the system. In this article, we propose EchoFace, an effective and robust liveness detection system to enhance face authentication in defending against media-based attacks, which works with today's smartphones/smartwatches without any hardware modification. EchoFace uses active acoustic sensing to differentiate the uneven stereostructure of the face and the flat forged media. Our proposed scheme effectively extracts the desired reflection profiles from the target. Moreover, we propose effective similarity measurements of reflection profiles to distinguish live users from forged media, which works robustly under various environmental conditions. EchoFace only requires low cost and universally equipped acoustic sensors without human intervention for liveness detection, which can be easily deployed in a variety of application scenarios. We implement EchoFace on commercial smartphones, and experiment results show that EchoFace achieves an average detection accuracy higher than 96% and false alarm rate lower than 4% across various media attacks and different levels of background noise. This shows its great potential to enhance the security of widely deployed face authentication systems in real scenarios.

The design paradigm for backscatter tags is to avoid complex functionality and make tags as simple as possible. However, such a design principle leads to the prevalence of signal collision as tags cannot sense other tags' ongoing transmissions. The high probability of tag collision will result in low overall throughput. Although there are some existing efforts to resolve tag collisions, they either require good channel conditions or can only resolve a limited number of tags as channel capacity is deficient when SNR is low. In this article, to overcome this limitation, we bring in antenna arrays to boost the channel capacity. We propose FreeScatter, which can support scalable concurrent backscatter transmission using an antenna array. FreeScatter extracts the path that signals traveled and formulates the tags' channel coefficient using the path representations. FreeScatter further exploits the frequency agnostic property so that it can support more spatial streams than the number of antennas. The experimental results show that we can enable up to 20 tags transmitting concurrently. With the ubiquitous connectivity of battery-free tags in the near future, FreeScatter can significantly boost the network throughput.

Existing Online to Offine (O2O) take-out food ordering systems expose the defects that the deliveryman is responsible for delivering limited orders at a time. The inefficiency of this method results in high delivery fee. In order to improve the efficiency of delivery, we proposed the FoodCarpool, a negotiation-based carpooling system for take-out food delivery. Comparing to existing take-out food delivery system which is both time and resource intensive, theoretical analysis ensures that our negotiation mechanism based system model will always increase users' utilities. Finally, we conduct a set of experiments to evaluate the performance of our system. The experimental results show that comparing to the existing scheme, our approach improves total users' utilities and ensures the satisfactions of users.

Human activity recognition is a very active research on pervasive computing and mobile health application. Many human activity systems based on inertial measurement unit (IMU) sensor data were proposed in the past few years. These systems mainly use IMU sensor placed on he torso and limbs to collect data and utilize supervised machine learning algorithms on sensor data. One main issue of these systems is that wearing multiple on-body IMU sensors may bring inconvenience to users' daily life. The other issue of these exiting methods is that an activity recognition model that is trained on a specific subject does not work well when being applied to predict another subject's activities since IMU activity data always carry information that is specific to the human subject who conducts the activities. In our work, inspired by the principle of domain adaption, we proposed a new deep-learning activity recognition model based on an adversarial network which can remove the subject-specific information within the IMU activity data and extract subject-independent features shared by the data collected on different subjects. We also for the first time use data collected from insole based IMU sensors on 8 participants for 5 common activities to build a new real world human activity dataset which can minimize the inconvenience for users to wear. We conducted experiments with our new real-world dataset. Results show that our subject independent activity recognition model outperforms state-of-art supervised learning techniques and eliminates the effects of individual differences between subjects successfully. The average recognition accuracy under the leave-one-out (L1O) condition achieves 99.0% which is higher than the performance of traditional human activity recognition system based on CNNs.

In a crowdsourcing system, security is a critical issue which affects the participation willingness of users. To motivate users' participation, most of existing work provide additional reward to compensate their loss due to security issues. However, more efficient way is to motivate the users to arm with higher security capability, to reduce the infection probability from the attackers and malicious software. In this paper, we propose an insurance-based incentive framework to motivate the users to upgrade to a higher security level. The framework can be formed as a Stackelberg game, where crowdsourcing platform is the leader and the users are followers. Through backward induction, we found that a Nash Equilibrium exists in the Stackelberg game. Simulation result shows that the proposed mechanism can enhance both social welfare, platform utility and users' utility in the crowdsourcing system.

With the development of embedded sensors in smartphones and the ever-growing number of mobile users, more and more spatial crowdsourcing applications come into our daily lives. Food delivery, a specific application of spatial crowdsourcing, emerged and quickly proliferates in recent years. However, task selection and scheduling remains a challenging problem in food delivery. In this paper, we aim to address the task selection and scheduling problem from a game-theoretic perspective. Specifically, the riders are allowed to select a set of tasks as well as the task completion order, by taking account of their locations, speed, traveling costs and capacities. The objective of each rider is to maximize his own utility. We formulate a food delivery game and prove the existence of a Nash equilibrium. Experimental results show that our scheme outperforms existing solutions in terms of the social welfare, average utility, task completion ratio, and fairness. Moreover, the social welfare attained by our scheme comes close to that of the centralized optimal solution.

Mobile crowdsourcing is a popular computing paradigm that enables smart devices to measure and collect various sensing data. When the users participate in the sensing platform, they may face various attacks and fall in a non-secure environment. Under the malicious attack, the sensing data which should be transmitted to the platform may suffer from data loss, which reduces the users' utility and platform's utility. The data loss can also lead to the reduction of the users' participatory motivations because they are not able to earn expected money due to data loss. Therefore, the total sensing quality and the social welfare will be affected eventually. To solve this problem, in this paper, we propose a novel insurance-based framework to compensate the data loss due to security threat in mobile crowdsourcing systems. This framework can motivate the users with high-security levels to participate in the crowdsourcing system, thus improves the platform's utility. We formulate our framework as a Stackelberg game, where the platform is a leader and the users are followers. The theoretical analysis shows that the Nash Equilibrium exists in our framework and it can maximize the platform's revenue while considering the users' participatory willingness. Simulation results show that our framework achieves more participators, more platform's utility and social welfare, compared with existing mechanism.

