Eavesdropping poses a critical threat to the confidentiality and integrity of voice communications. In recent years, techniques have advanced beyond traditional microphone-based methods toward more intelligent approaches, such as leveraging millimeter-wave sensing to detect the subtle vibrations induced by speakers and reconstruct voice information without direct audio capture. Despite their technical feasibility, these methods remain constrained by limited working ranges—typically only several meters—rendering them impractical for real-world stealthy eavesdropping. In this work, we propose MoiréEar, the first long-range passive eavesdropping system based on moiré patterns. The key idea is to exploit the amplification capability of moiré patterns, which amplify the minute vibrations induced by acoustic signals by hundreds of times, enabling long-range eavesdropping. To make the proposed method even more practical and stealthy, we develop new theoretical foundations that relax the strict requirements for generating moiré patterns. Specifically, our approach enables the use of irregular stripe structures (e.g., commonly seen barcodes) instead of standard moiré gratings to generate moiré patterns. We implement our design using a low-cost photodiode instead of cameras, achieving real-time eavesdropping with lightweight signal processing. Comprehensive experiments show that the system can extract intelligible audio at a distance of up to 90 m, outperforming the state of the art by an order of magnitude in range. We believe this new eavesdropping modality can inspire a wide range of IoT applications.

To sustain the exponential growth of AI, large-scale multi-node LLM services are increasingly deployed behind Server Load Balancers (SLBs). While these services heavily rely on Remote Direct Memory Access (RDMA) for high-performance communication, natively integrating RDMA with standard SLB architectures introduces severe incompatibilities, such as centralized node bottlenecks, scheduling inconsistencies, and performance degradation during long-distance RDMA transmission. Hence, we propose Draco-SLB, a novel endpoint-side transport shim layer. Draco-SLB transparently shields underlying RDMA execution from network-side complexities, including SLBs and intermediate network middleboxes.

Recently reported attacks against Programmable Logic Controllers (PLCs) have shown that the exploitation of Industrial Control Protocols (ICPs), i.e., ICP-based attacks, poses significant threats to industrial control systems. ICP-based attacks include two essential steps: generating tailored attack payloads and breaking through the session-ID-based PLC defenses. Traditional approaches to performing the two steps rely on laborious manual analysis. To analyze the threats posed by ICP-based attacks to commercial-off-the-shelf PLCs, we propose AIAF, an Automated ICP-based Attack Framework leveraging proprietary binary protocols, which operates automatically through an offline construction of effective attack payloads and an online ICP-based attack test. We have evaluated AIAF with 9 mainstream PLCs, covering 9 protocols, showing that AIAF can reverse engineer 12 kinds of session-ID negotiation (6 value-changed and 6 value-same), generate attack payloads, and execute 35 ICP-based attacks with a 94.29% success rate. Our further Internet-wide evaluation reveals that over 28K PLCs exposed to the Internet are vulnerable to ICP-based attacks.

As semantic communication (SemCom) attracts growing attention as a novel communication paradigm, ensuring the security of transmitted semantic information over open wireless channels has become a critical issue. However, traditional encryption methods often introduce significant additional communication overhead to maintain stability, and conventional learning-based secure SemCom methods typically rely on a channel capacity advantage for the legitimate receiver, which is challenging to guarantee in real-world scenarios. In this paper, we propose a coding-enhanced jamming method that eliminates the need to transmit a secret key by utilizing shared knowledge–potentially part of the training set of the SemCom system–between the legitimate receiver and the transmitter. Specifically, we leverage the shared private knowledge base to generate a set of private digital codebooks in advance using neural network (NN)-based encoders. For each transmission, we encode the transmitted data into digital sequence Y1and associate Y1with a sequence randomly picked from the private codebook, denoted as Y2, through superposition coding. Here, Y1serves as the outer code and Y2as the inner code. By optimizing the power allocation between the inner and outer codes, the legitimate receiver can reconstruct the transmitted data using successive decoding with the index of Y2shared, while the eavesdropper’s decoding performance is severely degraded, potentially to the point of random guessing. Experimental results demonstrate that our method achieves security comparable to state-of-the-art approaches while significantly improving the reconstruction performance of the legitimate receiver by more than 1 dB across varying channel signal-to-noise ratios (SNRs) and compression ratios.

Deep joint source-channel coding (DeepJSCC) has emerged as a promising paradigm for efficient and robust information transmission. However, its intrinsic characteristics also pose new security challenges, notably an increased vulnerability to eavesdropping attacks. Existing studies on defending against eavesdropping attacks in DeepJSCC, while demonstrating certain effectiveness, often incur considerable computational overhead or introduce performance trade-offs that may adversely affect legitimate users. In this paper, we present DeepGuard, to the best of our knowledge, the first physical-layer defense framework for DeepJSCC against eavesdropping attacks, validated through over-the-air experiments using software-defined radios (SDRs). Considering that existing eavesdropping attacks against DeepJSCC are limited to simulation under ideal channels, we take a step further by identifying and implementing four representative types of attacks under various configurations in orthogonal frequency-division multiplexing systems. These attacks are evaluated over-the-air under diverse scenarios, allowing us to comprehensively characterize the real-world threat landscape. To mitigate these threats, DeepGuard introduces a novel preamble perturbation mechanism that modifies the preamble shared only between legitimate transceivers. To realize it, we first conduct a theoretical analysis of the perturbation’s impact on the signals intercepted by the eavesdropper. Building upon this, we develop an end-to-end perturbation optimization algorithm that significantly degrades eavesdropping performance while preserving reliable communication for legitimate users. We prototype DeepGuard using SDRs and conduct extensive over-the-air experiments in practical scenarios. Extensive experiments demonstrate that DeepGuard effectively mitigates eavesdropping threats while preserving reliable communication for legitimate users. In particular, DeepGuard can reduce the eavesdropper’s reconstruction performance by as much as 29 dB in PSNR and decrease classification accuracy by up to 91% compared with the performance achieved by the legitimate user.

Deep learning-based feature matching has showcased great superiority for point cloud registration. While coarse-to-fine matching architectures are prevalent, they typically perform sparse and geometrically inconsistent coarse matching. This forces the subsequent fine matching to rely on computationally expensive optimal transport and hypothesis-and-selection procedures to resolve inconsistencies, leading to inefficiency and poor scalability for large-scale real-time applications. In this paper, we design a consistency-aware spot-guided Transformer (CAST) to enhance the coarse matching by explicitly utilizing geometric consistency via two key sparse attention mechanisms. First, our consistency-aware self-attention selectively computes intra-point-cloud attention to a sparse subset of points with globally consistent correspondences, enabling other points to derive discriminative features through their relationships with these anchors while propagating global consistency for robust correspondence reasoning. Second, our spot-guided cross-attention restricts cross-point-cloud attention to dynamically defined "spots"-the union of correspondence neighborhoods of a query's neighbors in the other point cloud, which are most likely to cover the true correspondence of the query ensured by local consistency, eliminating interference from similar but irrelevant regions. Furthermore, we design a lightweight local attention-based fine matching module to precisely predict dense correspondences and estimate the transformation. Extensive experiments on both outdoor LiDAR datasets and indoor RGB-D camera datasets demonstrate that our method achieves state-of-the-art accuracy, efficiency, and robustness. Besides, our method showcases superior generalization ability on our newly constructed challenging relocalization and loop closing benchmarks in unseen domains.

Recent semi-dense image matching methods have achieved remarkable success, but two long-standing issues still impair their performance. At the coarse stage, the over-exclusion issue of their mutual nearest neighbor (MNN) matching layer makes them struggle to handle cases with scale difference between images. To this end, we comprehensively revisit the matching mechanism and make a key observation that the hint concealed in the score matrix can be exploited to indicate the scale ratio. Based on this, we propose a scale-aware matching module which is exceptionally effective but introduces negligible overhead. At the fine stage, we point out that existing methods neglect the local consistency of final matches, which undermines their robustness. To this end, rather than independently predicting the correspondence for each source pixel, we reformulate the fine stage as a cascaded flow refinement problem and introduce a novel gradient loss to encourage local consistency of the flow field. Extensive experiments demonstrate that our novel matching pipeline, with these proposed modifications, achieves robust and accurate matching performance on downstream tasks.

Deep reinforcement learning (DRL) has been implemented across various critical applications, including smart grids, trac management systems, and autonomous vehicles. To safeguard intellectual property and mitigate security vulnerabilities, access to DRL models is typically restricted to a black-box format. is means specic details like the structure of the policy network and optimization processes are not openly available to users. It is crucial to determine if the hyper-parameters can be inferred from observable states and actions within these models, presenting two primary challenges: 1) limited data available from the black-box model and 2) the intertwined eects of hyperparameters on the model's behavior. Since DRL models exhibit varying behaviors in identical tasks depending on their hyper-parameter congurations, we introduce a novel hyper-parameter inference attack against DRL, named HyperInfer, which allows adversaries to deduce the settings of a black-box DRL model. In order to fully assess the risk of model hyper-parameter leakage, we design two novel state generation methods that provoke divergent responses from DRL models. We also develop an inference framework to elucidate the relationship between model behavior and hyper-parameter settings. rough comprehensive experiments involving multiple DRL models and environments, we demonstrate that model behaviors can indeed reveal hyper-parameter settings, with inference accuracy surpassing 90% in scenarios such as PPO with CartPole. We also discuss keyndings relevant to practical applications and explore how knowledge of hyperparameters can facilitate more sophisticated attacks. Lastly, we propose potential defensive strategies to minimize the risk of hyper-parameter leakage in DRL models.

Machine unlearning is the primary way to fight for the “right to be forgotten” in machine learning field, which is promoted among multiple privacy legislations, such as GDPR and CCPA. However, the latest work has shown that machine unlearning in deep learning cannot be easily verified, making it challenging for the data owners to be convinced that their data has indeed been deleted as claimed. This is especially problematic for federated learning (FL), where a number of participants jointly train a global model while each participant should be free to join and leave the federation as they wish. However,the lack of a reliable approach to verify unlearning in FL will no doubt discourage certain users from joining the federation.In this work, we propose A2E, a black-box watermarking scheme from a leaving participant's perspective to realize verifiable federated unlearning which incurs minimum impact and no security threats to vanilla FL. The key idea is to leverage adversarial training to inject the anti-adversarial example (A2E) characteristic into the uploaded model updates of the last contribution round as the watermark of the leaving participant. Then, we verify whether the server has indeed executed the effective unlearning, with the newly developed probabilistic quantification of unlearning confidence, by checking the unlearned global model's resistance to the specially generated watermark-dependent adversarial examples of the leaver. We conducted large-scale experiments on various popular datasets (including natural images, medical images, and speech) and model structures (including LeNet, ResNet, VGG, and LSTM). The results confirm the effectiveness of A2E in verifying federated unlearning with a high confidence. We also show that A2E is robust against multiple adaptive strategies from the adversarial server and participants.

Programmable logic controllers (PLCs) play a crucial role in various industrial manufacturing processes. Recent attack events show that attackers have a strong interest in controller variables of PLCs, including the device status and internal program logic. Detecting anomalous messages targeting PLC controller variables, which relies on the analysis of controller variable semantics, has proven to be an effective method for identifying such attacks. However, the proprietary nature of industrial control protocols (ICPs) poses a challenge to extracting the required semantics. In this paper, we propose an automated framework namedSePannerto extract the semantics of controller variables from proprietary ICPs based on network traffic. Specifically, we first collect multiple groups of interaction traffic of PLCs and perform the starting-aligned comparisons on them to locate the semantic fields directly. Then, we identify and investigate a new problem in semantic extraction — interference resulting from misordered messages — and propose a set of filtering criteria to eliminate it effectively. We evaluate SePanner using the S7COMM protocol, and the results indicate that SePanner can successfully extract the semantics of controller variables with 100% accuracy. Additionally, we employ SePanner to analyze 7 proprietary ICPs, successfully extracting the semantics of 63 controller variables and their 134 states. Additionally, we demonstrate the extensive applications of SePanner in multiple ICS security scenarios and present its better performance compared with existing ICP semantic analyzing tools.

Different from traditional object detectors such as YOLO, Detection Transformers (DETR) have reshaped the landscape of object detection by replacing heuristic-driven components like Non-Maximal Suppression with a fully end-to-end framework based on one-to-one Hungarian matching. While the majority of research has focused on improving the slow training convergence of DETR, this work investigates their security from an adversarial perspective. We unveil a critical vulnerability stemming directly from DETR’s core design: the deterministic one-to-one mapping between object queries and ground-truth objects can be exploited. This allows an adversary to craft perturbations that selectively manipulate specific target objects – causing them to vanish or be misclassified – while preserving the detection integrity of all other objects in the scene. Our initial analysis reveals that conventional gradient-based attacks are ill-suited for this task, as they induce unintended interference on non-target instances, a phenomenon we term as the “spillover effect”. To overcome this, we re-formulate the attack optimization by incorporating a novel penalty term that explicitly decouples the adversarial influence on target and non-target objects. Furthermore, we provide theoretical analysis to derive perturbation bounds under which the optimal matching assignments remain invariant, offering deeper insights into the model’s stability. Extensive experiments on standard benchmarks demonstrate that our proposed attack significantly improves the success rate and convergence speed while inducing far fewer feature-level artifacts, making the attack both more effective and stealthier.

Referring Video Object Segmentation (RVOS) aims to segment the target objects specified in human instructions. Previous approaches typically rely on explicit human instructions that contain target categories or salient appearance descriptions. These approaches tend to fail when the instructions require temporal video understanding and complex relational reasoning. In this work, we present RViSeg, a reasoning-centric video object segmentation model that leverages the reasoning capability of Multi-modal Large Language Models (MLLM) to handle complex queries. The primary challenge lies in enabling MLLM to perform efficient pixel-level video perception. To tackle this challenge, we introduce a novel Spatial Token Merge (STM) module that consolidates lengthy video tokens into compact region-level clusters, while preserving essential spatial details. This structured representation enables MLLM to infer user intention by interleaving spatial and temporal visual information. Furthermore, we propose a Query-based Target Retrieval (QTR) module that utilizes learnable tokens as the target identity for mask prediction. By propagating these instance-specific tokens both intra-clip and inter-clip, our RViSeg effectively encodes object motion, ensuring spatio-temporal consistency in segmentation results. To facilitate training and evaluation, we construct InstructVideo, a single- and multiple-object reasoning video segmentation benchmark. Comprehensive experiments demonstrate the effectiveness of the proposed components.

Wind speed monitoring is crucial for environmental management and forecasting. However, current solutions often struggle with high power consumption, especially at the end device, which typically has a sensor and wireless radios with limited battery capacity. To this end, we present WindScatter, an ultra-low-power, long-range, and large-scale wind speed monitoring system. WindScatter adopts the Integrated Sensing and Communication (ISAC) paradigm to enable low-power operation. It reuses the sensed data for communication by leveraging a TMR (Tunnel Magneto-Resistance) switch sensor to measure the wind speed information and control the backscatter communication simultaneously, thus avoiding the need for analog-to-digital conversion and a microcontroller for communication control. Our hardware-software co-design enables accurate measurements and stable concurrent transmission. We implement WindScatter and conduct extensive experiments and case studies to evaluate its performance. Results show that WindScatter supports measurements of all wind speed levels on the Extended Beaufort scale, from 1.5 m/s to 60 m/s, with an average error rate of 0.78%. WindScatter can sense and transmit wind speed data at a distance of 800 m with a power consumption of 136.5$\mu$W. Compared with commodity devices, WindScatter achieves comparable measurement range and accuracy while reducing cost by$91.5\times$and power consumption by$8,791\times$.

Electrode coating measurement is a crucial task in practical lithium-ion battery systems, where the thickness and refractive index of the electrode coating directly reflect the battery's quality, energy density, capacity, and lifespan. In this paper, we propose the design, implementation, and evaluation of BatTera, the practical system for an accurate, high-resolution, non-destructive, and safe electrode coating measurement, with the ability to simultaneously measure coating thickness and refractive index. BatTera's contributions are twofold. Firstly, we build a comprehensive mathematical model that characterizes the arrival time of echo signals from both sides of the electrode coating by thoroughly analyzing the electrode structure based on “coating-foil-coating”. This model serves as a theoretical foundation guiding the measurement of coating thickness and refractive index. Secondly, we propose a series of effective signal-processing algorithms to address the practical challenges of double-side coating misalignment and deformation interference, thus adaptive improving the signal-to-noise ratio of Terahertz signals and pushing BatTera one big step closer to real adoptions. We implement BatTera based on the commercial Terahertz device QT-TO1000 and conduct extensive experiments using five types of cathode electrode samples in three different sizes, collected from one of the world's largest new energy battery manufacturers. The results show that BatTera achieves high measurement accuracy with a mean average error of 6.106$\upmu$m for thickness and 0.230 for refractive index.

Carrier interference is a fundamental research challenge in backscatter systems. Existing solutions leverage frequency shifting or full duplex designs to mitigate carrier interference in the analog or digital domain. However, these solutions introduce extra spectrum usage, protocol overhead, and power consumption, all of which are undesirable in backscatter systems. In this paper, we revisit polarization mismatch and propose Lotus, a low-cost analog design to combat carrier interference for backscatter systems. Lotus comprises a novel antenna design and a backscatter tag design. At runtime, Lotus antenna replaces the receiver default antenna to cancel out the carrier interference, without any protocol or hardware overhead. Lotus tag mitigates the power loss caused by polarization mismatch and remains compatible with all existing backscatter radios. Experimental results show that Lotus achieves comparable cancellation gain (i.e., 42 dB) with the state-of-the-art frequency-shifting baseline. Meanwhile, Lotus outperforms the baseline by 2× and 6.5× in spectrum and power efficiency, respectively. Additionally, Lotus achieves comparable performance to the frequency shifting baseline regarding backscatter range and throughput across three backscatter technologies, including Wi-Fi, Bluetooth, and LoRa.

As data centers continue to expand in size and complexity, obtaining global traffic insights necessitates aggregating statistical data from numerous individual nodes, a process critical for effective network management. However, in data centers, existing approaches often rely on querying individual endpoint hosts to gather cluster-wide statistics, which introduces substantial latency and reduces efficiency, particularly in large-scale deployments. To address this issue, we propose HiMon, a cost-efficient and high-accurate distributed monitoring system for optimizing traffic aggregation. HiMon enables distributed nodes to perform real-time, flow-level statistical processing and report the data to a master node with minimal bandwidth consumption. The master node aggregates the collected data to construct a comprehensive global traffic view. To enable high-speed and high-precision perpacket processing on child nodes, we introduce MaxSketch. MaxSketch’s data structure and update strategy allow it to accurately estimate child node traffic with minimal memory and computational overhead. For high-speed aggregation on the master node, we present PolySketch, which significantly boosts aggregation efficiency by delegating most computational tasks to the child nodes. Together, the hierarchical sketch structures of MaxSketch and PolySketch form the HiMon monitoring system. Experimental evaluations demonstrate that HiMon surpasses baseline algorithms, achieving a 17-210× improvement in traffic processing efficiency, a 25-42× reduction in master node bandwidth consumption, and a 3.69-8.97× increase in accuracy.

In recent years, there has been a growing focus on developing Low Power Wide Area Network (LPWAN) protocols, especially within the LoRa research community. However, the research community for NB-IoT, another crucial LPWAN technology, has not experienced comparable expansion due to the absence of a functional and adaptable software-defined radio (SDR) implementation. To address this gap, we present SoftNB, the first fully functional physical layer SDR implementation for NB-IoT. SoftNB conforms to the latest 3GPP standards and features an efficient and effective signal processing pipeline to mitigate time, frequency, and phase offsets during transmission and reception. Additionally, SoftNB is compatible with various SDR platforms, including USRP, HackRF One, and RTL-SDR Dongle. Extensive evaluations of SoftNB demonstrate its superior performance. Compared to the state-of-the-art baseline, SoftNB achieves an 8× reduction in Block Error Rate (BLER) when the number of repetitions is set to 4 at a distance of 450 meters.

Robotic dexterous manipulation is a challenging problem due to high degrees of freedom (DoFs) and complex contacts of multi-fingered robotic hands. Many existing deep reinforcement learning (DRL) based methods aim at improving sample efficiency in high-dimensional output action spaces. However, existing works often overlook the role of representations in achieving generalization of a manipulation policy in the complex input space during the hand-object interaction. In this paper, we propose DexRep, a novel hand-object interaction representation to capture object surface features and spatial relations between hands and objects for dexterous manipulation skill learning. Based on DexRep, policies are learned for three dexterous manipulation tasks, i.e. grasping, in-hand reorientation, bimanual handover, and extensive experiments are conducted to verify the effectiveness. In simulation, for grasping, the policy learned with 40 objects achieves a success rate of 87.9% on more than 5000 unseen objects of diverse categories, significantly surpassing existing work trained with thousands of objects; for the in-hand reorientation and handover tasks, the policies also boost the success rates and other metrics of existing hand-object representations by 20% to 40%. The grasp policies with DexRep are deployed to the real world under multi-camera and single-camera setups and demonstrate a small sim-to-real gap.

Sensing-assisted beamforming techniques, with the aid of multimodal fusion perception, ensure highly reliable beam selection for V2I communication. However, due to the frequent communication path updates in high-mobility scenarios and the limited computing resources of base stations, the high-burden multimodal fusion computation make communication delays unavoidable. In this paper, we propose BeMamba, a novel multimodal fusion framework based on state space model for beamforming to balance the reliability and low latency of communication. Benefiting from the hidden state’s efficient sequence modeling ability with linear computational complexity, we designTime Sequence MambaandModal Sequence Mambato achieve intra-modal temporal fusion and cross-modal feature fusion. In addition, we develop dedicated data pre-processing methods as well as modality-specific feature extractors for the accessible modalities: image, LiDAR, radar, and GPS. On the DeepSense6G benchmark, our method achieves a 5.16% improvement in beam prediction accuracy, a 77.88% reduction in computational load, and a 4.56 times increase in inference speed.

Most of the federated learning techniques are limited to homogeneous model fusion. With the rapid growth of smart applications on resource-constrained edge devices, it becomes a barrier to accommodate their heterogeneous computing power and memory in the real world. Federated Distillation is a promising alternative to enable aggregation from heterogeneous models. However, the effectiveness of knowledge transfer still remains elusive under the shadow of distinct representation power from heterogeneous models. In this paper, we approach from an adversarial perspective to characterize the decision boundaries during distillation. By leveraging K-step PGD attacks, we successfully model the dynamics of the closest boundary points and establish a quantitative connection between the predictive uncertainty and boundary margin. Based on these findings, we further propose a new loss function to make the distillation attend to samples close to the decision boundaries, thus learning from more informed logit distributions. The extensive experiments over CIFAR-10/100 and Tiny-ImageNet demonstrate about 0.5-3.5% improvement of accuracy under different IID and non-IID settings, with only a small increment of computational overhead.

This work aims to reconstruct the 3D geometry of a rigid object manipulated by one or both hands using monocular RGB video. Previous methods rely on Structure-from-Motion or hand priors to estimate relative motion between the object and camera, which typically assume textured objects or single-hand interactions. To accurately recover object geometry in dynamic interactions, we incorporate priors from 3D generation model into object pose estimation and propose semantic consistency constraints to solve the challenge of shape and texture discrepancy between the generated priors and observations. The poses are initialized, followed by joint optimization of the object poses and implicit neural representation. During optimization, a novel pose outlier voting strategy with inter-view consistency is proposed to correct large pose errors. Experiments on three datasets demonstrate that our method significantly outperforms the state-of-the-art in reconstruction quality for both single- and two-hand scenarios. Our project page: https://east-j.github.io/dynhor/

Object detection is a fundamental enabler for many real-time downstream applications such as autonomous driving, augmented reality and supply chain management. However, the algorithmic backbone of neural networks is brittle to imperceptible perturbations in the system inputs, which were generally known as misclassifying attacks. By targeting the real-time processing capability, a new class of latency attacks has been reported recently. They exploit new attack surfaces in object detectors by creating a computational bottleneck in the post-processing module, which leads to cascading failure and puts the real-time downstream tasks at risk. In this work, we take an initial attempt to defend against this attack via background-attentive adversarial training that is also cognizant of the underlying hardware capabilities. We first draw system-level connections between latency attacks and hardware capacity across heterogeneous GPU devices. Based on the particular adversarial behaviors, we utilize objectness loss as a proxy and build background attention into the adversarial training pipeline, and achieve a favorable balance between clean and robust accuracy. The extensive experiments demonstrate the effectiveness of the defense in restoring real-time processing capability from 13 FPS to 43 FPS on Jetson Orin NX, with a better trade-off between the clean and robust accuracy. The source code is available at: https://github.com/Hill-Wu1998/underload.

Vision and touch are the most commonly used senses in human manipulation. While leveraging human manipulation videos for robotic task pretraining has shown promise in prior works, it is limited to image and language modalities and deployment to simple parallel grippers. In this paper, aiming to address the limitations, we collect a vision-tactile dataset by humans manipulating 10 daily tasks and 182 objects. In contrast with the existing datasets, our dataset is the first visual-tactile dataset for complex robotic manipulation skill learning. Also, we introduce a novel benchmark, featuring six complex dexterous manipulation tasks and a reinforcement learning-based vision-tactile skill learning framework. 18 non-pretraining and pretraining methods within the framework are designed and compared to investigate the effectiveness of different modalities and pertaining strategies. Key findings based on our benchmark results and analyses experiments include: 1) Despite the tactile modality used in our experiments being binary and sparse, including it directly in the policy training boosts the success rate by about 20\% and joint pretraining it with vision gains a further 20\%. 2) Joint pretraining visual-tactile modalities exhibits strong adaptability in unknown tasks and achieves robust performance among all tasks. 3) Using binary tactile signals with vision is robust to viewpoint setting, tactile noise, and the binarization threshold, which facilitates to the visual-tactile policy to be deployed in reality. The dataset and benchmark are available at \url{https://github.com/LQTS/VTDexManip}.

Visual and tactile pretraining have been extensively studied in dexterous robot manipulation tasks. However, existing methods typically require the simultaneous acquisition of visual and tactile data, making it difficult to utilize low-cost, unpaired visual-tactile datasets. Moreover, these methods often rely on tactile sensors to provide input data for reinforcement learning (RL) during the physical deployment of robotic dexterous hands, which highly increases deployment costs. To address these challenges, we propose UpViTaL, an unpaired visualtactile self-supervised representation learning method for RLbased robot dexterous manipulation. Specifically, we collect low-cost unpaired visual and tactile datasets for manipulation skill learning using a camera and tactile gloves on three robot manipulation tasks. The temporal tactile self-supervised representation learning module of UpViTaL is used to explore efficient tactile representations from time-series tactile data. In parallel, the visual pretraining module of UpViTaL helps to extract efficient visual representations from visual data. In addition, we fuse unpaired visual-tactile representations through an RL reward mechanism, which does not require robotic dexterous hands tactile sensors for practical deployment. We validate our approach on three dexterous robot manipulation tasks. Experimental results demonstrate that UpViTaL can efficiently learn robot manipulation skills. Compared to existing approaches for visual pretraining, our method significantly improves the success rate by more than 30%.

In robotic navigation, maintaining precise pose estimation and navigation in complex and dynamic environments is crucial. However, environmental challenges such as smoke, tunnels, and adverse weather can significantly degrade the performance of single-sensor systems like LiDAR or GPS, compromising the overall stability and safety of autonomous robots. To address these challenges, we propose AF-RLIO: an adaptive fusion approach that integrates 4D millimeterwave radar, LiDAR, inertial measurement unit (IMU), and GPS to leverage the complementary strengths of these sensors for robust odometry estimation in complex environments. Our method consists of three key modules. Firstly, the pre-processing module utilizes radar data to assist LiDAR in removing dynamic points and determining when environmental conditions are degraded for LiDAR. Secondly, the dynamic-aware multimodal odometry selects appropriate point cloud data for scan-tomap matching and tightly couples it with the IMU using the Iterative Error State Kalman Filter. Lastly, the factor graph optimization module balances weights between odometry and GPS data, constructing a pose graph for optimization. The proposed approach has been evaluated on datasets and tested in real-world robotic environments, demonstrating its effectiveness and advantages over existing methods in challenging conditions such as smoke and tunnels. Furthermore, we open source our code at https://github.com/NeSC-IV/AF-RLIO.git to benefit the research community.

Recent innovations in autonomous drones have facilitated time-optimal flight in single-drone configurations, and enhanced maneuverability in multi-drone systems by applying optimal control and learning-based methods. However, few studies have achieved time-optimal motion planning for multi-drone systems, particularly during highly agile maneuvers or in dynamic scenarios. This paper presents a decentralized policy network using multi-agent reinforcement learning for time-optimal multi-drone flight. To strike a balance between flight efficiency and collision avoidance, we introduce a soft collision-free mechanism inspired by optimization-based methods. By customizing PPO in a centralized training, decentralized execution (CTDE) fashion, we unlock higher efficiency and stability in training while ensuring lightweight implementation. Extensive simulations show that, despite slight performance tradeoffs compared to single-drone systems, our multi-drone approach maintains near-time-optimal performance with a low collision rate. Real-world experiments validate our method, with two quadrotors using the same network as in simulation achieving a maximum speed of 13.65 m/s and a maximum body rate of 13.4 rad/s in a 5.5 m × 5.5 m × 2.0 m space across various tracks, relying entirely on onboard computation [video33https://youtu.be/KACuFMtGGpo][code44https://github.com/KafuuChikai/Dashing-for-the-Golden-Snitch-Multi-Drone-RL].

Autonomous high-speed flight in unknown, clut-tered environments is essential for a variety of quadrotor applications, such as inspection, search, and rescue. In this study, we propose a novel trajectory planner designed to achieve efficient, high-speed, collision-free flights in such environments. The proposed approach begins by generating a safe flight corridor based on the path found by Lazy Theta*, representing the safe regions with polytopic sets. These sets are then used to define discrete-time control barrier function (DCBF), ensuring the quadrotor stays within safe bounds during flight. By selecting a single waypoint ahead of the quadrotor on the path as the next waypoint, the trajectory is optimized by considering both the total flight time and safety constraints. Extensive simulations and real-world experiments have confirmed our method's feasibility, demonstrating its capability for high-speed performance and reliable obstacle avoidance. [video44https://www.youtube.com/playlist?list=PLJFduoH7QICOhcIX3JFsZwB4IgS4_-sPt]

The flying speed of autonomous quadrotors has increased significantly in the field of autonomous drone racing. However, most research primarily focuses on the aggressive flight of a single quadrotor, simplifying the racing gate traversal problem to a waypoint passing problem that neglects the orientations of the racing gates or implicitly considers the waypoint direction during path planning. In this paper, we propose a systematic method called Pairwise Model Predictive Control (PMPC) that can guide two quadrotors online to navigate racing gates with minimal time and without collisions. The flight task is initially simplified as a point-mass model waypoint passing problem to provide time optimal reference through an efficient two-step velocity search method. Subsequently, we utilize the spatial configuration of the racing track to compute the optimal heading at each gate, maximizing the visibility of subsequent gates for the quadrotors. To address varying gate orientations, we introduce a novel Magnetic Induction Line-based spatial curve to guide the quadrotors through racing gates of different orientations. Furthermore, we formulate a nonlinear optimization problem that uses the point-mass trajectory as initial values and references to enhance solving efficiency. The feasibility of the proposed method is validated through both simulation and real-world experiments. In real-world tests, the two quadrotors achieved a top speed of$6.1m/s$on a 7-waypoint racing track within a compact flying arena of$5m\times 4m\times 2m$.