Fixed channelization configuration in today's wireless devices falls inefficient in the presence of growing data traffic and heterogeneous devices. In this regard, a number of fairly recent studies have provided spectrum adaptation capabilities for current wireless devices, however, they are limited to inband adaptation or incur substantial coordination overhead. The target of this paper is to fill the gaps in spectrum adaptation by overcoming these limitations. We propose SEER, a frame-level wideband spectrum adaptation solution which consists of two major components: i) a specially-constructed preamble that can be detected by receivers with arbitrary RF bands, and ii) a spectrum detection algorithm that identifies the desired transmission band in the context of multiple asynchronous senders by exploiting the preamble's temporal and spectral properties. SEER can be realized on commodity radios, and can be easily integrated into devices running different PHY/MAC protocols. We have prototyped SEER on the GNURadio/USRP platform to demonstrate its feasibility. Furthermore, using 1.6GHz channel measurements and trace-driven simulations, we have evaluated the merits of SEER over state-of-the-art approaches.

Dynamic spectrum access (DAS) is an efficient way to redistribute spare channels among users. Conventionally, dynamic spectrum access is conducted through (double) spectrum auction, where a third-party auctioneer collects bids from buyers and sellers, and determines the spectrum allocation. Rather than placing bids only on individual channels, combinatorial spectrum auction allows buyers to express their valuations for different combinations of channels. However, auction mechanisms are generally vulnerable to the collusion between the auctioneer and buyers or sellers. Furthermore, to find the optimal allocation in combinatorial auction is usually NP-hard. In this paper, we propose to leverage a many-to-many matching framework to realize combinatorial spectrum trading. Unlike traditional many-to-many matching problem, spectrum matching is more challenging, because spectrum allocation is interference-limited rather than quota-limited. To deal with this problem, we propose a novel matching algorithm, which takes buyers' interference relationship into consideration. We theoretically prove that the matching result is individual rational, strong pairwise stable and is a subgame-perfect Nash equilibrium of the corresponding spectrum bargaining game. Simulation results show that the proposed algorithm can converge to a stable matching within a few iterations.

Dynamic spectrum access (DSA) redistributes spectrum from service providers with spare channels to those in need for them. Existing works on such spectrum exchange mainly focus on double auctions, where an auctioneer centrally enforces a certain spectrum allocation policy. In this paper, we take a different and new perspective, proposing to use matching as an alternative tool to realize DSA in a distributed way for a free market, which consists of only buyers and sellers, but no trustworthy third-party authority. Compared with conventional many-to-one matching problems, the spectrum matching problem is distinctively challenging due to the interference bound between buyers: the same channel can be reused by an unlimited number of non-interfering buyers, but must be exclusively occupied by only one of interfering buyers. In this paper, we firstly formulate the spectrum matching problem as a many-to-one matching with peer effects, i.e., a buyer's utility is affected by other buyers who are matched to the same seller. We then present a two-stage distributed algorithm that converges to an interference-free and Nash-stable matching result. Simulations show that the proposed distributed matching algorithm can achieve 90% of the social welfare from the optimal matching result.

A typical scenario for public WLANs is large audience environment where Wi-Fi hotspots serve scores of mobile devices. The performance of those Wi-Fi hotspots is extremely poor in terms of low goodput and severe delay due to heavy contention and MAC inefficiency. After carefully investigating the traffic patterns in public WLANs, we proposeCarpool, a practical design that facilitates transmission sharing among multiple receivers, to tackle this problem. The key idea is to reduce contention by feeding frames for multiple destinations into one transmission at physical layer (PHY). As such, each downlink transmission carries payloads for multiple receivers, which reduces contention overhead and enables in-time response to concurrent requests from multiple users. To achieve efficient and reliable transmission in Carpool, we propose i) a lightweight frame structure to support multiple receivers, and ii) a real-time channel estimation scheme to continuously calibrate channel estimation during the transmission of a Carpool frame. We have implemented the entire PHY of Carpool on the GNURadio/USRP platform and tested it in various indoor environments. Furthermore, our trace-driven MAC evaluation shows that Carpool achieves up to$3.2 \times$goodput gain and reduces up to$75$percent delay compared to the IEEE 802.11n MAC frame aggregation scheme.

Reflective wrist photoplethysmograph (PPG), obtained by a watch or wristband, can provide a natural and unconstrained way for daily life heart rate monitoring. However, reflective wrist PPG often suffers from poor signal quality and various distortions due to daily life motion artifact. In this paper, we analyze the influence of motion artifact on reflective wrist PPG signals, and propose a method to extract reliable heart rate from such distorted PPG signals. The proposed method consists of adaptive filtering, heart rate selection, and motion identification. Experimental results show that our proposed method can generate reliable heart rate values from wrist PPG signals with different types of motion artifact.

The proliferation of WiFi hotspots in public places enables ubiquitous Internet access. These public WiFi hotspots usually serve scores of mobile devices and suffer from extremely poor performance in terms of low good put and severe delay. In this paper, we first study the traffic characteristics in public WiFi networks, and demonstrate that the main causes of such poor performance are media access control (MAC) inefficiency and downlink-uplink traffic asymmetry. To cope with these issues, we call attention to transmission carpool, which facilitates an access point (AP) to send multiple frames for different mobile stations (STAs) in a single transmission. It reduces contention and conveys more frames in each channel access. As such, each downlink transmission carries more payload and thus improves efficiency and solves traffic asymmetry simultaneously.

Partially overlapped channels are barely used for concurrent transmission in WiFi networks, since they lead to collisions where the collided packets cannot be decoded successfully. In this paper, we observe that the actual corrupted symbols by partial-channel interference in OFDM-based WiFi networks are not as severe as we expected. There remains extra coding redundancy that can be exploited from the corrupted symbols, and utilized for packet recovery. Accordingly, we present a novel paradigm termed Piros, in order to Push the lImits of partially concurrent transmission in WiFi networks. Piros strategically leverages the coding redundancy according to the overlap portion in a distributed manner, and extracts useful decoding information from the corrupted symbols to decode the packet with partial-channel interference.

Fixed channelization configuration in today's wireless devices falls inefficient in the presence of growing data traffic and heterogeneous devices. In this regard, a number of fairly recent studies have provided spectrum adaptation capabilities for current wireless devices, however, they are limited to inband adaptation or incur substantial coordination overhead. The target of this paper is to fill the gaps in spectrum adaptation by overcoming these limitations. We propose Seer, a frame-level wideband spectrum adaptation system which consists of two major components: i) a specially-constructed preamble that can be detected by receivers with arbitrary RF bands, and ii) a spectrum detection algorithm that identifies the intended transmission band in the context of multiple asynchronous senders by exploiting the preamble's temporal and spectral properties. Seer can be realized on commodity radios, and can be easily integrated into devices running different PHY/MAC protocols. We have prototyped Seer on the GNURadio/USRP platform and tested it under various environments. Furthermore, our evaluation using 1.6GHz spectrum measurements shows that Seer largely improves system throughput over fixed channel configuration and state-of-the-art spectrum adaptation approaches.