We aim to develop a general multi-agent reinforcement learning (MARL) policy that enables a group of robots to efficiently explore large-scale, unknown environments with random pose initialization. Existing MARL-based multi-robot exploration methods face challenges in reliably mapping observations to actions in large-scale scenarios and lack of zero-shot generalization to unknown environments. To this end, we propose a generic multi-task pre-training algorithm (termed TaskExp) to enhance the generalization of learning-based policies. In particular, we design a decision-related task to guide the policy to focus on valuable subspaces of the action space, improving the reliability of policy mapping. Moreover, two perception-related tasks-Location Estimation and Map Prediction-are designed to enhance the zero-shot capability of the policy by guiding it to extract general invariant features from unknown environments. With TaskExp pre-training, our policy significantly outperforms state-of-the-art planning-based methods in large-scale scenarios and demonstrates strong zero-shot performance in unseen environments. Furthermore, TaskExp can also be easily integrated to improve the existing learning-based multi-robot exploration methods.

Supervised behavioral cloning using robot visual-action data has been widely investigated in robot manipulation. However, these methods typically require simultaneous acquisition of visual and action data, which makes them difficult to utilize unpaired visual-action datasets: e.g. videos on Internet or action only data which has less privacy and security concerns. To take advantage of the action data without synchronized visual observation, we propose UnVALe, a novel dexterous robotic manipulation RL framework that utilizes action data without paired images to learn priors of human dexterous manipulation skills. Specifically, an LSTM-based network is designed to learn the temporal action prior by reconstructing the input trajectories, and a VAE network is designed to learn the spatial action prior by reconstructing the input action. Novel rewards are proposed to incorporate the priors into reinforcement learning, which encourages action output from RL polices to maintain low reconstruction errors in the LSTM and VAE networks. We perform extensive validation on three dexterous robot manipulation tasks. The experimental results show that UnVALe can effectively improve robot manipulation performance. Compared with existing visual pretraining methods, our method achieves a more than 30% increase in success rates.

Multi-robot exploration in unknown environments is a fundamental task for multi-robot systems, which requires the coordination of the robots to avoid collisions and conflicts while performing task allocation. Existing exploration strategies improve the efficiency of multi-robot exploration by modeling the multi-robot task allocation problem as a variant of the multiple traveling salesman problem. However, this is computationally intensive and difficult to deploy on physical platforms. Hence, this paper develops a hybrid strategy for range-sensing multi-robot exploration with effective team coordination, enabling a larger team dispersion degree and higher exploration efficiency. In addition, we present a novel multi-robot exploration point detection method suitable for narrow and dynamic environments, effectively reducing exploration failure and incompleteness. The Gazebo simulations demonstrate better exploration efficiency and the least time cost of our exploration framework compared with state-of-the-art methods, and real-world experiments also validate the effectiveness. The code is released at https://github.com/NeSC-IV/DHC_ME.

Robotic and autonomous driving platforms necessitate efficient 3D Multi-Object Tracking (MOT) that harmonizes geometric precision, motion robustness, and computational efficiency. Traditional 3D MOT approaches face critical challenges: geometric similarity metrics (e.g., IoU-based) degrade at long ranges with high computational costs, while distance-based methods fail to capture object orientation and shape; the effects of occlusion and the intricate relative ego-object motion degrade tracking performance in dynamic scenes. To this end, we propose PB-MOT, an online framework integrating two key innovations: ego-motion-compensated state estimation that decouples dynamic interactions; and a rotated ellipse association algorithm unifying pose and shape-aware matching with adaptive distance constraints. Evaluations on the KITTI benchmark show that our PB-MOT achieves state-of-the-art performance with a HOTA score of 81.94%, while running at an impressive 2,402.76 FPS on CPU. This enables real-time, high-fidelity perception and tracking for resource-constrained robotic systems.

Bimanual dexterous manipulation remains a significant challenge in robotics due to the high DoFs of each hand and their coordination. Existing single-hand manipulation techniques often leverage human demonstrations to guide RL methods but fail to generalize to complex bimanual tasks involving multiple sub-skills. In this paper, we propose VTAO-BiManip, a novel framework that integrates visual-tactile-action pre-training with object understanding, aiming to enable human-like bimanual manipulation via curriculum reinforcement learning (RL). We improve prior learning by incorporating hand motion data, providing more effective guidance for dual-hand coordination. Our pretraining model predicts future actions as well as object pose and size using masked multimodal inputs, facilitating cross-modal regularization. To address the multi-skill learning challenge, we introduce a two-stage curriculum RL approach to stabilize training. We evaluate our method on a bimanual bottle-cap twisting task, demonstrating its effectiveness in both simulated and real-world environments. Our approach achieves a success rate that surpasses existing visual-tactile pretraining methods by over 20%.

Mobile manipulators integrate the locomotion flexibility of quadruped robots with the operational capabilities of robotic manipulators. This integrated system is particularly effective for teleoperating explosive ordnance disposal (EOD) tasks in hazardous environments, enabling the safe handling of explosive devices. However, when the quadruped operates in narrow corridors or cluttered spaces, its ability to reposition is limited. This limitation, combined with targets located laterally relative to the robot, poses critical challenges for achieving rapid and intuitive teleoperation of the manipulator. Existing manipulator mapping methods either fail to support lateral teleoperation or lack proper coordinate transformations, leading to mismatches between the intended and actual movement directions of the leader and follower devices. This reduces operational intuitiveness and increases the cognitive load on human operators. To overcome these issues, we propose a hybrid mapping method that combines joint-space velocity control with Cartesian-space control. This method leverages joint-space velocity commands for rapid manipulator reorientation, while employing Cartesian-space commands to achieve precise end-effector teleoperation. Furthermore, we introduce a virtual base coordinate frame that adaptively adjusts in response to the manipulator’s reorientation. This adaptive compensation ensures that the visual feedback from the camera mounted on the end-effector remains consistent and intuitive. The proposed method was validated through experiments on a quadruped robot equipped with a manipulator in an EOD scenario. Results demonstrated significant improvements, including 100% success rate, 43.9% task duration reduction, and 31.7% NASA-TLX score decrease, indicating decreased cognitive load and enhanced task efficiency compared to baseline methods.

Over the past decade, there has been a remarkable surge in utilizing quadrotors for various purposes due to their simple structure and aggressive maneuverability. One of the key challenges is online time-optimal trajectory generation and control technique. This paper proposes an imitation learning-based online solution to efficiently navigate the quadrotor through multiple waypoints with near-time-optimal performance. The neural networks (WN&CNets) are trained to learn the control law from the dataset generated by the time-consuming CPC algorithm and then deployed to generate the optimal control commands online to guide the quadrotors. To address the challenge of limited training data and the hover maneuver at the final waypoint, we propose a transition phase strategy that utilizes MINCO trajectories to help the quadrotor ‘jump over’ the stop-and-go maneuver when switching waypoints. Our method is demonstrated in both simulation and real-world experiments, achieving a maximum speed of 5.6m/s while navigating through 7 waypoints in a confined space of 5.5m × 5.5m × 2.0m [video3]. The results show that with a slight loss in optimality, the WN&CNets significantly reduce the processing time and enable online control for multi-point flight tasks.

Last-mile Delivery Parcel Volume (LDPV) quantifies the number of parcels destined for a specific region, particularly a manually divided Area-Of-Interest (AOI). Accurate prediction of AOI-level LDPV is crucial for the efficient management of logistics resources. However, the straightforward adaptation of existing prediction models often falls short, primarily due to (I) a lack of consideration for the intuition behind AOI divisions, and (II) a reliance solely on fully observed historical data, which may not inform future trends. To overcome the above pitfalls, leveraging rich AOI data and advanced parcel travel time estimation services in JD Logistics, this paper introduces a novel framework called Dual-view Prediction Networks (DualPNs). It combines a Vector-Quantified AutoEncoder (VQ-AE) and a Template-Augmented Zero-Inflated Poisson (TA-ZIP), enabling both point and probabilistic distribution predictions of AOI-level LDPV. Specifically, VQ-AE utilizes a vector quantization technique to distill a large number of AOIs into representative templates, thereby addressing the first pitfall. Subsequently, TA-ZIP dynamically integrates fully observed and lookahead features, aligning them with template-specific decoders to parameterize the probabilistic distributions, thus resolving the second pitfall. We conduct extensive experiments in two cities, comprising over 47,000 and 126,000 AOIs respectively, to demonstrate the superiority of our DualPNs over other baselines. Moreover, a real-world case study highlights the effectiveness of DualPNs for enhancing downstream courier allocation by yielding an average improvement of 1.51% in the on-time delivery rate.

Large language models (LLMs) have emerged as a cornerstone for advancing AI technologies. It revolutionizes the way we interact with devices, websites, and information, and paves the way for the development of highly intuitive and capable virtual assistants. Training of today's LLMs happens in cloud data centers due to the requirement of enormous data and a significant amount of computing power. Despite extensive research in mobile edge computing, fine-tuning pre-trained LLMs using resource-constrained devices like commodity smartphones remains highly under-explored. In this paper, we propose Confidant, a practical collaborative training framework that allows modern LLMs to be fine-tuned across multiple off-the-shelf mobile devices. To this end, Confidant partitions an LLM into several sub-models, allowing each of them to fit in the memory of a mobile device. Multiple mobile devices then collaborate to train the LLM by employing a novel pipeline parallel training approach. In specific, Confidant encompasses a memory-aware dynamic model partitioning and intra-device multi-processor scheduler to minimize the training time across heterogeneous platforms. To ensure resilient distributed training, a hybrid fault tolerance mechanism is devised to proactively manage potential device and network failures. We fully implemented Confidant in C++/Python, and built a cross-framework adapter, enabling collaborative training on a variety of mobile platforms. Experimental results show that Confidant excels in achieving computation-, memory-efficient, and robust customization of LLMs - it manages to train state-of-the-art billion-sized LLMs including BERT, GPT-2, Phi2, and LLaMA3, and fine-tunes Phi2-2.7B on Alpaca in just 40.1 hours using three consumer-grade mobile devices.

Despite large language models (LLMs) being an essential part of our lives, training of LLMs still needs to be done in cloud data centers due to the large requirements of data and computing power, leaving fine-tuning pre-trained LLMs on resource-constrained mobile devices remains highly under-explored. In this demo, we present Confidant, a practical collaborative training system that allows modern LLMs to be fine-tuned across multiple off-the-shelf mobile devices. Confidant partitions an LLM into several sub-models, deploying each of them to a mobile device. Multiple mobile devices then collaborate to train the LLM by employing a novel pipeline parallel training approach. Specifically, Confidant encompasses a memory-aware dynamic model partitioning and intra-device multi-processor scheduler to minimize the training time across heterogeneous platforms. A hybrid fault tolerance mechanism is also devised to proactively manage potential device and network failures. By building a cross-framework adapter and fully implementing Confidant on smartphones and laptops, we present the demo of collaborative training on a variety of mobile platforms.

We introduce RFMPose, a novel generative framework for category-level 6D object pose estimation that learns deterministic pose trajectories through Riemannian Flow Matching (RFM). Existing discriminative approaches struggle with multi-hypothesis predictions (e.g., symmetry ambiguities) and often require specialized network architectures. RFMPose advances this paradigm through three key innovations: (1) Ensuring geometric consistency via geodesic interpolation on Riemannian manifolds combined with bi-invariant metric constraints; (2) Alleviating symmetry-induced ambiguities through Riemannian Optimal Transport for probability mass redistribution without ad-hoc design; (3) Enabling end-to-end likelihood estimation through Hutchinson trace approximation, thereby eliminating auxiliary model dependencies. Extensive experiments on the Omni6DPose demonstrate state-of-the-art performance of the proposed method, with significant improvements of $\textbf{+4.1}$ in $\mathrm{\textbf{IoU}_{25}}$ and $\textbf{+2.4}$ in $\textbf{5°2cm}$ metrics compared to prior generative approaches. Furthermore, the proposed RFM framework exhibits robust sim-to-real transfer capabilities and facilitates pose tracking extensions with minimal architectural adaptation.

Condensing large datasets into smaller synthetic counterparts has demonstrated its promise for image classification. However, previous research has overlooked a crucial concern in image recognition: ensuring that models trained on condensed datasets are unbiased towards protected attributes (PA), such as gender and race. Our investigation reveals that dataset distillation fails to alleviate the unfairness towards minority groups within original datasets. Moreover, this bias typically worsens in the condensed datasets due to their smaller size. To bridge the research gap, we propose a novel fair dataset distillation (FDD) framework, namely FairDD, which can be seamlessly applied to diverse matching-based DD approaches (DDs), requiring no modifications to their original architectures. The key innovation of FairDD lies in synchronously matching synthetic datasets to PA-wise groups of original datasets, rather than indiscriminate alignment to the whole distributions in vanilla DDs, dominated by majority groups. This synchronized matching allows synthetic datasets to avoid collapsing into majority groups and bootstrap their balanced generation to all PA groups. Consequently, FairDD could effectively regularize vanilla DDs to favor biased generation toward minority groups while maintaining the accuracy of target attributes. Theoretical analyses and extensive experimental evaluations demonstrate that FairDD significantly improves fairness compared to vanilla DDs, with a promising trade-off between fairness and accuracy. Its consistent superiority across diverse DDs, spanning Distribution and Gradient Matching, establishes it as a versatile FDD approach.

As the implementation of machine learning (ML) systems becomes more widespread, especially with the introduction of larger ML models, we perceive a spring demand for massive data. However, it inevitably causes infringement and misuse problems with the data, such as using unauthorized online artworks or face images to train ML models. To address this problem, many efforts have been made to audit the copyright of the model training dataset. However, existing solutions vary in auditing assumptions and capabilities, making it difficult to compare their strengths and weaknesses. In addition, robustness evaluations usually consider only part of the ML pipeline and hardly reflect the performance of algorithms in real-world ML applications. Thus, it is essential to take a practical deployment perspective on the current dataset copyright auditing tools, examining their effectiveness and limitations. Concretely, we categorize dataset copyright auditing research into two prominent strands: intrusive methods and non-intrusive methods, depending on whether they require modifications to the original dataset. Then, we break down the intrusive methods into different watermark injection options and examine the non-intrusive methods using various finger-prints. To summarize our results, we offer detailed reference tables, highlight key points, and pinpoint unresolved issues in the current literature. By combining the pipeline in ML systems and analyzing previous studies, we highlight several future directions to make auditing tools more suitable for real-world copyright protection requirements.

Text-to-image models based on diffusion processes, such as DALL-E, Stable Diffusion, and Midjourney, are capable of transforming texts into detailed images and have widespread applications in art and design. As such, amateur users can easily imitate professional-level paintings by collecting an artist's work and fine-tuning the model, leading to concerns about artworks' copyright infringement. To tackle these issues, previous studies either add visually imperceptible perturbation to the artwork to change its underlying styles (perturbation-based methods) or embed post-training detectable watermarks in the artwork (watermark-based methods). However, when the artwork or the model has been published online, i.e., modification to the original artwork or model retraining is not feasible, these strategies might not be viable.