Similar to TV bands, the guardband frequencies are not occupied therefore are whitespace that potentially allows additional communication activities. Considering the difference to TV whitespace, we propose independent communication for guardband whitespace. In this paper, we present the Pilotfish system which realizes independent communication and turns guardband whitespace into new communication channels. To address the big challenges of interference mitigation, we employ novel PHY design which includes specially customized FBMC and an Nulled Decoding technique to null the strong background signal in guardbands. We implemented Pilotfish using software radio system. Empirical evaluation results validate the Pilotfish design in both PHY and MAC.

For wireless service providers (WSP), the emerging femtocell market brings new opportunities as well as challenges. It is difficult for a WSP to have complete information of the technical strength of other WSP in the market, and it is hard to estimate the user demand, which is influenced by the price and service quality of all competing WSP in the market. To address these problems, in this paper, we propose an economic framework for the WSP to maximize their utility, via a joint pricing and spectrum allocation strategy, under the condition of incomplete information of other rival WSP in the market. We study two scenarios: 1) all WSP enter the market at the same time; 2) the WSP enter the market at different times. In both scenario, we formulate the problem as a Bayesian game, and derive the Bayesian Nash equilibrium. The simulation results verify that the proposed joint pricing and spectrum allocation strategy outperforms sole pricing or sole spectrum allocation strategy. Interestingly, when the WSP enter the market at different times, the later entered WSP chooses more aggressive pricing and spectrum allocation strategy than the early entered WSP.

To accommodate the soaring mobile broadband traffic, the Federal Communications Commission (FCC) in the U.S. sets out to retrieve under-utilized spectrum (e.g., TV Whitespace) and lay the groundwork for spectrum redistribution. Auction is an efficient way to allocate resources to those who value them the most. The large pool of spectrums to be released, especially the ones in TV Whitespace, consist of wide-range frequencies. Apart from spatial reuse, spectrum heterogeneity imposes new challenges for spectrum auction design: 1) Wireless service providers with different targeted cell coverages have different spectrum frequency preferences; 2) interference relationship is frequency-dependent due to frequency-selective signal fading. Unfortunately, existing spectrum auction mechanisms either assume spectrum valuation is homogeneous or use homogeneous interference graph to group buyers who can reuse the same spectrum. In this paper, we propose TAMES, an auction framework for heterogeneous spectrum transaction. We consider a multi-seller-multi-buyer double auction, in which every buyer submits a bid, consisting of the spectrum demand and a bidding profile of prices for spectrums contributed by all sellers. A novel buyer grouping approach is proposed to tackle the problem of heterogeneous interference graph. TAMES is proved to be truthful as well as individually rational. The simulation results show that TAMES significantly improves spectrum utilization, sellers' revenue and buyers' utility by making smart use of spectrum heterogeneity, while keeping low running time comparable with existing auction mechanisms. Moreover, via simulation, we show how to help buyers obtain continuous spectrums which further improves buyers' satisfaction.

According to the recent rulings of the Federal Communications Commission (FCC), TV white spaces (TVWS) can now be accessed by secondary users (SUs) after a list of vacant TV channels is obtained via a geo-location database. Proper business models are therefore essential for database operators to manage geo-location databases. Database access can be simultaneously priced under two different schemes: the registration scheme and the service plan scheme. In the registration scheme, the database reserves part of the TV bandwidth for registered White Space Devices (WSDs). In the service plan scheme, the WSDs are charged according to their queries. In this paper, we investigate the business model for the TVWS database under a hybrid pricing scheme. We consider the scenario where a database operator employs both the registration scheme and the service plan scheme to serve the SUs. The SUs' choices of different pricing schemes are modeled as a non-cooperative game and we derive distributed algorithms to achieve Nash Equilibrium (NE). Considering the NE of the SUs, the database operator optimally determines pricing parameters for both pricing schemes in terms of bandwidth reservation, registration fee and query plans.

Nowadays, cardiovascular disease (CVD) has become a disease of the majority. As an important instrument for diagnosing CVD, electrocardiography (ECG) is used to extract useful information about the functioning status of the heart. In the domain of ECG analysis, cluster analysis is a commonly applied approach to gain an overview of the data, detect outliers or pre-process before further analysis. In recent years, to provide better medical care for CVD patients, the cardiac telehealth system has been widely used. However, the extremely large volume and high update rate of data in the telehealth system has made cluster analysis challenging work. In this paper, we design and implement a novel parallel system for clustering massive ECG stream data based on the MapReduce framework. In our approach, a global optimum of clustering is achieved by merging and splitting clusters dynamically. Meanwhile, a good performance is gained by distributing computation over multiple computing nodes. According to the evaluation, our system not only provides good clustering results but also has an excellent performance on multiple computing nodes.

Femtocell refers to a new class of low-power, low-cost base stations (BSs) which can provide improved indoor coverage and higher voice/data Quality of Service (QoS). Hybrid access in two-tier macro-femto networks is regarded as the most ideal access control mechanism to help offload macrocell traffic to femtocell, thus enhancing overall network performance. However, without suitable incentive mechanism, the Femtocell Service Providers (FSPs) are not willing to share their femtocell resource with the Macrocell Service Provider (MSP). To address this problem, in this paper, we propose an ACcess Permission (ACP) transaction framework, in which a single MSP purchases ACP from multiple FSPs in various locations throughout T timeslots, and FSPs who have overlapped coverage compete with each other for selling their ACP. However, we are facing the challenge that the demand of MSP in each location dynamically changes at each timeslot. At the start of each timeslot, FSPs are unaware of the demand of MSP, which impedes them to choose an ideal strategy that yields high payoff. To address the problem of information incompleteness, we propose an adaptive strategy updating algorithm, which is based on online learning process and enables FSPs to obtain guaranteed payoff. We conduct simulations to evaluate the payoff and the payoff gap of the FSPs when the MSP's demand is constant, quasi-constant or probabilistic. We also show that the payoff of the FSPs is affected by the learning speed of the proposed algorithm.

In this paper, we consider the scenario where a secondary operator leases licensed spectrum from a spectrum owner and then serves its end users with both licensed and unlicensed bands. An end user can decide its traffic segmentation in terms of the percentage of its total amount of traffic demand transmitted via licensed and unlicensed band respectively. The optimal spectrum investment and pricing decision of the secondary operator is studied considering the user-determined traffic segmentation. We model and analyze the interactions among the spectrum owner, the secondary operator and the end users with Stackelberg game. By deriving optimal strategies for the three types of game players, we show that the end users can increase their utility by determining a proper traffic segmentation on the licensed and unlicensed band considering both the service price and the quality of service (QoS). Also the secondary operator and the spectrum owner can make more profit leveraging dynamic spectrum leasing.

Spectrums are heterogeneous, especially from the aspect of their central frequency. According to signal propagation properties, low-frequency spectrum generally has lower path loss, thus longer transmission range, compared with high-frequency spectrum. Cellular operators with different targeted cell size will have different preferences for spectrums with different frequencies. Furthermore, the transmission range also affects the interference relationships among transmitters. Transmitters who can reuse the same high-frequency spectrum may interfere with each other when reusing the low-frequency spectrum, so it is difficult to decide how to construct the interference graph to exploit spectrum reusability among transmitters. Auction is considered as an efficient way for spectrum allocation. However, most of the previous works only considered homogenous spectrum auction, failing to address the problem of spectrum heterogeneity. In this paper, we propose TAMES, a Truthful Auction Mechanism for hEterogeneous Spectrum allocation, which allows buyers to freely express their different preferences towards different spectrums. Frequency-specific interference graphs are constructed to determine buyer groups. The proposed heterogeneous spectrum auction is theoretically proved to be truthful and individual rational. The simulation results verifies that the proposed auction mechanism outperforms other auction mechanisms with homogenous bid or homogenous interference graph. The proposed auction mechanism is able to yield higher buyers' satisfaction, seller's revenue and spectrum utilization.

According to the recent rulings of the Federal Communications Commission (FCC), TV white spaces (TVWS) can now be accessed by secondary users (SUs) after a list of vacant TV channels is obtained via a geo-location database. Proper business models are essential for database operators to manage the cost of maintaining geo-location databases. Database access can be simultaneously priced under two different schemes: the registration scheme and the service plan scheme. In the registration scheme, the database reserves part of the TV bandwidth for registered White Space Devices (WSD) in a soft-license way. In the service plan scheme, WSDs are charged according to their queries. In this paper, we investigate the business model for the TVWS database under a hybrid pricing scheme. We consider the scenario where a database operator employs both the registration scheme and the service plan scheme to serve the SUs. The SUs' choices of different pricing schemes are modeled as a non-cooperative game and we derive distributed algorithms to achieve the Nash Equilibrium (NE). Considering the NE of the SUs, the database operator optimally determines the pricing parameters for both pricing schemes in terms of bandwidth reservation, registration fee and query plans.

The vision of Self-Organizing Networks (SON) has been drawing considerable attention as a major axis for the development of future networks. As an essential functionality in SON, cell outage detection is developed to autonomously detect macrocells or femtocells that are inoperative and unable to provide service. Previous cell outage detection approaches have mainly focused on macrocells while the outage issue in the emerging femtocell networks is less discussed. However, due to the two-tier macro-femto network architecture and the small coverage nature of femtocells, it is challenging to enable outage detection functionality in femtocell networks. Based on the observation that spatial correlations among users can be extracted to cope with these challenges, this paper proposes a Cooperative femtocell Outage Detection (COD) architecture which consists of a trigger stage and a detection stage. In the trigger stage, we design a trigger mechanism that leverages correlation information extracted through collaborative filtering to efficiently trigger the detection procedure without inter-cell communications. In the detection stage, to improve the detection accuracy, we introduce a sequential cooperative detection rule to process the spatially and temporally correlated user statistics. In particular, the detection problem is formulated as a sequential hypothesis testing problem, and the analytical results on the detection performance are derived. Numerical studies for a variety of femtocell deployments and configurations demonstrate that COD outperforms the existing scheme in both communication overhead and detection accuracy.

Sleep apnea, which is a common sleep disorder characterized by the repetitive cessation of breathing during sleep, can result in various diseases, including headaches, hypertension, stroke and cardiac arrest, as well as produce severe consequences such as impaired concentration and traffic accidents. A traditional diagnosis method of sleep apnea is polysomnography, which can only be conducted in sleep center with specialized personals, thus is expensive and inconvenient. Moreover, it is only used for understanding the conditions, without treatment function. Some other methods or devices have been developed to alleviate sleep apnea, such as continuous positive airway pressure (CPAP) and intraoral mandibular advancement device and surgery. However, they only provide a treatment method without detection or monitoring function. There is no existing device which can provide both apnea detection and treatment functionality. In this paper, we propose and implement a smart phone-based auto-adjustable pillow system, which enables both sleep apnea detection and treatment. Sleep apnea events can be detected in real-time using the blood oxygen sensor, accordingly, the height and shape of the pillow can be automatically adjusted to terminate the sleep apnea event. On the other hand, after the adjustment, the sensor can continuously monitor the blood oxygen signal to evaluate the effectiveness of the pillow adjustment and to help in selecting a suitable adjustment scheme. Therefore, a real-time feedback control system is formed. Besides, compared with existing diagnosis or treatment devices, our system is non-invasive, inexpensive and portable, which can be used at home or during traveling. In this paper, a real-time sleep apnea detection and classification algorithm is proposed to decide whether the pillow should be adjusted or not. We also design a real-time feedback pillow adjustment algorithm, to decide when and how to adjust the pillow and how to evaluate the effectiveness of the adjustment. We conducted experiments on 40 patients, which demonstrate that using our novel smart pillow system, both the sleep apnea duration and the number of sleep apnea events are dramatically reduced by more than 50%.

Femtocells' ability to improve the in-building coverage and capacity in a cost-efficient way has drawn significant attention from mobile operators. However, a mobile operator may lack a fixed-line network infrastructure, which is indispensable for enabling femtocell service. In this paper, we propose a hybrid cooperation framework where a mobile operator can collaborate with a fixed-line operator (as a virtual integrated operator) to provide femtocell service to indoor users. The framework consists of sequential game and Nash bargaining. The sequential game models the interactions of the operator and users. Specifically, the operator announces the price for wireless services first and then the users decide their spectrum demands in response to the given price. Then the two operators divide the cooperation benefit according to the Nash bargaining model, which makes the profit sharing fair and cooperation framework amenable to operators. We theoretically derive the unique closed-form equilibrium for the framework as well as the conditions that promote the cooperation. The simulation results verify that the cooperation framework can make more revenue for the operators and the spectrum efficiency is significantly improved.

Self-healing functionality is developed to allow Self-Organizing Networks (SON) autonomously recover from outage. The dynamic topology and configurations of femtocell access points (femto APs) bring significant challenges for enabling self-healing functionality in femtocell networks. Observing that outage in femtocells only has local impacts, this paper proposes a local grouping architecture (LOGA). To recover from outage, LOGA forms the Inner Group to make reconfigurations of femtocells as local as possible. Furthermore, to prevent these reconfigurations from causing extra outages, LOGA sets additional constraints on the Outer Group, which consists of femtocells in the vicinity of the reconfigured femtocells. To properly form local groups in this architecture, a sequential grouping algorithm is proposed. Numerical results demonstrate that LOGA outperforms the existing self-healing scheme both in the number of reconfigured femtocells and in the recovered SINR.