Differentiable architecture search (DARTS) is a prevailing direction in automatic machine learning, but it may suffer from performance collapse and generalization issues. Recent efforts mitigate them by integrating regularization into architectural parameters or rule-based operations selection. These efforts primarily emphasize learning the global class-specific features through the image classification task, while overlooking the fine-grained local information during the search process. In this article, we take the first trial to observe that three semantic challenges arise from the classification-based DARTS: 1) inaccurate class-specific features; 2) partial target attention; and 3) blurred semantic regions. To tackle them in one shot, we propose Semantic-DARTS, combining the masked image modeling (MIM) paradigm with the classification task to incorporate local semantic information into the architecture search. Specifically, we design a lightweight reconstruction head that recovers the corrupted image based on the condensed latent feature, which learns both the local semantics and their relationship patch-wisely. Simultaneously, the concurrent classification head strengthens the connection between the global category of the target and the local semantics of their parts. As evidenced by our experiments, the proposed approach achieves state-of-the-art results on CIFAR-10, CIFAR-100, and ImageNet. Furthermore, the searched model is not only able to improve global class-specific features but also to capture fine-grained local representations, improving both the classification performance and the generalization ability.

Modeling and solving the flexible job shop scheduling problem (FJSP) is critical for modern manufacturing. However, existing works primarily focus on the time-related makespan target, often neglecting other practical factors, such as transportation. To address this, we formulate a more comprehensive multitarget FJSP that integrates makespan with varied transportation times and the total energy consumption of processing and transportation. The combination of these multiple real-world production targets renders the scheduling problem highly complex and challenging to solve. To overcome this challenge, this article proposes an end-to-end multiagent proximal policy optimization (PPO) approach. First, we represent the scheduling problem as a disjunctive graph (DG) with designed features of subtasks and constructed machine nodes, additionally integrating information of arcs denoted as transportation and standby time, respectively. Next, we use a graph neural network (GNN) to encode features into node embeddings, representing the states at each decision step. Finally, based on the vectorized value function and local critic networks, the PPO algorithm and DG simulation environment iteratively interact to train the policy network. Our extensive experimental results validate the performance of the proposed approach, demonstrating its superiority over the state-of-the-art in terms of high-quality solutions, online computation time, stability, and generalization.

Autonomous reconstruction of unknown scenes using multiple robots acting as mobile Internet of Things (IoT) nodes becomes a fundamental capability for extensive IoT applications, such as environmental monitoring, and search and rescue. However, existing multi-robot autonomous reconstruction approaches still suffer from incomplete observations, redundant generated coverage tasks, and overly distant assigned tasks. To this end, we first utilize a diffusion-based model for object completion in autonomously reconstructed scenes using 3D Gaussian Splatting, and obtain Gaussian mixture model-based object uncertainties to guide the robots in generating more accurate scanning tasks that fill holes and enhance reconstruction quality. We then design an efficient task filtering mechanism that utilizes clustering frontiers and exploration tasks, as well as instances and reconstruction tasks, enabling the elimination of redundant coverage tasks. We also devise a task reassignment mechanism for robots based on the required travel costs to avoid unnecessary detours, which further improves scanning efficiency. Extensive experimental results show that our method exhibits higher reconstruction quality and superior planning efficiency compared to existing multi-robot autonomous reconstruction methods.

Given 2D point correspondences between an image pair, inferring the camera motion is a fundamental issue in the computer vision community. The existing works generally set out from the epipolar constraint and estimate the essential matrix, which is not optimal in the maximum likelihood (ML) sense. In this paper, we dive into the original measurement model with respect to the rotation matrix and normalized translation vector and formulate the ML problem. We then propose an optimal two-step algorithm to solve it: In the first step, we estimate the variance of measurement noises and devise a consistent estimator based on bias elimination; In the second step, we execute a one-step Gauss-Newton iteration on manifold to refine the consistent estimator. We prove that the proposed estimator achieves the same asymptotic statistical properties as the ML estimator: The first is consistency, i.e., the estimator converges to the ground truth as the point number increases; The second is asymptotic efficiency, i.e., the mean squared error of the estimator converges to the theoretical lower bound - Cramer-Rao bound. In addition, we show that our algorithm has linear time complexity. These appealing characteristics endow our estimator with a great advantage in the case of dense point correspondences. Experiments on both synthetic data and real images demonstrate that when the point number reaches the order of hundreds, our estimator outperforms the state-of-the-art ones in terms of estimation accuracy and CPU time.

Load balancers (LBs) are crucial in cloud environments, ensuring workload scalability. They route packets destined for a service (identified by a virtual IP address, or VIP) to a group of servers designated to deliver that service, each with its direct IP address (DIP). Consequently, LBs significantly impact the performance of cloud services and the experience of tenants. Many academic studies focus on specific issues such as designing new load balancing algorithms and developing hardware load balancing devices to enhance the LB's performance, reliability, and scalability. However, we believe this approach is not ideal for cloud data centers for the following reasons: (i) the increasing demands of users and the variety of cloud service types turn the LB into a bottleneck; and (ii) continually adding machines or upgrading hardware devices can incur substantial costs. In this paper, we propose the Next Generation Load Balancer (NGLB), designed to bypass the TCP connection datapath from the LB, thereby eliminating latency overheads and scalability bottlenecks of traditional cloud LBs. The LB only participates in the TCP connection establishment phase. The three key features of our design are: (i) the introduction of anactive address learningmodel to redirect traffic and bypass the LB, (ii) amulti-tenant isolationmechanism for deployment within multi-tenant Virtual Private Cloud networks, and (iii) a distributed flow control method, known ashierarchical connection cleaner, designed to ensure the availability of backend resources. The evaluation results demonstrate that NGLB reduces latency by 16% and increases nearly 3× throughput. With the same LB resources, NGLB improves 10× rate of new connection establishment. More importantly, five years of operational experience has proven NGLB's stability for high-bandwidth services.

3D human reconstruction from RGB images achieves decent results in good weather conditions but degrades dramatically in rough weather. Complementarily, mmWave radars have been employed to reconstruct 3D human joints and meshes in rough weather. However, combining RGB and mmWave signals for weather-robust 3D human reconstruction is still an open challenge, given the sparse nature of mmWave and the vulnerability of RGB images. The limited research about the impact of missing points and sparsity features of mmWave data on reconstruction performance, as well as the lack of available datasets for paired mmWave-RGB data, further complicates the process of fusing the two modalities. To fill these gaps, we build up an automatic 3D body annotation system with multiple sensors to collect a large-scale mmWave dataset. The dataset consists of synchronized and calibrated mmWave radar point clouds and RGB(D) images under different weather conditions and skeleton/mesh annotations for humans in these scenes. With this dataset, we conduct a comprehensive analysis about the limitations of single-modality reconstruction and the impact of missing points and sparsity on the reconstruction performance. Based on the guidance of this analysis, we design ImmFusion, the first mmWave-RGB fusion solution to robustly reconstruct 3D human bodies in various weather conditions. Specifically, our ImmFusion consists of image and point backbones for token feature extraction and a Transformer module for token fusion. The image and point backbones refine global and local features from original data, and the Fusion Transformer Module aims for effective information fusion of two modalities by dynamically selecting informative tokens. Extensive experiments demonstrate that ImmFusion can efficiently utilize the information of two modalities to achieve robust 3D human body reconstruction in various weather environments. In addition, our method achieves superior accuracy compared to that of the state-of-the-art Transformer-based LiDAR-camera fusion methods.

Neural radiance field (NeRF) has achieved impressive results in high-quality 3D scene reconstruction. However, NeRF heavily relies on precise camera poses. While recent works like BARF have introduced camera pose optimization within NeRF, their applicability is limited to simple trajectory scenes. Existing methods struggle while tackling complex trajectories involving large rotations. To address this limitation, we propose CT-NeRF, an incremental reconstruction and optimization pipeline using only RGB images without pose and depth input. In this pipeline, we first propose a local-global bundle adjustment under a pose graph connecting neighboring frames to enforce the consistency between poses to escape the local minima caused by only pose consistency with the scene structure. Further, we instantiate the consistency between poses as a reprojection error constraint resulting from pixel-level correspondences between input image pairs. Through the incremental reconstruction, CT-NeRF enables the recovery of both camera poses and scene structure and is capable of handling scenes with complex trajectories. We evaluate the performance of CT-NeRF on two real-world datasets, NeRF-Buster and Free-Dataset, which feature complex trajectories. Results show CT-NeRF outperforms existing methods in novel view synthesis and pose estimation accuracy.

In object goal navigation tasks, the robot’s understanding of semantic relationships in the environment is a key factor in its ability to localize target objects. Previously, learning-based methods trained robots using 3D scene datasets to learn semantic relationships. However, these approaches perform poorly in new environments with unfamiliar semantic contexts. In this paper, we propose ChatNav which leverages the powerful knowledge summarizing and reasoning capabilities of a Large Language Model (LLM) for zero-shot inference of explicit semantic relationships. These relationships are further integrated into the navigation system for efficient localization of target objects. ChatNav employs a spatial object clustering algorithm to collect semantic clues and designs common-sense-based prompts for interacting with LLM. It then uses a gravity-repulsion model to convert inference results into heuristic factors for robust navigation decision-making. Our approach requires no additional training and can consistently obtain accurate semantic relationships from LLM, making it well-suited for navigating unknown environments. Experimental results demonstrate the outstanding navigation performance of our proposed method on the Gibson and HM3D datasets, surpassing the current state-of-the-art object goal navigation methods.

Stealthy attacks manipulate the operation of Industrial Control Systems (ICSs) without being undetected, allowing persistent manipulation of system operation and thus the potential to cause destructive damage. This paper introduces a new vulnerability of ICS that can be exploited to mount stealthy attacks without requiring any domain knowledge. This vulnerability is caused by a common practice in system monitoring, i.e., the SCADA monitors ICS operation at a much lower frequency than system execution, causing a loss of precision when the SCADA tries to cross-validate the issued control commands using the collected sensory data. Exploiting this vulnerability, an attack calledPLC-SAGEis designed to stealthily manipulate the system operation by identifying and injecting malicious control commands that will not be concluded as abnormal by the SCADA. This paper further discusses a preferred ICS engineering practice and an attestation strategy to mitigate the above vulnerability and protect ICS fromPLC-SAGE. BothPLC-SAGEand the proposed mitigations have been experimentally validated on two ICS platforms.

The security risk of semantic attacks to Industrial Control Systems (ICSs) is increasing. Semantic attacks manipulate targeted system modules by identifying the physical semantics of variables in Programmable Logic Controllers (PLCs) programs, i.e., the sensing/actuating modules represented by the variables, which is usually and inefficiently achieved via manual examination of system documents and long-term observation of system behavior. In this paper, we designARES, a method thatAutomaticallyReverseEngineers theSemantics of variables in PLC programs without requiring any domain knowledge.ARESis built on the fact that the Supervisory Control And Data Acquisition (SCADA) system monitors the behavior of PLC using a fixed mapping between the variables of program code and data log, and the data log variables are marked with physical semantics. By identifying the mapping between PLC code and SCADA data (i.e., the code-data mapping),ARESreverse engineers the physical semantics of program variables.ARESalso sheds light on the preferred defense strategies in implementing control rules that improve the resistance of PLC programs to semantic attacks, as well as in detecting and responding to semantics attacks in real time. We have experimentally evaluatedARESand the recommended defending practices on two ICS platforms.

The increasing network integration of industrial control systems amplifies the risk of cyberattacks on Programmable Logic Controllers (PLCs). In particular, the weak authentication of industrial communication protocols makes PLC program variables vulnerable to manipulation. Current defensive methods cannot reliably identify manipulated variables, even after PLC program manipulations have been detected. To bridge this gap, we presentADIS, a cross-domain Attack Detection and Identification System designed to detect and identify manipulated PLC program variables. Building on a novel state-aware graph representation of the PLC program,ADISdetects variable manipulations by comparing SCADA monitoring data with the control logic defined by the PLC program.ADISfurther identifies suspiciously manipulated program variables by excluding cascading failures from the detected anomalies and tracking suspicious variables based on the edges of the state-aware dependency graph. We have implemented and evaluatedADISon two platforms. The results demonstrate thatADISdetects attacks with a true positive rate exceeding 99% and a false positive rate of less than$0.04{\unicode {0x2030}}$. Furthermore, it successfully identifies manipulated program variables with up to a 71.3% reduction in suspicious variables compared to a baseline method.

Physical modules are the basic functional units of industrial control systems, governed by Programmable Logic Controllers (PLCs) according to predefined control logic. Attackers can compromise the integrity of physical modules by tampering with control logic, potentially disrupting production or causing physical damage. While model checking can detect logic bugs that violate module integrity requirements, it depends heavily on domain-specific knowledge, which is traditionally summarized by human experts, limiting both scalability and completeness. This paper proposes DGVerifier, a wiring diagram-guided framework that automatically verifies two general integrity requirements of physical modules: action integrity and state transition integrity. DGVerifier can extract module-related information from PLC wiring diagrams, mine domain-specific knowledge to generate specifications, and also model PLC programs as automata for verification. Evaluation on two real-world systems — an Elevator Control System and an Automated Assembly Line Control System — shows DGVerifier can recover 89.3% (25/28) of the required specifications and identify four hidden logic bugs violating physical module integrity.

Voltage collapse is a critical form of system instability in power systems, occurring when power generation is unable to meet power demand, resulting in considerable socio-economic impacts. Current methodologies for studying voltage collapse primarily utilize simulation-based approaches. While informative, they offer little theoretical insights into the mechanism of this perplexing phenomenon due to their numerical nature. This article introduces a novel analytical framework in distribution system based on dimension reduction. By effectively mapping high-dimensional systems into simpler, lower dimensional equivalents, our framework is capable of mathematically solving system equations. This subsequently differentiates the critical factors from the less influential ones and proposes a novel voltage stability index. The voltage stability index is directly calculated by the structure-based weighted sum of power demands without monitoring data. This approach facilitates the identification of potential origins of system instability and highlights components that are particularly vulnerable, thereby enabling more targeted and effective measures for system reinforcement and risk mitigation. We rigorously test our framework on seven different distribution systems, demonstrating its efficacy and potential as a tool for enhancing grid stability. Our findings indicate that this novel approach can offer significant advantages in understanding and mitigating the risks of voltage collapse.

Trajectory prediction is an essential component in autonomous driving, particularly for collision avoidance systems. Considering the inherent uncertainty of the task, numerous studies have utilized generative models to produce multiple plausible future trajectories for each agent. However, most of them suffer from limited representation ability or unstable training issues. To overcome these limitations, we propose utilizing the diffusion model to generate the distribution of future trajectories. Two cruxes are to be settled to realize such an idea. First, the diversity of intention is intertwined with the uncertain surroundings, making the true distribution hard to parameterize. Second, the diffusion process is time-consuming during the inference phase, rendering it unrealistic to implement in a real-time driving system. We propose an Intention-aware denoising Diffusion Model (IDM), which addresses the above two problems. We decouple the original uncertainty into intention uncertainty and action uncertainty and model them with two dependent diffusion processes. To decrease the inference time, we reduce the variable dimensions in the intention-aware diffusion process and restrict the initial distribution of the action-aware diffusion process, which leads to fewer diffusion steps. To validate our approach, we conduct experiments on the Stanford Drone Dataset (SDD) and the ETH/UCY dataset. Our methods achieve state-of-the-art results, with a minFDE of 13.83 pixels on the SDD dataset and 0.36 meters on ETH/UCY datasets. Compared with the original diffusion model, IDM reduces inference time by two-thirds. Interestingly, our experiments further reveal that introducing intention information is beneficial in modeling the diffusion process of fewer steps.