The available spectrum in cognitive radio networks is usually discontinuous but wide, which provides abundant frequency domain diversity. In this paper, we identify the opportunity of leveraging the newly-emerged technique interference alignment to exploit such diversity to support concurrent transmission and improve the network throughput in secondary networks. To enable interference alignment, independent-fading subcarriers should be grouped together to provide sufficient dimensions for intended signals and non-intended interferences at the receiver side. We formulate the subcarrier grouping problem for interference alignment to maximize the number of concurrent transmissions, and propose a greedy-based algorithm to solve it, which is proved to be optimal. Simulation results show that using the proposed scheme, the total throughput in cognitive radio networks can be greatly improved.

Excessive stress may lead to health problems like headache, trouble sleeping, depression and chronic diseases such as cardiovascular and cerebrovascular diseases. In this paper we present deStress, the mobile and remote stress monitoring, alleviation and management system, whose features are: firstly it is wearable and inexpensive, which uses only one wearable stress monitor sensor and a mobile phone-based application (Android OS) to monitor stress. Secondly, deStress quantitatively assesses the user's stress level continuously, not just classifies the users into stressed or non-stressed state. Thirdly, deStress provides a system for stress monitoring and management, through which the stress data could be recorded, analyzed and shared with medical professionals. Last but not least, a novel adaptive respiration-based bio-feedback approach is implemented to alleviate stress. To the best of our knowledge, deStress is the first telehealth system dedicated to mobile and remote stress monitoring, alleviation and management. Extensive experiment are conducted in 30 persons to demonstrate the feasibility and effectiveness of deStress, and the result shows that the stress level assessment of deStress correctly indicates the mental states of the users, and under the guidance of deStress the users could alleviate their stress level dramatically.

Femtocell refers to a new class of low-power, low-cost base stations (BSs) which can provide better coverage and improved voice/data Quality of Service (QoS). Hybrid access in two-tier macro-femto network is regarded as the most ideal access control mechanism to enhance overall network performance. But the implementation of hybrid access is hindered by a lack of market that can motivate ACcess Permission (ACP) trading between Wireless Service Providers (WSPs) and private femtocell owners. In this paper, we propose a reverse auction framework for fair and efficient ACP transaction. Unlike strict outcome (the demand of bidder must be fully satisfied) in most of the existing works on auction design, the proposed auction model allows range outcome, in which WSP accepts partial demand fulfillment and femtocell owners makes best-effort selling. We first propose a Vickery-Clarke-Grove (VCG) based mechanism to maximize social welfare. As the VCG mechanism is too time-consuming, we further propose an alternative truthful mechanism (referred to as suboptimal mechanism) with acceptable polynomial computational complexity. The simulation results have shown that the suboptimal mechanism generates almost the same social welfare and the cost for WSP as VCG mechanism.

Auction is widely applied in wireless communication for spectrum allocation. Most of prior works have assumed that spectrums are identical. In reality, however, spectrums provided by different owners have distinctive characteristics in both spacial and frequency domains. Spectrum availability also varies in different geo-locations. Furthermore, frequency diversity may cause non-identical conflicts among spectrum buyers since different frequencies have distinct communication ranges. Under such realistic scenario, existing spectrum auction schemes cannot provide truthfulness or efficiency. In this paper, we propose a Truthful double Auction for HEterogeneous Spectrum, called TAHES. TAHES allows buyers to explicitly express their personalized preferences for heterogeneous spectrums and also addresses the problem of interference graph variation. We prove that TAHES has nice economic properties including truthfulness, individual rationality and budget balance.

The promise of high speed (over 1Gbps) wireless transmission rate at the physical layer can be significantly compromised with the current design of 802.11 DCF. There are three overheads in the 802.11 MAC that contribute to the performance degradation: DIFS, random backoff and ACK. Motivated by the recent progress in OFDM and self-interference cancellation technologies, in this paper, we propose a novel MAC design called REPICK (REversed contention and PIggy-backed ACK) to collectively address all the three overheads. The key idea in our proposal is to take advantage of OFDM subcarriers in the frequency domain to enhance the MAC efficiency. In REPICK, we propose a novel reverse contention algorithm, which enables and facilitates receivers to contend channel in the frequency domain (reversed contention). We also design an efficient mechanism which allows ACKs from receivers to be piggy-backed through subcarriers together with the contention information (piggy-backed ACK). We prove through rigorous analysis that the proposed scheme can substantially reduce the overheads associated with 802.11 DCF and a guaranteed throughput gain can be obtained. In addition, results from extensive simulations demonstrate that REPICK can improve the throughput by up to 170%.

The concept of femtocell that operates in licensed spectrum to provide home coverage has attracted interest in the wireless industry due to high spatial reuse, and extensive deployments of femtocells is expected in the future. In this paper, we consider the scenario that a femtocell service provider (FSP) expects to rent spectrum from the coexisting macrocell service provider (MSP) to serve its end users. In addition to the spectrum leasing payment, the FSP may allow hybrid access of macrocell users to improve the utilities of itself and MSP, which are defined as the sum of data traffic and payment/revenue. We propose the spectrum leasing framework taking hybrid access into consideration. The whole procedure is modeled as a three-stage Stackelberg game, where MSP and FSP determine the spectrum leasing ratio, spectrum leasing price and open access ratio sequentially to maximize their utilities, and the existence of the Nash Equilibrium of the sequential game is analyzed. We characterize the equilibrium, in terms of access price, spectrum acquisition of FSP, the open access ratio, and price of anarchy via simulation. Numerical results show that both MSP and FSP can benefit from spectrum leasing, and hybrid access of femtocell can further improve their utilities, which provide sufficient incentive for their cooperation.

It is a well known fact that the quality of sleep is an important factor in health-related quality of life (HRQoL), and people could prevent potential problems by tracking the quality of their sleep. Unfortunately sleep scoring, which is a systematic way to address the sleep staging as well as the scoring of arousals, respiratory, cardiac, and movement events, is usually conducted with specialized equipment which is expensive and operated by specialists in dedicated sleep centers. Related research studies and products (e.g. ZEO) tried to solve this problem, but they either used multiple probes that cause discomfort to the patient, or could not score in real time. In this paper, we design and implement RASS, a portable Real-time Automatic Sleep Scoring system. RASS only requires one probe, which is inexpensive and, as a result, may be used at home or during travel. RASS accurately scores the sleeping state and detects sleep apnea in real-time based on the sensing results of pulse, blood oxygen, activity, sound and light signals. An alarm will be generated when a severely abnormal sleep state is detected. RASS has been tested with 48 patients, and the test results show that RASS could achieve higher than 84% accuracy.