Whilesmalldrone video streaming systems create unprecedented video content, they also place a power burden exceeding 20% on the drone's battery, limiting flight endurance. We present${\sf Mighty}$, a hardware-software solution to minimize the power consumption of a drone's video streaming system by offloading power overheads associated with both video compression and transmission to a ground controller.${\sf Mighty}$innovates a high performance co-design among:(1)a ring oscillator-based, ultra-low power backscatter radio;(2)a spectrally-efficient, non-linear, low-power physical layer modulation and multi-chain radio architecture; and(3)a lightweight video compression codec-bypassing software design. Our co-design exploits synergies among these components, resulting in joint throughput and range performance that pushes the known envelope. We prototype${\sf Mighty}$on PCB board and conduct extensive field studies both indoors and outdoors. The power efficiency of${\sf Mighty}$is about 16.6 nJ/bit. A head-to-head comparison with aDJI Mini2drone's default video streaming system shows that${\sf Mighty}$achieves similar throughput at a drone-to-controller distance of up to 150 meters, with 34–55× improvement of power efficiency than WiFi-based video streaming solutions.

Frequency switching is a fundamental capability for wireless communication systems. However, this capability is significantly constrained in backscatter systems. The difficulty is to generate tunable high-frequency modulation signals on a backscatter tag at an acceptable power budget. In this paper, we present Twaltz, a new design paradigm for backscatter communication that enables frequency switching across large frequency bands. By exploiting a low-power semiconductor device, i.e., tunnel diode, and carefully addressing its physical features, Twaltz generates oscillation signals up to 1.2 GHz while maintaining micro-watt level power consumption. Twaltz further facilitates on-tag oscillation signal stabilization and programmable oscillation frequency tuning. We prototype Twaltz on a PCB board, demonstrating its efficiency in cross-band communication for LoRa backscatter, and verifying its performance in concurrent transmission, channel hopping, and data transmission.

Continual machine learning in the context of limited computational resources and data availability is critical in the connected digital world. Current intelligent applications predominantly rely on deep learning models requiring labor/computation-intensive training. These models often struggle to adapt effectively to new data while preserving performance on previously learned knowledge. In this paper, we introduce a lightweight method for continual knowledge adaptation that can address these challenges. To prevent disruption of the existing services, we propose a Mixture-of-Experts (MoE) adapter that integrates seamlessly with the existing vision model to encode new data. The weights of the original model are kept fixed during the adaptation process, ensuring the preservation of previously learned knowledge. The MoE technique enables scaling up the parameters of the adapter while maintaining a relatively low computation, making it fit for constrained devices in mobile computation scenarios. Furthermore, to enhance learning efficiency and accelerate convergence with new data, we implement a knowledge fusion mechanism that facilitates interaction between the existing knowledge and the information extracted from new data. The timing of employing the fusion module is further investigated. We find that it is conducive in scenarios where the task's performance requirements are enhanced. The MoE adapter and knowledge fusion module are integrated at each stage with minimal trainable parameters, efficiently optimizing resource usage. Extensive experiments and ablation studies validate the effectiveness of the proposed method. Specifically, the proposed method prevents an accuracy drop of 43.02% on the previous data compared to the continual train method, while achieving an accuracy of 44.81% on the new data, which is even 0.34% higher than fully training a new model.

With the rapid development of Connected and Autonomous Vehicles, In-Vehicle Network attacks have garnered heightened research scrutiny due to vehicles’ increasing connectivities to the external environment. The common characteristic among these attacks is to tamper with targeted powertrain-related signals in the Powertrain Controller Area Network (PT-CAN) and further physically threaten vehicles’ safety. These powertrain-related signals are encoded within CAN messages grounded by the syntax specification, which is proprietary to Original Equipment Manufacturers and publicly unavailable. Thus, to undertake comprehensive security analysis and strategies, reverse engineering PT-CAN to the semantic level is urgently needed. However, the existing methods rely on interactions (injecting challenge signals/actions) with the targeted vehicle, and certain manual efforts are required. To fill this gap, we proposePicaCAN, a novel framework to extract signals from CAN messages and reverse engineer their physical semantics based on physically induced causality. Once access to the CAN traffic,PicaCANoffers the researcher an eye on the vehicle’s powertrain system, decoding binaries flows into powertrain-related signals automatically. We experimentally evaluatePicaCANon PT-CAN of three automobiles containing two power types. The experimental results show thatPicaCANcould successfully extract physical signals representing all targeted semantics (pedals, engine speed, etc.) from two Internal Combustion Engine Vehicles and one Hybrid Electric Vehicle under EV mode.

Urban computing leverages data analysis to improve urban areas' efficiency and sustainability, tackling tasks like traffic management, crime forecasting, and air quality predictions. Current models, while efficient, often struggle with tasks beyond their initial training due to limited flexibility. Typically, new tasks require developing specialized models, which may not perform optimally with limited data. To overcome these challenges, we propose the development of a universal pretrained model that understands a city's various aspects comprehensively. This model serves as a robust foundation, ready to be quickly adjusted for different urban tasks as they arise, even if they occur in different cities. Unlike language models, urban computing models must handle unique spatial-temporal dynamics, making standard pretraining techniques inadequate. Our approach includes a spatial-temporal module with multi-graph convolution and temporal attention mechanisms, capturing the necessary spatial-temporal patterns during pretraining. We also integrate a prompt-tuning module within this framework, which can be adapted for new predictive tasks. The results of extensive experiments on four urban predictive tasks across two cities demonstrate the effectiveness of our model

Recent advancements in sensor technology and deep learning have led to significant progress in 3D human body reconstruction. However, most existing approaches rely on data from a specific sensor, which can be unreliable due to the inherent limitations of individual sensing modalities. Additionally, existing multi-modal fusion methods generally require customized designs based on the specific sensor combinations or setups, which limits the flexibility and generality of these methods. Furthermore, conventional point-image projection-based and Transformer-based fusion networks are susceptible to the influence of noisy modalities and sensor poses. To address these limitations and achieve robust 3D human body reconstruction in various conditions, we propose AdaptiveFusion, a generic adaptive multi-modal multi-view fusion framework that can effectively incorporate arbitrary combinations of uncalibrated sensor inputs. By treating different modalities from various viewpoints as equal tokens, and our handcrafted modality sampling module by leveraging the inherent flexibility of Transformer models, AdaptiveFusion is able to cope with arbitrary numbers of inputs and accommodate noisy modalities with only a single training network. Extensive experiments on large-scale human datasets demonstrate the effectiveness of AdaptiveFusion in achieving high-quality 3D human body reconstruction in various environments. In addition, our method achieves superior accuracy compared to state-of-the-art fusion methods.

TCP ensures reliable transmission through its stateful implementation and remains crucial today. TCP performance profiling is essential for tasks like diagnosing network performance problems, optimizing transmission performance, and developing new TCP variants, etc. Existing profiling methods lack enough attention to TCP state transition to provide detailed insights on TCP performance. Thus, we build TcpSight, a tool focusing on TCP state transition throughout connection lifetimes. TcpSight conducts stateful analysis by capturing key events using an efficient per-connection lock-free data management mechanism. Besides, TcpSight enhances profiling by integrating application layer information collected from the TCP stack. With the profiling results, users can identify the culprit of TCP performance degradation, and evaluate the performance of TCP algorithms. We design optional modules and filtering mechanisms to reduce TcpSights overhead. Our evaluation presents that TcpSight incurs an additional CPU consumption of about 16.6% (without filtering) and 10.6% (with filtering) when the servers load is 55.7%, and generates storage consumption about 1.88 KB per connection on average. We also give application cases of TcpSight and the deployment experiences in Alibaba Cloud. TcpSight helps in revealing meaningful findings and insights into exploiting TCP in the production deployment.

Face authentication (FA) schemes are widely adopted in smart homes nowadays. However, existing FA systems for smart appliances are commonly camera-based and hence experience performance degradation in poor illumination conditions. Mainstream FA systems based on radio frequency require dedicated hardware that is inaccessible to many appliances. In this paper, we propose an acoustic signals-based FA scheme that extracts acoustic signal features associated with facial 3D geometries to achieve FA named SoundFace . This scheme can be widely deployed on most appliances in home environments. We propose a novel two-stage locating approach based on acoustic sensing to capture the signal variation of the user’s face and separate the face region echoes from multipath interferences in the distance dimension. To obtain distinguishable facial features, we design a Convolutional Neural Network (CNN)-based feature extractor. In addition, the acoustic signal is highly susceptible to different changes in practical authentication. To overcome it, we utilize a transfer learning technique with little training overhead to enable SoundFace resilient to various authentication changes. Extensive evaluations demonstrate that SoundFace achieves an average true authentication rate of over 96.2% and an equal error rate of 4.2%, and it is robust to various real-world settings.

This work presents a visual odometry (VO) system that leverages image edge features. Edges are spatially expressive cues commonly present across diverse environments, offering rich textural and structural information. However, existing edge-based VO methods often fail to fully exploit this potential. To this end, we introduce a novel feature representation termedorganized edges, which transforms disjoint edge pixels into sequentialized clusters, enabling more effective retention and utilization of the underlying textural and structural information. Another nice property of this formulation is that organized edges can perform edge-level association across multiple frames, enabling the establishment of a co-visibility graph. To achieve precise and efficient pose estimation, we propose a range of particularly designed tracking and joint optimization methods based on the characteristics of organized edges. For tracking, we formulate edge-wise rather than pixel-wise residuals to achieve robust and accurate inter-frame registration. For joint optimization, we introduce a novel shape-preserving edge-fitting method and an organized edge-based Bundle Adjustment (BA) approach, which decomposes the traditional BA problem into fitting and registration to preserve the structural integrity. Based on these novel techniques, we develop a complete VO system that exclusively employs organized edge features, achieving efficient tracking and precise local mapping. Extensive experiments demonstrate its accuracy and robustness in indoor environments, outperforming or achieving comparable performance to state-of-the-art methods.

Our work aims to reconstruct a 3D object that is held and rotated by a hand in front of a static RGB camera. Previous methods that use implicit neural representations to recover the geometry of a generic hand-held object from multi-view images achieved compelling results in the visible part of the object. However, these methods falter in accurately capturing the shape within the hand-object contact region due to occlusion. In this paper, we propose a novel method that deals with surface reconstruction under occlusion by incorporating priors of 2D occlusion elucidation and physical contact constraints. For the former, we introduce an object amodal completion network to infer the 2D complete mask of objects under occlusion. To ensure the accuracy and view consistency of the predicted 2D amodal masks, we devise a joint optimization method for both amodal mask refinement and 3D reconstruction. For the latter, we impose penetration and attraction constraints on the local geometry in contact regions. We evaluate our approach on HO3D and HOD datasets and demonstrate that it outperforms the state-of-the-art methods in terms of reconstruction surface quality, with an improvement of 52% on HO3D and 20% on HOD. Project webpage: https://east-j.github.io/ihor.

Recommender systems predict and suggest relevant options to users in various domains, such as e-commerce, streaming services, and social media. Recently, deep reinforcement learning (DRL)-based recommendation systems have become increasingly popular in academics and industry since DRL can characterize the long-term interaction between the system and users to achieve a better recommendation experience, e.g., Netflix, Spotify, Google, and YouTube.

Tracking by natural language specification (TNL) aims to consistently localize a target in a video sequence given a linguistic description in the initial frame. Existing methodologies perform language-based and template-based matching for target reasoning separately and merge the matching results from two sources, which suffer from tracking drift when language and visual templates missalign with the dynamic target state and ambiguity in the later merging stage. To tackle the issues, we propose a joint multi-modal tracking framework with 1) a prompt modulation module to leverage the complementarity between temporal visual templates and language expressions, enabling precise and context-aware appearance and linguistic cues, and 2) a unified target decoding module to integrate the multi-modal reference cues and executes the integrated queries on the search image to predict the target location in an end-to-end manner directly. This design ensures spatio-temporal consistency by leveraging historical visual information and introduces an integrated solution, generating predictions in a single step. Extensive experiments conducted on TNL2K, OTB-Lang, LaSOT, and RefCOCOg validate the efficacy of our proposed approach. The results demonstrate competitive performance against state-of-the-art methods for both tracking and grounding. Code is available at https://github.com/twotw02/QueryNLT

Internet of Things (IoT) devices face an escalating threat from code reuse attacks (CRAs) as they can reuse existing code for malicious purpose. Thus a practical cost-effective Control-Flow Integrity (CFI) mechanism for IoT devices is urgently needed. However, existing CFI solutions suffer from impractical-ities, including high performance overhead and a heavy reliance on offline perfect Control-Flow Graph (CFG) generation. To tackle these challenges, we propose a fine-grained dependable CFI scheme for IoT devices that real-time updates the CFG of devices. We evaluate the implementation on RISC-V architectures and the results show that our CFI scheme provides both backward- and forward-edge protection with almost no performance overhead in the case of fixed CFG, negligible power overhead, and low hardware overhead. Compared to the current hardware-assisted CFI designs, our design eliminates the dependence on the offline perfect CFG generation and performs real-time CFG updating for better practicality.

The demand for continual machine learning in the context of limited computational resources and data availability is critical in the evolving landscape of the connected digital world. Current network applications predominantly rely on deep learning models that require labor/computation-intensive training processes. These models often struggle to effectively adapt to new data while preserving performance on previously acquired knowledge. In this paper, we introduce a lightweight framework for continual knowledge adaptation and learning designed to address these challenges. To prevent disruption of existing services, we propose an attention-based adapter that integrates seamlessly with the existing vision model to encode new incoming data. The weights of the original model are kept fixed during the adaptation process, ensuring the preservation of previously learned knowledge. Furthermore, to enhance learning efficiency and accelerate convergence with new data, we implement a knowledge fusion mechanism that facilitates interaction between existing knowledge and information from new data. Our framework is modular, enabling flexible deployment across distributed devices. The adapter and knowledge fusion module are implemented at each stage with minimal trainable parameters, optimizing resource usage. Extensive experiments and ablation studies validate the effectiveness of the proposed framework.

The interference-plus-noise covariance matrix (INCM) reconstruction-based adaptive beamforming methods have been successful in preventing signal self-nulling. However, their computational complexity is generally high, which cannot be neglected. In this paper, we propose a data-driven adaptive beamforming method named Deep-Reconstruction, which utilizes deep learning to establish a direct mapping from the sample covariance matrix to the inverse of the INCM. Specifically, we devise a Unet-based fully convolutional network to extract the low-dimensional representations of interferences and noise from the sample covariance matrix. Meanwhile, a conjugate symmetrization layer is designed to maintain a Hermitian structure of the network output. As a result, an accurate estimation of the inverse of the INCM can be obtained for the beamformer design. Simulation results demonstrate that the proposed method can effectively avoid signal self-nulling, while achieving a higher computational efficiency as compared to the traditional methods.

The millimeter-wave (mmWave) radar has been exploited for gesture recognition. However, existing mmWave-based gesture recognition methods cannot identify different users, which is important for ubiquitous gesture interaction in many applications. In this paper, we propose GesturePrint, which is the first to achieve gesture recognition and gesture-based user identification using a commodity mmWave radar sensor. GesturePrint features an effective pipeline that enables the gesture recognition system to identify users at a minor additional cost. By introducing an efficient signal preprocessing stage and a network architecture GesIDNet, which employs an attention-based multi-level feature fusion mechanism, GesturePrint effectively extracts unique gesture features for gesture recognition and personalized motion pattern features for user identification. We implement GesturePrint and collect data from 17 participants performing 15 gestures in a meeting room and an office, respectively. GesturePrint achieves a gesture recognition accuracy (GRA) of 98.87% with a user identification accuracy (UIA) of 99.78% in the meeting room, and 98.22% GRA with 99.26% UIA in the office. Extensive experiments on three public datasets and a new gesture dataset show GesturePrint's superior performance in enabling effective user identification for gesture recognition systems.

Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary data to detect anomalies without any training sample in a target dataset. It is a crucial task when training data is not accessible due to various concerns, e.g., data privacy, yet it is challenging since the models need to generalize to anomalies across different domains where the appearance of foreground objects, abnormal regions, and background features, such as defects/tumors on different products/ organs, can vary significantly. Recently large pre-trained vision-language models (VLMs), such as CLIP, have demonstrated strong zero-shot recognition ability in various vision tasks, including anomaly detection. However, their ZSAD performance is weak since the VLMs focus more on modeling the class semantics of the foreground objects rather than the abnormality/normality in the images. In this paper we introduce a novel approach, namely AnomalyCLIP, to adapt CLIP for accurate ZSAD across different domains. The key insight of AnomalyCLIP is to learn object-agnostic text prompts that capture generic normality and abnormality in an image regardless of its foreground objects. This allows our model to focus on the abnormal image regions rather than the object semantics, enabling generalized normality and abnormality recognition on diverse types of objects. Large-scale experiments on 17 real-world anomaly detection datasets show that AnomalyCLIP achieves superior zero-shot performance of detecting and segmenting anomalies in datasets of highly diverse class semantics from various defect inspection and medical imaging domains. Code will be made available at https://github.com/zqhang/AnomalyCLIP.

The design of Low Power Wide Area Network (LPWAN) protocols has attracted increasing attention in recent years, particularly within the LoRa research community. However, NB-IoT, another critical LPWAN technology, has not seen similar growth in its research community due to the lack of a functional and flexible software-defined radio (SDR) implementation. To address this gap, we present SoftNB, the first fully functional physical layer SDR implementation for NB-IoT. SoftNB conforms to the latest 3GPP standards and features an efficient and effective signal processing pipeline to mitigate time, frequency, and phase offsets during transmission and reception. Additionally, SoftNB is compatible with various SDR platforms, including USRP, HackRF One, and RTL-SDR Dongle. Extensive evaluations of SoftNB demonstrate its superior performance. Compared to the state-of-the-art baseline, SoftNB achieves an$8\times$reduction in Block Error Rate (BLER) when the number of repetitions is set to 4 at a distance of 450 meters.

Grasping motion planning aims to find a feasible grasping trajectory in the configuration space given an input target grasp. While optimizing grasp motion with two or three-fingered grippers has been well studied, the study on natural grasp motion planning with a dexterous hand remains a very challenging problem due to the high dimensional working space. In this work, we propose a novel temporal-parametric grasp prior (TPGP) optimization method to simplify the difficulty of grasping trajectory optimization for the dexterous hand while maintaining smooth and natural properties of the grasping motion. Specifically, we formulate the discrete trajectory parameters into a temporal-based parameterization, where the prior constraint provided by a hand poser network, is introduced to ensure that hand pose is natural and reasonable throughout the trajectory. Finally, we present a joint target optimization strategy to enhance the target pose for more feasible trajectories. Extensive validations on two public datasets show that our method outperforms state-of-the-art methods regarding grasp motion on various metrics.

Robust and accurate pose estimation in unknown environments is an essential part of robotic applications. We focus on LiDAR-based point-to-point ICP combined with effective semantic information. This paper proposes a novel semantic information-assisted ICP method named SAGE-ICP, which leverages semantics in odometry. The semantic information for the whole scan is timely and efficiently extracted by a 3D convolution network, and these point-wise labels are deeply involved in every part of the registration, including semantic voxel downsampling, data association, adaptive local map, and dynamic vehicle removal. Unlike previous semantic-aided approaches, the proposed method can improve localization accuracy in large-scale scenes even if the semantic information has certain errors. Experimental evaluations on KITTI and KITTI-360 show that our method outperforms the baseline methods, and improves accuracy while maintaining real-time performance, i.e., runs faster than the sensor frame rate.

Recently, pre-trained vision models have gained significant attention in motor control, showcasing impressive performance across diverse robotic learning tasks. While previous works predominantly concentrate on the significance of the pre-training phase, the equally important task of extracting more effective representations based on existing pre-trained visual models remains unexplored. To better leverage the representation capabilities of pre-trained models for robotic grasping, we propose InterRep, a novel interaction representation method that possesses not only the strengths of pre-trained models, known for their robustness in noisy environments and their proficiency in recognizing essential features, but also the capacity of capturing dynamic interaction details and local geometric features during the grasping process. Based on the novel representation, we introduce a deep reinforcement learning method to learn generalizable grasping policies. The experimental results demonstrate that our proposed representation outperforms the baselines in terms of both training speed and generalization. For the generalized grasping tasks with dexterous robotic hands, our method boasts a success rate nearly 20% higher than methods using the global features of the entire image from pre-trained models. In addition, our proposed representation method demonstrates promising performance when applied to a different robotic hand and task. It also exhibits excellent performance on real robots with a success rate of 70%.

Interactive hand mesh reconstruction from singleview images poses a significant challenge with the severe occlusion and depth ambiguity inherent in interactive hand gestures. Recent approaches that employ probabilistic models and tokenpruned techniques have shown decent results in multi-view human body reconstruction. Nevertheless, these methods have not fully utilized multi-scale semantic information from multiview images and are not applicable in scenarios involving severe occlusion during dual-hand interactions. Simultaneously, current single-view methods independently reconstruct the left and right hands, which are ineffective in enhancing the interaction between both hands. To address these challenges, we propose CAMInterHand, a cooperative attention-based method for multi-view interactive hand pose and mesh reconstruction. Specifically, CAMInterHand extracts local pyramid features and global vertex features from multi-scale feature maps of multi-view images, enabling the exploration of rich local semantic information and facilitating effective feature alignment. Furthermore, CAMInterHand employs the cooperative attention fusion module to fuse all features from multi-view images, enhancing interactions among vertices of dual hands within global and local contexts. We conduct extensive experiments on the large-scale multi-view dataset InterHand2.6M and CAMInterHand achieves a substantial performance improvement over existing methods for multi-view and single-view interactive hand reconstruction.

Sparse keypoint matching based on distinct 3D feature representations can improve the efficiency and robustness of point cloud registration. Existing learning-based 3D descriptors and keypoint detectors are either independent or loosely coupled, so they cannot fully adapt to each other. In this work, we propose a tightly coupled keypoint detector and descriptor (TCKDD) based on a multi-task fully convolutional network with a probabilistic detection loss. In particular, this self-supervised detection loss fully adapts the keypoint detector to any jointly learned descriptors and benefits the self-supervised learning of descriptors. Extensive experiments on both indoor and outdoor datasets show that our TCKDD achieves state-of- the-art performance in point cloud registration. Furthermore, we design a keypoint detector and descriptors-assisted LiDAR odometry and mapping framework (KDD-LOAM), whose real-time odometry relies on keypoint descriptor matching-based RANSAC. The sparse keypoints are further used for efficient scan-to-map registration and mapping. Experiments on KITTI dataset demonstrate that KDD-LOAM significantly surpasses LOAM and shows competitive performance in odometry.

Existing methods for place recognition in 3D point clouds either ignore partial structure information by converting 3D scans to 2D images or construct constrained bag-of-words (BoW) representations reliant on specific feature extraction algorithms. In this paper, we propose a novel method based on incremental and general bag of words. Incorporating an adaptable keypoint and 3D local feature extraction method, we employ an incremental BoW model that is updated regularly. This enables a coarse-to-fine candidate selection from the database. And a revisit can be identified following geometric verification. In addition, we propose a new supplementary metric that addresses the leaving-out issue of the conventional metric, enhancing the identification of true loops. Employing a state-of-the-art (SOTA) keypoint and feature extraction algorithm, we evaluate our method as well as SOTA place recognition methods using diverse datasets with varying qualities. Experimental results demonstrate that our method outperforms the baselines across all three datasets, showcasing robust performance and notable generalization capabilities.

Precise and long-term stable localization is essential in parking lots for tasks like autonomous driving or autonomous valet parking, etc. Existing methods rely on a fixed and memory-inefficient map, which lacks robust data association approaches. And it is not suitable for precise localization or long-term map maintenance. In this paper, we propose a novel mapping, localization, and map update system based on ground semantic features, utilizing low-cost cameras. We present a precise and lightweight parameterization method to establish improved data association and achieve accurate localization at centimeter-level. Furthermore, we propose a novel map update approach by implementing high-quality data association for parameterized semantic features, allowing continuous map update and refinement during re-localization, while maintaining centimeter-level accuracy. We validate the performance of the proposed method in real-world experiments and compare it against state-of-the-art algorithms. The proposed method achieves an average accuracy improvement of 5cm during the registration process. The generated maps consume only a compact size of 450 KB/km and remain adaptable to evolving environments through continuous update.

Recent works on the pretraining for robot manipulation have demonstrated that representations learning from large human manipulation data can generalize well to new manipulation tasks and environments. However, these approaches mainly focus on human vision or natural language, neglecting tactile feedback. In this article, we make an attempt to explore how to pre-train a representation model for robotic manipulation using both human manipulation visual and tactile data. We develop a system for collecting visual and tactile data, featuring a cost-effective tactile glove to capture human tactile data and Hololens2 for capturing visual data. With this system, we collect a dataset of turning bottle caps. Furthermore, we introduce a novel visual-tactile fusion network and learning strategy M2VTP, with one key module to tokenize 20 sparse binary tactile signals sensing touch states for the learning of tactile context and the other key module applying the attention and mask mechanism to the interaction of visual and tactile tokens for visual-tactile representation learning. We utilize our dataset to pre-train the fusion model and embed the pre-trained model into a reinforcement learning framework for downstream tasks. Experimental results demonstrate that our pre-trained model significantly aids in learning manipulation skills. Compared to methods without pre-training, our approach achieves a success rate increase of over 60%. Additionally, when compared to current visual pre-training methods, our success rate exceeds them by more than 50%.

Hand motion tracking is essential in many fields, e.g., immersive virtual reality, teleoperation of robotic hand, and hand rehabilitation of stroke patient, as human hand plays a crucial role in our daily life. The highly under-actuated hand exoskeleton, which can track the 6-DoF motions of each fingertip via a highly under-actuated kinematic chain, exhibits many benefits in wearability and portability over other solutions. However, due to the non-anthropomorphic linkage, this hand exoskeleton also encounters difficulties in measuring human-finger’s joint angles. While the joint-space is important in many scenarios, such as teleoperating a robotic hand with anthropomorphic kinematics but with different size to human. Here we proposed a new method to reconstruct the human finger joints by using a highly under-actuated hand exoskeleton. Our key contribution is the arc-fitting algorithm, which is able to calibrate the misalignment between the exoskeleton’s and the human-finger’s base frames and estimate the length of human’s phalanxes, by using the fingertip’s circular motions. With knowing the aforementioned informations, the joint angles can be reconstructed in high precision based on the inverse kinematics models of human fingers. Furthermore, our proposed method is compared with a baseline method, in which the joint angles obtained by a motion capture system are served as ground-truth. The results demonstrate that our proposed method exhibits excellent performance in reconstructing finger’s joint configurations.

Implicit neural representations have demonstrated significant promise for 3D scene reconstruction. Recent works have extended their applications to autonomous implicit reconstruction through the Next Best View (NBV) based method. However, the NBV method cannot guarantee complete scene coverage and often necessitates extensive viewpoint sampling, particularly in complex scenes. In the paper, we propose to 1) incorporate frontier-based exploration tasks for global coverage with implicit surface uncertainty-based reconstruction tasks to achieve high-quality reconstruction. and 2) introduce a method to achieve implicit surface uncertainty using color uncertainty, which reduces the time needed for view selection. Further with these two tasks, we propose an adaptive strategy for switching modes in view path planning, to reduce time and maintain superior reconstruction quality. Our method exhibits the highest reconstruction quality among all planning methods and superior planning efficiency in methods involving reconstruction tasks. We deploy our method on a UAV and the results show that our method can plan multi-task views and reconstruct a scene with high quality.

The sim-to-real gap poses a significant challenge in RL-based multi-agent exploration due to scene quantization and action discretization. Existing platforms suffer from the inefficiency in sampling and the lack of diversity in Multi-Agent Reinforcement Learning (MARL) algorithms across different scenarios, restraining their widespread applications. To fill these gaps, we propose MAexp, a generic platform for multi-agent exploration that integrates a broad range of state-of-the-art MARL algorithms and representative scenarios. Moreover, we employ point clouds to represent our exploration scenarios, leading to high-fidelity environment mapping and a sampling speed approximately 40 times faster than existing platforms. Furthermore, equipped with an attention-based Multi-Agent Target Generator and a Single-Agent Motion Planner, MAexp can work with arbitrary numbers of agents and accommodate various types of robots. Extensive experiments are conducted to establish the first benchmark featuring several high-performance MARL algorithms across typical scenarios for robots with continuous actions, which highlights the distinct strengths of each algorithm in different scenarios.

Industrial protocols are widely used in Industrial Control Systems (ICSs) to network physical devices, thus playing a crucial role in securing ICSs. However, most commercial industrial protocols are proprietary and owned by their vendors, which impedes the implementation of protections against cyber threats. In this paper, we design REInPro to Reverse Engineer Industrial Protocols. REInPro is inspired by the fact that the structure of industrial protocols can be determined by a particular field referred to control field. By applying a probabilistic model of network traffic behavior, REInPro automatically identifies the control field and groups the associated network traffic into clusters. REInPro then infers critical semantics of industrial protocols by differentiating the features of corresponding protocol fields. We have experimentally implemented and evaluated REInPro using 8 different industrial protocols across 6 Programmable Logic Controllers (PLCs) belonging to 5 original equipment manufacturers. The experimental results show REInPro to reverse-engineer the formats and semantics of industrial protocols with an average correctness/perfection of 0.70/0.58 and 0.96/0.39.

Neural networks (NNs) are known to have diverse defects such as adversarial examples, backdoor and discrimination, raising great concerns about their reliability. While NN testing can effectively expose these defects to a significant degree, understanding their root causes within the network requires further examination. In this work, inspired by the idea of debugging in traditional software for failure isolation, we propose a novel unified neuron-isolation-based framework for debugging neural networks, shortly IDNN. Given a buggy NN that exhibits certain undesired properties (e.g., discrimination), the goal of IDNN is to identify the most critical and minimal set of neurons that are responsible for exhibiting these properties. Notably, such isolation is conducted with the objective that by simply ‘freezing’ these neurons, the model’s undesired properties can be eliminated, resulting in a much more efficient model repair compared to computationally expensive retraining or weight optimization as in existing literature. We conduct extensive experiments to evaluate IDNN across a diverse set of NN structures on five benchmark datasets, for solving three debugging tasks, including backdoor, unfairness, and weak class. As a lightweight framework, IDNN outperforms state-of-the-art baselines by successfully identifying and isolating a very small set of responsible neurons, demonstrating superior generalization performance across all tasks.