Interference is a critical issue in wireless communications. In a typical multiple-user environment, different users may severely interfere with each other. Coordination among users therefore is an indispensable part for interference management in wireless networks. It is known that coordination among multiple nodes is a costly operation taking a significant amount of valuable communication resource. In this paper, we have an interesting observation that by generating intended patterns, some simultaneous transmissions, i.e., "interference,” can be successfully decoded without degrading the effective throughput in original transmission. As such, an extra and "free” coordination channel can be built. Based on this idea, we propose a DC-MAC to leverage this "free” channel for efficient medium access in a multiple-user wireless network. We theoretically analyze the capacity of this channel under different environments with various modulation schemes. USRP2-based implementation experiments show that compared with the widely adopted CSMA, DC-MAC can improve the channel utilization efficiency by up to 250 percent.

RFID has been gaining popularity due to its variety of applications, such as inventory control and localization. One important issue in RFID system is tag identification. In RFID systems, the tag randomly selects a slot to send a Random Number (RN) packet to contend for identification. Collision happens when multiple tags select the same slot, which makes the RN packet undecodable and thus reduces the channel utilization. In this paper, we redesign the RN pattern to make the collided RNs decodable. By leveraging the collision slots, the system performance can be dramatically enhanced. This novel scheme is called DDC, which is able to directly decode the collisions without exact knowledge of collided RNs. In the DDC scheme, we modify the RN generator in RFID tag and add a collision decoding scheme for RFID reader. We implement DDC in GNU Radio and USRP2 based testbed to verify its feasibility. Both theoretical analysis and testbed experiment show that DDC achieves 40 percent tag read rate gain compared with traditional RFID protocol.

RFID tag identification is a crucial problem in UHF RFID systems. Traditional tag identification algorithms can be classified into two categories, ALOHA-based and tree-based. Both of them are inefficient due to the incidental high coordination cost. In this paper, we bring CSMA into UHF RFID systems to enhance tag read rate by reducing coordination cost. However, it is not straightforward due to the simple hardware design of passive RFID tags, which is unable to sense the transmissions or collisions of other tags. To tackle this challenge, we propose receiver-based CSMA (RCSMA) in this paper. In RCSMA, the reader notifies the tags channel condition. According to different sensing results of reader's notifications, the tags take corresponding actions, e.g., random back off. RCSMA does not require special RFID tag hardware design. An absorbing Markov chain model is presented to analyze the performance of RCSMA and shown to be consistent with the simulation results. Compared with optimized ALOHA-based algorithms and optimized tree-based algorithms, RCSMA can enhance the tag read rate by 30-70 percent under different reader and tag data rates.

Femtocell technology addresses the problem of poor indoor coverage, benefiting both wireless service provider (WSP) and end users. With the introduction of femtocell, the cross-tier interference between macro link and femto link becomes a major factor which greatly impacts the network performance. Different access control approaches, by generating different interference patterns, also severely affect the overall throughput of the network and need to be carefully investigated. Among all the access control mechanisms, hybrid access is the most promising one, which allows roaming unregistered users (referred to as macro users) to access the nearby femto base station (BS) while reserving certain resource for registered home users (referred to as femto users), improving overall network capacity. However, to successfully leverage hybrid access is challenging because the femto holders (FHs) are selfish, unwilling to share their femto facilities and spectrum resource with macro users without any incentive mechanism. In this paper, we propose a novel utility-aware refunding framework to motivate hybrid access in femtocell. Within the framework, both WSP and FHs are assumed to be selfish, and target at maximizing their own utilities. WSP provides certain refunding to motivate FHs to open their resource for macro users. FHs decide the resource allocation among femto and macro users according to the amount of refunding WSP offers. Under this framework, the optimal strategies of both WSP and FHs are analyzed by formulating the problem as a Stackelberg Game. A unique Nash Equilibrium is achieved and a hybrid access protocol is designed according to the analysis. Extensive simulations have been conducted and the results show that the utilities of both WSP and FHs are significantly improved exploiting the hybrid access mechanism.

Auction is widely applied in wireless communication for spectrum allocation. Most of prior works have assumed that all spectrums are identical. In reality, however, spectrums provided by different owners have distinctive characteristics in both spacial and frequency domains. Spectrum availability also varies in different geo-locations. Furthermore, frequency diversity may cause non-identical conflict relationships among spectrum buyers since different frequencies have distinct communication ranges. Under such a scenario, existing spectrum auction schemes cannot provide truthfulness or efficiency. In this paper, we propose a Truthful double Auction mechanism for HEterogeneous Spectrum, called TAHES, which allows buyers to explicitly express their personalized preferences for heterogeneous spectrums and also addresses the problem of interference graph variation. We prove that TAHES has nice economic properties including truthfulness, individual rationality and budget balance. Results from extensive simulation studies demonstrate the truthfulness, effectiveness and efficiency of TAHES.

Diagnosis for configuration troubleshooting in femtocell networks is extremely important for end users and network operators. However, because the small-size femtocell only serves several users, the historical data are very scarce. The data scarcity makes traditional cellular troubleshooting solutions which require a large amount of historical data not applicable. In this paper, we propose a new framework based on transfer learning technology to address the data scarcity so as to enhance the accuracy of the diagnosis model. The proposed framework extracts additional diagnosis knowledge by transferring data information from other femtocells. Based on this framework, we design a Cell-Aware Transfer scheme (CAT), which splits data for each femtocell to further enhance the diagnosis accuracy. Extensive evaluations show that our approach can achieve higher accuracy than traditional methods in self-organizing femtocell network scenarios.

Femtocell technology is regarded as a promising way to deal with poor indoor coverage and increase spectrum spatial reuse. In this paper, we focus on the scenario that macro and femto base stations are deployed by the same Wireless Service Provider (WSP), which treats the revenue maximization as its ultimate target. In such a system, there are several design factors which will affect the overall revenue, including price decision and resource allocation between macrocell and femtocell. In this paper, we propose an economic framework, where users choose either macrocell or femtocell service to optimize their own utility and the monopolistic WSP tries to maximize its revenue via pricing and spectrum allocation strategy. Theoretical results of optimal prices for macrocell and femtocell are given. Extensive theoretical analysis is carried out to determine the spectrum allocation strategy and evaluate the revenue of the WSP. The system capacity and the ratio of macrocell and femtocell users are also discussed. The results have indicated that the revenue of the WSP is significantly improved by combining the pricing strategy and the spectrum allocation strategy.