It is notoriously challenging to audit the potential unauthorized data usage in deep learning (DL) model development lifecycle, i.e., to judge whether certain private user data has been used to train or fine-tune a DL model without authorization. Yet, such data usage auditing is crucial to respond to the urgent requirements of trustworthy Artificial Intelligence (AI) such as data transparency, which are promoted and enforced in recent AI regulation rules or acts like General Data Protection Regulation (GDPR) and EU AI Act. In this work, we propose TeDA, a simple and flexible testing framework for auditing data usage in DL model development process. Given a set of user’s private data to protect (Dp), the intuition of TeDA is to apply membership inference (with good intention) for judging whether the model to audit (Ma) is likely to be trained with Dp. Notably, to significantly expose the usage under membership inference, TeDA applies imperceptible perturbation directed by boundary search to generate a carefully crafted test suite Dt (which we call ‘isotope’) based on Dp. With the test suite, TeDA then adopts membership inference combined with hypothesis testing to decide whether a user’s private data has been used to train Ma with statistical guarantee. We evaluated TeDA through extensive experiments on ranging data volumes across various model architectures for data-sensitive face recognition and medical diagnosis tasks. TeDA demonstrates high feasibility, effectiveness and robustness under various adaptive strategies (e.g., pruning and distillation).

Due to the vast testing space, the increasing demand for effective and efficient testing of deep neural networks (DNNs) has led to the development of various DNN test case prioritization techniques. However, the fact that DNNs can deliver high-confidence predictions for incorrectly predicted examples, known as the over-confidence problem, causes these methods to fail to reveal high-confidence errors. To address this limitation, in this work, we propose FAST, a method that boosts existing prioritization methods through guided FeAture SelecTion. FAST is based on the insight that certain features may introduce noise that affects the model's output confidence, thereby contributing to high-confidence errors. It quantifies the importance of each feature for the model's correct predictions, and then dynamically prunes the information from the noisy features during inference to derive a new probability vector for the uncertainty estimation. With the help of FAST, the high-confidence errors and correctly classified examples become more distinguishable, resulting in higher APFD (Average Percentage of Fault Detection) values for test prioritization, and higher generalization ability for model enhancement. We conduct extensive experiments to evaluate FAST across a diverse set of model structures on multiple benchmark datasets to validate the effectiveness, efficiency, and scalability of FAST compared to the state-of-the-art prioritization techniques.

This paper presents the design, implementation, and evaluation of MagWear, a novel biomagnetism-based system that can accurately and inclusively monitor the heart rate and respiration rate of mobile users with diverse skin tones. MagWear's contributions are twofold. Firstly, we build a mathematical model that characterizes the magnetic coupling effect of blood flow under the influence of an external magnetic field. This model uncovers the variations in accuracy when monitoring vital signs among individuals. Secondly, leveraging insights derived from this mathematical model, we present a softwarehardware co-design that effectively handles the impact of human diversity on the performance of vital sign monitoring, pushing this generic solution one big step closer to real adoptions. We have implemented a prototype of MagWear on a two-layer PCB board and followed IRB protocols to conduct system evaluations. Our extensive experiments involving 30 volunteers demonstrate that MagWear achieves high monitoring accuracy with a mean percentage error (MPE) of 1.55% for heart rate and 1.79% for respiration rate. The head-to-head comparison with Apple Watch 8 further demonstrates MagWear's consistently high performance in different user conditions.

Time-Sensitive Networking (TSN) offers deterministic communication for time-sensitive applications, using Cyclic Queuing and Forwarding (CQF) to manage time-triggered flows (TT flows). Scheduling TT flows is essential for optimizing network resource utilization and scheduling success rates. Existing works prefer heuristic algorithms due to their favorable trade off between computational overhead and performance. However, they overlook two critical factors during design: search space approximation efficiency and the impact of routing policy. In this study, we present H-GATS (Hybrid Genetic Algorithm and Tabu Search) for CQF-based TSN flow routing and scheduling. H-GATS combines the global search of Genetic Algorithms with the local search of Tabu Search, achieving fine-grained search efficiency and reduced execution time in complex networks. Moreover, H-GATS considers the offset and routing of flows, further improving scheduling performance. Compared to GA, Tabu, and JRS-LB, H-GATS is 5.8×, 3.05×, and 1.6× faster, respectively, in achieving the same success rates. Additionally, H-GATS improves the success rate by 5.5%, 9.6%, and 3.9% and enhances the resource utilization rate by 18%, 13.3%, and 3.3% over these baselines.

Zero-shot (ZS) 3D anomaly detection is a crucial yet unexplored field that addresses scenarios where target 3D training samples are unavailable due to practical concerns like privacy protection. This paper introduces PointAD, a novel approach that transfers the strong generalization capabilities of CLIP for recognizing 3D anomalies on unseen objects. PointAD provides a unified framework to comprehend 3D anomalies from both points and pixels. In this framework, PointAD renders 3D anomalies into multiple 2D renderings and projects them back into 3D space. To capture the generic anomaly semantics into PointAD, we propose hybrid representation learning that optimizes the learnable text prompts from 3D and 2D through auxiliary point clouds. The collaboration optimization between point and pixel representations jointly facilitates our model to grasp underlying 3D anomaly patterns, contributing to detecting and segmenting anomalies of unseen diverse 3D objects. Through the alignment of 3D and 2D space, our model can directly integrate RGB information, further enhancing the understanding of 3D anomalies in a plug-and-play manner. Extensive experiments show the superiority of PointAD in ZS 3D anomaly detection across diverse unseen objects.

The advancement of Industrial Internet-of-Things (IIoT) magnifies the cyber risk of automated production lines, especially to deception attacks that tamper with the monitoring data to prevent the manipulated operation of production lines from being detected. To address this issue, we propose Stochastic Manufacturing (StoM), a new paradigm of manufacturing that is resistant to deception by design. StoM voids the foundation of deception attacks — i.e., the highly predictable operation data due to the cyclical manufacturing process — by injecting controlled stochasticity into the operation of production lines without degrading manufacturing efficiency or quality. StoM then examines if this stochasticity can be observed from the operation data and triggers an alarm of deception attack if not. We have experimentally evaluated StoM on two production line platforms, showing StoM to detect deception attacks with a detection rate exceeding 99.1%, a false alarm rate below 0.1%, and a latency of less than 1.2 manufacturing cycles. Our empirical analysis also shows that it is highly impractical for attackers to spoof the controlled stochasticity.

It is essentially required to predict the ball’s flight trajectory accurately and timely for a robotic table tennis ball bouncing task. Existing solutions, which can be categorized into model-based and learning-based groups, both exhibits unpleasant disadvantages. For example, they often require to identify many dynamic parameters accurately or to collect extensive labeled data, which are generally very difficult or costly to achieve in real world. In this paper, we proposed a light-wight and rapid trajectory prediction approach for online table tennis bouncing tasks based on a simplified model. In the proposed approach, the ball’s flight poses are captured and estimated by a low-cost RGB-D camera. Then the ball’s landing position is predicted in advance by using a fitted 3D parabola. Compared with existing solutions, our proposed approach is lightweight and easy to deploy. In experiments, 66 flight trajectories of the ball are collected to serve as benchmark. The prediction errors for all landing positions are all less than 20mm, in which most of them are less than 10mm. In addition, the prediction can be achieved 141.7ms in advance, which is fast enough for the robotic arm to plan and move itself to the predicted landing point.

We study learning adversarial Markov decision process (MDP) in the episodic setting under the constraint of differential privacy (DP). This is motivated by the widespread applications of reinforcement learning (RL) in non-stationary and even adversarial scenarios, where protecting users’ sensitive information is vital. We first propose two efficient frameworks for adversarial MDPs, spanning full-information and bandit settings. Within each framework, we consider both Joint DP (JDP), where a central agent is trusted to protect the sensitive data, and Local DP (LDP), where the information is protected directly on the user side. Then, we design novel privacy mechanisms to privatize the stochastic transition and adversarial losses. By instantiating such privacy mechanisms to satisfy JDP and LDP requirements, we obtain near-optimal regret guarantees for both frameworks. To our knowledge, these are the first algorithms to tackle the challenge of private learning in adversarial MDPs.

Time-sensitive software-defined networking (TSSDN) is an emerging technology that combines the real-time network configuration capabilities of software-defined networking (SDN) with the deterministic flow delivery capabilities of time-sensitive networking (TSN), making it ideal for use in the Industrial Internet of Things (IIoT). However, as data flows generated by industrial applications grow exponentially, it is challenging to achieve low-latency and reliable data flow transmission at the same time in TSSDN due to the limited network resources. To address this issue, we propose the adoption of the frame replication and elimination for reliability (FRER) mechanism in TSSDN-based IIoT systems. However, it is important to acknowledge that the FRER mechanism introduces stress on the already restricted network resources by generating redundant paths. In light of this concern, we construct an end-to-end delay bound model and a reliability model to analyze this issue. To mitigate the stress imposed on the network, we formulate an optimization problem for maximizing the overall system utility while adhering to the transmission requirements of business flows and the limitations of hardware resources. Consequently, we devise an algorithm for reliability-enhanced flow routing and scheduling, which effectively solves the aforementioned optimization problem. To validate the effectiveness and performance of our proposed algorithm, we conduct numerical simulations on four data sets. The results demonstrate the superior performance of our approach compared to existing methods.

Traffic incident detection is a critical task within traffic monitoring systems, enabling on-the-fly alerts for emergency actions. Numerous efforts have been made to detect and localize traffic incidents using data recorded by inductive loop detectors. However, they only focus on the node-level incidents that happen within the surveillance areas and ignore the edge-level ones that take place outside of these areas. In this paper, we propose to detect both kinds of incidents simultaneously based on the sparsely distributed sensors. An important challenge is how to explicitly model the edge status and detect this kind of incidents. Additionally, capturing complex relationships among traffic dynamics, road locations, and temporal information is non-trivial. In this paper, we first describe the traffic dynamics by a fine-grained graph where the sensor range is designed as a hyper-parameter to control the coverage boundaries. Then, we propose an Edge-and Node-aware Dual AutoEncoder (ENDAE), where the correlations are decoupled into inter-nodes, inter-series and inter-attribute parts, which are further captured via node encoder, temporal encoder and attribute encoder, respectively. Furthermore, the reconstruction errors are calculated for node-level and edge-level event detection separately. The overall method is evaluated based on two real-world datasets from Bay Area and Los Angeles in California. ENDAE surpasses all the state-of-the-art method in both kinds of incidents, with at least 12.5% improvement in recall and 18.5% decrease in delay. Notably, for edge-level incidents, ENDAE achieves double the recall of the previous SOTA methods.

Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs). The adversarial distortion injected into the power signal will greatly affect the system’s normal control and operation. Therefore, it is imperative to conduct vulnerability assessment for MLsgAPPs applied in the safety-critical power systems. In this paper, we provide a comprehensive review of the recent progress in designing attack and defense methods for MLsgAPPs. Unlike the traditional survey about ML security, this is the first review work about the security of MLsgAPPs that focuses on the characteristics of power systems. We first highlight the specifics for constructing adversarial attacks on MLsgAPPs. Then, the vulnerability of MLsgAPP is analyzed from the perspective of the power system and ML model, respectively. Afterward, a comprehensive survey is conducted to review and compare existing studies about the adversarial attacks on MLsgAPPs in scenarios of generation, transmission, distribution, and consumption, and the countermeasures are reviewed according to the attacks that they defend against. Finally, the future research directions are discussed on the attacker’s and defender’s side, respectively. We also analyze the potential vulnerability of large language model-based (e.g., ChatGPT) smart grid applications. Overall, our purpose is to encourage more researchers to contribute to investigating the adversarial issues of MLsgAPPs.

The COVID-19 pandemic has challenged countries worldwide to strike a balance between implementing epidemic control measures and maintaining economic activity. In response, many countries have adopted sustainable, precise, region-specific, and multilevel prevention and control measures. To apply these measures more effectively and purposefully, it is imperative to quantify their impact on the transmission of COVID-19 within urban areas. Here, we propose a dynamic metapopulation susceptible-exposed-infectious-removed (SEIR) model that incorporates the urban mobility network to simulate the spread of COVID-19 in Beijing and investigate the effects of precise intervention measures. Our proposed model accurately fits the real epidemic trajectory, even with the significant changes in human mobility patterns before and after the epidemic. Additionally, it can also serve as a useful policy evaluation tool by simulating the impact of perturbations in mobility networks on epidemic transmission dynamics. Based on this tool, our results demonstrate that point-of-interest capacity limitation measures can significantly reduce the number of infections with only a minor loss of urban mobility. Furthermore, we show that community dynamic management measures can effectively control and mitigate COVID-19 spread while enabling the normal operation of most economic and social activities. By quantifying the impact of precise intervention measures on new infections and mobility losses, our model enables a cost-benefit analysis of these measures, thus informing targeted and sustainable policy responses to COVID-19.

In response to the repeated outbreaks of the COVID-19, many countries implement the region-specific, multilevel epidemic prevention and control policies. To fully understand the impact of these interventions on urban mobility, it is urgent to analyze spatial–temporal mobility pattern at the neighborhood level and structural changes in urban mobility networks. Here, we construct urban mobility networks among points of interest (POIs), using large-scale anonymous mobility data from de-identified mobile phone users. We comprehensively investigate the changes of urban mobility networks during two waves of the COVID-19 pandemic in Beijing from both graph and subgraph perspectives. Beyond an overall mobility reduction in Beijing, we find that the mobility change is spatially and temporally heterogeneous among different urban regions. We uncover a disproportionately large reduction in long-distance, nighttime, and non-essential travel. This results in a more geographically fragmented, local, and regional network in the pandemic. We demonstrate that these structural changes slow down the spatial spread of the COVID-19 in the mobility network.

Neural Architecture Search (NAS) is a powerful tool for automating effective image and video processing DNN designing. The ranking of the accuracy has been advocated to design an efficient performance predictor for NAS. The previous contrastive method solves the ranking problem by comparing pairs of architectures and predicting their relative performance. However, it only focuses on the rankings between the two involved architectures and neglects the overall quality distributions of the search space, which may suffer generalization issues. On the contrary, we propose to let the performance predictor concentrate on the global quality level of specific architecture, and learn the tier embeddings of the whole search space automatically with learnable queries. The proposed method, dubbed as Neural Architecture Ranker with Query-to-Tier technique (NARQ2T), explores the quality tiers of the search space globally and classifies each individual to the tier they belong to. Thus, the predictor gains knowledge of the performance distributions of the search space which helps to generalize its ranking ability to the datasets more easily. Thanks to the encoder-decoder design, our method is able to predict the latency of the searched model without deteriorating the performance prediction. Meanwhile, the global quality distribution facilitates the search phase by directly sampling candidates according to the statistics of quality tiers, which is free of training a search algorithm, e.g., Reinforcement Learning or Evolutionary Algorithm, thus it simplifies the NAS pipeline and saves the computational overheads. The proposed NARQ2T achieves state-of-the-art performance on two widely used datasets for NAS research. Moreover, extensive experiments have validated the efficacy of the designed method.

The Metaverse refers to the integration of physical and virtual realities, offering new possibilities for enhancing operations and services across various industries. However, its application in the energy sector is still in its nascent stage. The energy industry, crucial for the global economy and society, faces significant challenges due to its complex and risky nature, such as health, safety, and environmental (HSE) concerns, and the remote locations of extraction sites. Although some studies have explored the use of the Metaverse in this industry for data visualization, energy process modeling, and training, a comprehensive review of existing technologies and applications is lacking. This article addresses this gap by examining the potential of the Metaverse for the energy industry, using the Oil&Gas sector as a case study. We identify the essential technologies needed to create a realistic and immersive Metaverse experience and review the current literature on its industrial applications in the energy sector. Distinguishing our work from others, we present four practical case studies developed from our own experience in the Oil&Gas sector. These cases demonstrate the tangible benefits of Metaverse solutions in improving operational efficiency, reducing costs, and enhancing worker safety and productivity, highlighting the unique value of the Metaverse in addressing industry-specific challenges.