In this paper, we focus on the dynamic spectrum access of infrastructure-based cognitive radio networks, a primary network and multiple secondary networks, which are collocated with each other. To improve network performance of all networks, we propose a cooperative communication-aware spectrum leasing framework. In the proposed framework, the primary network leverages secondary APs as cooperative relays, and decides the optimal strategy on the relay selection and the price for spectrum leasing. Based on primary network's strategy, secondary networks determine the length of spectrum access time they purchase from the primary network. Finally, each network allocates the total spectrum access time of the network among its end users. The above sequential decision procedure is formulated as a Stackelberg game, with primary network acting as the leader and secondary networks as the followers, and a unique Nash Equilibrium (NE) point is achieved through backward induction analysis. At this NE point, all networks maximize their utilities in terms of transmission rate and revenue/payment. Simulation results show that the primary network and secondary networks achieve higher utilities by exploiting cooperative transmission under our proposed framework, which gives all networks incentive for cooperation.

The promise of high speed (over 1Gbps) wireless transmission rate at the physical layer can be significantly compromised with the current design in 802.11 DCF. There are three overheads in the 802.11 MAC that contribute to the performance degradation: DIFS, random backoff and ACK. Motivated by the current progress in OFDM and self-interference cancellation technologies, in this poster, we propose a novel MAC design called REPICK (REversed contention and PIggy-backed ACK) to collectively address these problems. The key idea in our proposal is to take advantage of OFDM subcarriers in the frequency domain to enhance the MAC efficiency. Specifically, in REPICK, we propose a novel reverse contention algorithm which enables receivers to contend for channel access with subcarriers in the frequency domain (reversed contention). We also design a mechanism which allows ACKs from receivers to be piggy-backed through subcarriers together with the contention information (piggy-backed ACK). We demonstrate REPICK's efficiency through both analysis and simulations.

RFID has been gaining popularity due to its variety of applications, such as inventory control and localization. One important issue in RFID system is tag identification. In RFID systems, the tag randomly selects a slot to send a Random Number (RN16) packet to contend for identification. Collision happens when multiple tags select the same slot, which makes the RN packet undecodable and thus reduces the channel utilization. In this paper, we redesign the RN pattern to make the collided RNs decodable. By leveraging the collision slots, the system performance can be dramatically enhanced. This novel scheme is called DDC, which is able to directly decode the collisions without exact knowledge of collided RNs. In the DDC scheme, we modify the RN generator in RFID tag and add a collision decoding scheme for RFID reader. We implement DDC in GNU Radio and USRP2 based testbed to verify its feasibility. Both theoretical analysis and testbed experiment show that DDC achieves 40% tag read rate gain compared with traditional RFID protocol.

Vehicular Ad-hoc Networks(VANETs) can facilitate many applications such as road safety, intelligent transportation and advertising. These applications usually call for multi-hop data delivery from access points to moving vehicles with user specified delay requirements. However, most existing routing protocols for VANETs only focus on message forwarding from vehicles to access points or take no account of the delay constraint. In this paper, we focus on the development of a carry-and-forward scheme that delivers data from access points to vehicles. Utilizing the vehicle's trajectory obtained from the navigation system, we propose TaDB, a Trajectory-assisted Delay Bounded Message Delivery Algorithm. To choose delivery route within delay constraint while minimizing transmission cost, TaDB uses a Cluster-Aware Link Delay Model to estimate link delay for both the Carry and the Forward strategies on each road segment. TaDB also leverages the vehicle's planned trajectory to estimate its future location. Simulation results show that TaDB can achieve a delivery ratio very close to optimal.

Cooperative communication is a promising technique for future wireless networks, which significantly improves link capacity and reliability by leveraging broadcast nature of wireless medium and exploiting cooperative diversity. However, most of existing works investigate its performance theoretically or by simulation. It has been widely accepted that simulations often fail to faithfully capture many real-world radio signal propagation effects, which can be overcome through developing physical wireless network testbeds. In this work, we build a cooperative testbed based on GNU Radio and Universal Software Radio Peripheral (USRP) platform, which is a promising open-source software-defined radio system. Both single-relay cooperation and multi-relay cooperation can be supported in our testbed. Some key techniques are provided to solve the main challenges during the testbed development: e.g., maximum ratio combine in single-relay transmission and synchronized transmission among multiple relays. Extensive experiments are carried out in the testbed to evaluate performance of various cooperative communication schemes. The results show that cooperative transmission achieves significant performance enhancement in terms of link reliability and end-to-end throughput.

Interference is a critical issue in wireless communications. In a typical multiple-user environment, different users may severely interfere with each other. Coordination among users therefore is an indispensable part for interference management in wireless networks. It is known that, coordination among multiple nodes is a costly operation taking a significant amount of valuable communication resource. In this paper, we have an interesting observation that by generating intended patterns, some simultaneous transmissions, i.e., "interference", can be successfully decoded without degrading the effective throughput in original transmission. As such, an extra and "free" coordination channel can be built. Based on this idea we propose a DC-MAC to leverage this "free" channel for efficient medium access in a multiple-user wireless network. We theoretically analyze the capacity of this channel under different environments with various modulation schemes. USRP2-based implementation experiments show that compared with the widely adopted CSMA, DC-MAC can improve the channel utilization efficiency by up to 250%.

Content Distribution is a key application in an infrastructure-based wireless network. Existing works on this area use either pure broadcast transmission or pure unicast transmission. In this paper, we propose a hybrid broadcast-unicast transmission scheme to improve the efficiency of content distribution for wireless networks with multi-rate support. In the proposed scheme, access point broadcast packets to nodes within its transmission range with a deliberately selected broadcast rate. After that, the point-to-point unicast transmissions are exploited for data forwarding. It mitigates the inherit problems that comes from unicast and broadcast transmission respectively, such as severe contention and large amount of redundant re-transmission. Furthermore, cooperative communication is exploited as a physical layer technique to enhance the throughput of the network. A cooperative communication aware routing is designed accordingly, where the contention relationship between wireless links have been taken into consideration. Simulation results show that the proposed scheme outperforms existing schemes dramatically in terms of network throughput and transmission delay.

Cognitive radio has been proposed in recent years to promote spectrum efficiency by exploiting the existence of spectrum holes. The heterogeneity of both spectrum availability and traffic demand among secondary users has brought significant challenge for efficient spectrum allocation in cognitive radio networks. Observing that spectrum resource can be better matched to traffic demand of secondary users with the help of relay nodes, in this paper we propose to utilize cooperative relays to assist the transmission and improve spectrum efficiency. Different from traditional cooperative communication within a single channel, in our scheme a relay node may be selected for a link to bridge the link's source and destination using its different common channels with those two nodes. With these new logical links composed of both direct link and relay link, new routing protocols are needed so that end-to-end performance can be better improved. Therefore, we define a new link cost, relay- aware link cost, which considers several aspects including channel availability, channel condition, channel utilization and potential relays. Based on this link cost, a relay-assisted routing (RAR) protocol is proposed which includes routing discovery and local adjustment. Simulation results demonstrate the effectiveness of the proposed routing scheme.

Cooperative communication is a new physical layer technique which improves link capacity by exploiting broadcast nature and spatial diversity of wireless channel. The introduction of cooperative communication in wireless networks changes the traditional definition of link and the contention relationship among links. In this paper, we focus on cooperative communication aware routing protocol design in wireless mesh networks, targeting at maximizing the overall end-to-end throughput of the whole network and meanwhile taking contention relationship among multiple links into consideration. We propose a routing metric called contention-aware cooperative metric (CCM) and prove that CCM has the isotonic property. Therefore, efficient algorithms such as Dijkstra or Bellman-Ford can be used to find CCM-based minimum cost paths. Based on CCM, we propose a routing protocol called Contention-aware Cooperative Routing (CCR) which can be implemented in both link-state and distance-vector routing protocols. Extensive simulations are conducted on ns-2 to evaluate the performance of our novel routing metric and routing protocol. The results show that CCR achieves significant throughput gain compared with hop- count-based routing and ETT-based routing. The end-to-end delay is also dramatically reduced under CCR routing.

Cognitive radio has been proposed in recent years to promote the spectrum utilization by exploiting the existence of spectrum holes. The heterogeneity of both spectrum availability and traffic demand in secondary users has brought significant challenge for efficient spectrum allocation in cognitive radio networks. Observing that spectrum resource can be better matched to traffic demand of secondary users with the help of relay node that has rich spectrum resource, in this paper we exploit a new research direction for cognitive radio networks by utilizing cooperative relay to assist the transmission and improve spectrum efficiency. An infrastructure-based secondary network architecture has been proposed to leverage relay-assisted discontiguous OFDM (D-OFDM) for data transmission. In this architecture, relay node will be selected which can bridge the source and the destination using its common channels between those two nodes. With the introduction of cooperative relay, many unique problems should be considered, especially the issue for relay selection and spectrum allocation. We propose a centralized heuristic solution to address the new resource allocation problem. To demonstrate the feasibility and performance of cooperative relay for cognitive radio, a new MAC protocol has been proposed and implemented in a Universal Software Radio Peripheral (USRP)-based testbed. Experimental results show that the throughput of the whole system is greatly increased by exploiting the benefit of cooperative relay.

Recent research has proven that network coding has great potential to improve network throughput in wireless networks. To fully exploit the performance gain brought by network coding, coding-aware routing has been studied to proactively change route of flows for creating more coding opportunities. However, in today's multi-rate wireless networks, coding may not be a wise decision as the lowest rate has to be used for coded information broadcasting, which causes significant resource waste for the high-rate links. In this paper, we propose the idea of cooperative network coding (CNC) to exploit spatial diversity for improving coding opportunity. We provide a theoretical formulation for calculating the maximal throughput of unicast traffic that can be achieved with CNC in multi-rate wireless networks. CNC-aware routing under both Alice-Bob and X-structure are discussed in this paper. The performance evaluation demonstrates that a CNC-aware route selection scheme that leverages cooperative communication to improve coding opportunity leads to higher end-to-end throughput comparing with the coding-oblivious and traditional coding-aware schemes.

With the development of cognitive radio technologies, dynamic spectrum access becomes a promising approach to increase the efficiency of spectrum utilization and solve spectrum scarcity problem. Under dynamic spectrum access, unlicensed wireless users (secondary users) can dynamically access the licensed bands from legacy spectrum holders (primary users) on an opportunistic basis. While most primary users in existing works assume secondary transmissions as negative interference and don't actively involve them into the primary transmission, in this paper, motivated by the idea of cooperative communication, we propose a cooperative cognitive radio framework, where primary users, aware of the existence of secondary users, may select some of them to be the cooperative relay, and in return lease portion of the channel access time to them for their own data transmission. Secondary users cooperating with primary transmissions have the right to decide their payment made for primary user in order to achieve a proportional access time to the wireless media. Both primary and secondary users target at maximizing their utilities in terms of their transmission rate and revenue/payment. This model is formulated as a Stackelberg game and a unique Nash Equilibrium point is achieved in analytical format. Based on the analysis we discuss the condition under which cooperation will increase the performance of the whole system. Both analytical result and numerical result show that the cooperative cognitive radio framework is a promising framework under which the utility of both primary and secondary system are maximized.

In a network supporting cooperative communication, the sender of a transmission is no longer a single node, which causes the concept of a traditional link to be reinvestigated. Thus, the routing scheme basing on the link concept should also be reconsidered to ";truly"; exploit the potential performance gain introduced by cooperative communication. In this paper, we investigate the joint problem of routing selection in network layer and contention avoidance among multiple links in MAC layer for multi-hop wireless networks in a cooperative communication aware network. To the best of our knowledge, it is the first work to investigate the problem of cooperative communication aware routing in multi-source multi-destination multi-hop wireless networks. Several important concepts, including virtual node, virtual link and virtual link based contention graph are introduced. Basing on those concepts, an optimal cooperative routing is achieved and a distributed routing scheme is proposed after some practical approximations. The simulation results show that our scheme reduces the total transmission power comparing with non-cooperative routing and greatly increases the network throughput comparing with single flow cooperative routings.

In this paper, we propose a novel protocol called VC-MAC that utilizes the concept of cooperative communication tailored for vehicular networks, especially for gateway downloading scenarios. VC-MAC leverages the broadcast nature of the wireless medium to maximize the system throughput. The spatial and user diversity are exploited by the concurrent cooperative relaying to overcome the unreliability of the wireless channel in vehicular networks. We theoretically analyze the selection of optimal relay set using weighted independent set (WIS) model, and then design a back-off mechanism to select the concurrent relays in a distributed manner. Extensive simulations in ns-2 are carried out to demonstrate that compared with existing strategies, VC-MAC effectively enhances cooperative information downloading and significantly increases the system throughput.