Federated learning (FL) has emerged as a privacy-aware collaborative learning paradigm where participants jointly train a powerful model without sharing their private data. One desirable property for FL is the implementation of theright to be forgotten (RTBF), i.e., a leaving participant has the right to request the deletion of its private data from the global model. However,unlearning itself may not be enough to implement RTBF unless the unlearning effect can be independently verified, an important aspect that has been overlooked in the current literature. Unlearning verification is particularly challenging in FL as the unlearning effect on one participant's data could be canceled by the contribution of other participants. In this work, we prompt the concept ofverifiable federated unlearningand proposeVeriFi, a unified framework that allows systematic analysis of federated unlearning and quantification of its effect, with different combinations of various unlearning and verification methods. InVeriFi, the leaving participant is granted theright to verify (RTV)to actively verify the unlearning effect in the next few rounds immediately after notifying the server of its intention to leave, along with local verification done through two steps: 1)markingthat fingerprints the leaving participant by specially-designedmarkersand 2)checkingthat examines the global model's performance change on the markers. Based onVeriFi, we have conducted so far the most systematic study on verifiable federated unlearning, covering six unlearning methods and five verification methods. Our study sheds light on the existing drawbacks and potential alternatives for both unlearning and verification methods. During the study, we also propose a more efficient and FL-friendly unlearning method$^{u}$S2U, and two more effective and robust non-invasive (without training controllability, external data, white-box model access nor introducing new security risks) verification methods$^{v}$FM and$^{v}$EM. While the proposed methods may not be a panacea for all the challenges, they address several key drawbacks of existing methods and represent a promising step toward effective, efficient, robust, and more importantly, non-invasive federated unlearning and verification. We extensively evaluateVeriFion seven datasets, including natural/facial/medical images and audios, and four types of deep learning models, including both Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). We hope, such an extensive and holistic experimental evaluation, although admittedly complex and challenging, could help establish important empirical understandings, evidence, and insights for trustworthy federated unlearning.

Smart speakers are widely used as the primary user interface in intelligent systems, including smart homes and industrial IoT. However, they are vulnerable to voice spoofing attacks which result in malicious command execution or privacy information leakage. Passive liveness detection, which thwarts voice spoofing via analyzing the collected audio rather than deploying sensors to distinguish between live-human and spoofing voices, has drawn increasing attention. But existing schemes either face performance degradation under environmental factor changes or require the user to keep fixed gestures, which limit their deployment in real-world scenarios. Besides, the space distributed property of smart speakers causes building a universal classifier for all involved users to be cumbersome and increases privacy leakage issues. To address the challenges mentioned above, we propose LIVEARRAY, an efficient, lightweight, and privacy-preserving passive liveness detection system. LIVEARRAY exploits a novel liveness feature, array fingerprint, which utilizes the microphone array inherently adopted by the smart speaker to improve the accuracy of liveness detection. LIVEARRAY's further employs the federated learning-based architecture to reduce the dataset collection overhead during classifier building and eliminate the potential privacy leakage during data transmission. Experimental results show that LIVEARRAY achieves an accuracy of 99.16%, which is superior to existing passive schemes

The abuse of drones has raised critical concerns about public security and personal privacy, bringing an urgent requirement for drone recognition. Existing radio frequency (RF)-based recognition methods follow the assumption of the closed set, resulting in the unknown signals being misclassified as known classes. To address this problem, we propose a Signal Semantic-based open Set Recognition (S3R) method in this paper. First, the short-time Fourier transform is introduced to construct the signal spectra, decoupling the drone signals with other interference signals. Then, we design a texture extractor and a position extractor to extract the texture features and position features from the spectra, respectively. The extracted features are further fused and structurally optimized to construct distinguishable signal semantics. Based on the structural characteristics of signal semantics, an outlier analysis-based semantic classifier is proposed, which searches the outliers of each known class in the closed set as the bounding thresholds to detect unknown instances. Finally, the detected unknown instances are further classified into their exact classes by implementing clustering in a new semantic space, where semantics are augmented by introducing basic features from the intermediate layers of the texture extractor. Besides, a real-world spectrogram dataset of commonly-used drones is released, which includes 24 classes and covers 7 brands. Extensive experiments demonstrate that the proposed S3R method outperforms the state-of-the-art methods in terms of accuracy and generalizability for both the closed set and the open set.

We consider signal source localization from range-difference measurements. First, we give some readily-checked conditions on measurement noises and sensor deployment to guarantee the asymptotic identifiability of the model and show the consistency and asymptotic normality of the maximum likelihood (ML) estimator. Then, we devise an estimator that owns the same asymptotic property as the ML one. Specifically, we prove that the negative log-likelihood function converges to a function, which has a unique minimum and positive definite Hessian at the true source’s position. Hence, it is promising to execute local iterations, e.g., the Gauss-Newton (GN) algorithm, following a consistent estimate. The main issue involved is obtaining a preliminary consistent estimate. To this aim, we construct a linear least-squares problem via algebraic operation and constraint relaxation and obtain a closed-form solution. We then focus on deriving and eliminating the bias of the linear least-squares estimator, which yields an asymptotically unbiased and further consistent estimate. Noting that the bias is a function of the noise variance, we further devise a consistent noise variance estimator that involves a 3-order polynomial rooting. Based on the preliminary consistent location estimate, a one-step GN iteration suffices to achieve the same asymptotic property as the ML estimator. Simulation results demonstrate the superiority of our proposed algorithm in the large sample case.

Wind speed forecasting provides the upcoming wind information and is important to the safe operation of High-Speed Railway (HSR). However, it remains a challenge due to the stochastic and highly varying characteristics of wind. In this paper, we propose a novel Transformer-based method for short-term wind speed forecasting, named WindTrans. Two major cruxes are addressed. First, the task is performed on fine-grained wind speed gathered from multiple sensors. These data present dynamic intra-series and inter-series correlations, which are hard for previous methods to recover. We advance a Transformer-based deep learning model, which has two distinctive characteristics: (1) a graph encoder, which captures the dynamic spatial correlation among wind speeds at different locations, and (2) a temporal decoder to model long sequence wind speed time series, which is resistant to noise in time series. Second, wind speed patterns gradually evolve in long-term periods, thus deactivating prediction models trained on historical data. To tackle this bottleneck, we put forward an experience replay-based scheme to renew the model regularly. To ensure that the renewed model still dominates historical wind patterns, we store and replay only a small portion of historical data named episodic memory. A simple but efficient strategy is designed to constitute episodic memory and thus relieve the computation burden. Experiments conducted on two real-world datasets demonstrate the superiority of our method over existing approaches. Particularly, WindTrans surpasses state-of-the-art methods by up to 36.7%, 29.3% and 13.3% improvement in MAPE measure for 1 hour ahead prediction on 10-minute, 5-minute, and 1-minute-based tasks, respectively. Furthermore, via our continual learning scheme, the model retains competitive performance with only 6.9% datum stored and retrained on.

Effectively predicting the evolution of COVID-19 is of great significance to contain the pandemic. Extensive previous studies proposed a great number of SIR variants, which are efficient to capture the transmission characteristics of COVID-19. However, the parameter estimation methods in previous studies are based on data from epidemiological investigations, which inevitably have caused a large delay. The popularity of digital trajectory data world-wide makes it possible to understand epidemic spreading from human mobility perspective. The major advantage of digital trajectory data lies in that the co-location level of a population is reflected at every moment, making it possible to forecast the evolution in advance. We showed that the mobility data contributed by mobile phone users could be exploited to estimate the contact probability between individuals, thus revealing the dynamic transmission of COVID-19. Specifically, we developed an estimation method to obtain human co-location levels and quantified the variations of human mobility during the epidemic. Then, we extended the infection rate with a real-time co-location level to further forecast the transmission of an epidemic, predicting the epidemic size much more accurately than conventional methods. Finally, the proposed method was applied to evaluate the quantitative effect of different non-pharmacological interventions by predicting the epidemic situations with various mobility characteristics. The empirical results and simulations corroborated our theoretical analysis, providing effective guidance to contain the pandemic.

Despite the substantial progress in predicting human mobility, most existing methods fail to reveal the spatiotemporal patterns under significant interventions such as COVID-19, which disrupt the routine of human mobility. To fill this gap, this paper presents a unified framework for learning human mobility in both regular and intervened scenarios through explicit modeling of the intervention and the intervened system. To be concrete, we design a novel Deep State-Space Model (DSSM) called AGC-ODE: Adaptive Graph Controlled Neural Ordinary Differential Equation for human mobility prediction during COVID-19. The transition equation that describes continuous-time dynamics of human mobility is parameterized with a graph-controlled Neural ODE, and the latent control that guides the equation propagating is inferred through the multi-head gating filters. Additionally, an information capacity constraint is applied to foster the disentanglement of interventions. Lastly, AGC-ODE utilizes a data-driven initialization strategy to improve DSSM’s initial state estimation. We conduct extensive experiments and analysis on two real-world datasets of Beijing and the U.S. to demonstrate the superiority and interpretability of our model. Furthermore, we introduce a deployed system that is based on AGC-ODE and how it helps epidemic prevention during the COVID era and work resumption in the post-COVID era.

Anomaly detection in multivariate time series has been widely studied in one-class classification (OCC) setting. The training samples in this setting are assumed to be normal. In more practical situations, it is difficult to guarantee that all samples are normal. Meanwhile, preparing a completely clean training dataset is costly and laborious. Such a case may degrade the performance of OCC-based anomaly detection methods which fit the training distribution as the normal distribution. To overcome this limitation, in this paper, we propose MTGFlow, an unsupervised anomaly detection approach for Multivariate Time series anomaly detection via dynamic Graph and entity-aware normalizing Flow. MTGFlow first estimates the density of the entire training samples and then identifies anomalous instances based on the density of the test samples within the fitted distribution. This relies on a widely accepted assumption that anomalous instances exhibit more sparse densities than normal ones, with no reliance on the clean training dataset. However, it is intractable to directly estimate the density due to the complex dependencies among entities and their diverse inherent characteristics, not to mention detecting anomalies based on the estimated distribution. In order to address these problems, we utilize the graph structure learning model to learn interdependent and evolving relations among entities, which effectively captures the complex and accurate distribution patterns of multivariate time series. In addition, our approach incorporates the unique characteristics of individual entities by employing an entity-aware normalizing flow. This enables us to represent each entity as a parameterized normal distribution. Furthermore, considering that some entities present similar characteristics, we propose a cluster strategy that capitalizes on the commonalities of entities with similar characteristics, resulting in more precise and detailed density estimation. We refer to this cluster-aware extension as MTGFlow_cluster. Extensive experiments are conducted on six widely used benchmark datasets, in which MTGFlow and MTGFlow_cluster demonstrate their superior detection performance.

With the increasing proliferation of Internet-of-Things (IoT) devices, there is a growing trend towards distributing the power of deep learning (DL) among edge devices rather than centralizing it at the cloud. To deploy deep and complex models at edge devices with limited resources, model partitioning of deep neural network (DNN) models has been widely studied. However, most of the existing literature only considers distributing the inference model while still training the model at the cloud. In this paper, we propose FTPipeHD, a novel DNN training approach that trains DNN models across distributed heterogeneous devices with the fault-tolerance mechanism. To accelerate the training with the time-varying computing power of each device, we optimize the partition points dynamically according to real-time computing capacities. We also propose a novel weight redistribution approach that replicates the weights to both the neighboring nodes and the central node periodically, which combats the failure of multiple devices during training while incurring limited communication costs. Our numerical results demonstrate that FTPipeHD is 6.8 times faster in training than the state-of-the-art method when the computing capacity of the best device is 10 times greater than the worst one. It is also shown that the proposed method is able to accelerate the training even with the existence of device failures.

It is usually infeasible to fit and train an entire large deep neural network (DNN) model using a single edge device due to the limited resources. To facilitate intelligent applications across edge devices, researchers have proposed partitioning a large model into several sub-models, and deploying each of them to a different edge device to collaboratively train a DNN model. However, the communication overhead caused by the large amount of data transmitted from one device to another during training, as well as the sub-optimal partition point due to the inaccurate latency prediction of computation at each edge device can significantly slow down training. In this paper, we propose AccEPT, an acceleration scheme for accelerating the edge collaborative pipeline-parallel training. In particular, we propose a light-weight adaptive latency predictor to accurately estimate the computation latency of each layer at different devices, which also adapts to unseen devices through continuous learning. Therefore, the proposed latency predictor leads to better model partitioning which balances the computation loads across participating devices. Moreover, we propose a bit-level computation-efficient data compression scheme to compress the data to be transmitted between devices during training. Our numerical results demonstrate that our proposed acceleration approach is able to significantly speed up edge pipeline parallel training up to 3 times faster in the considered experimental settings

This paper presents the design, implementation, and evaluation of MagWear, a novel biomagnetism-based system that can accurately and inclusively monitor the heart rate, respiration rate, and blood pressure of users. MagWear's contributions are twofold. First, we build a mathematical model that characterizes the magnetic coupling effect of blood flow under the influence of an external magnetic field. This model uncovers the variations in accuracy when monitoring vital signs among individuals. Second, leveraging insights derived from this mathematical model, we present a software-hardware co-design that effectively handles the impact of human diversity on the performance of vital sign monitoring, pushing this generic solution one big step closer to real adoptions. Following IRB protocols, our extensive experiments involving 30 volunteers demonstrate that MagWear achieves high monitoring accuracy with a mean percentage error (MPE) of 1.55% for heart rate (HR), 1.79% for respiration rate (RR), 3.35% for systolic blood pressure (SBP), and 3.89% for diastolic blood pressure (DBP). MagWear can also be extended to detect anemia and blood oxygen saturation, which is also our ongoing work.

In the past few years, dockless bike-sharing has become a popular means of public transportation and brought significant convenience to millions of citizens. As one of the key components of a shared bike, the smart locking/unlocking module has proposed a new challenge of how to provide robust power supplement for them. Current charging solutions for shared bikes are mainly based on mechanical power and solar power, and rarely take user experience and charging delay into consideration. In this article, we design a robust RF-based wireless charging system for dockless bike-sharing. Our system utilizes radio frequency (RF) power to provide stable charging service while preserving the quality of service. In our system, an RF wireless charging sensing node is integrated on the bike's basket, so that the mutual interference during charging process and space occupation can be reduced. In order to reduce charging delay, we first design an efficient charging direction scheduling algorithm for a single charger. Then, we extend the solution to multiple-charger scenarios via dynamic programming. Our system has been successfully implemented on a dockless bike-sharing system. The experimental results verify that our design can satisfy the charging demands of shared-bikes and achieve 85% of the optimal solution.

Vertical federated learning (VFL) revolutionizes privacy-preserved collaboration for small businesses that have distinct but complementary feature sets. However, as the scope of VFL expands, the constant entering and leaving of participants and the subsequent exercise of the “right to be forgotten” pose a great challenge in practice. The question of how to efficiently erase one’s contribution from the shared model remains largely unexplored in the context of VFL. In this article, we introduce a vertical federated unlearning framework, which integrates model checkpointing techniques with a hybrid, first-order optimization technique. The core concept is to reduce backpropagation time and improve convergence/generalization by combining the advantages of the existing optimizers. We provide in-depth theoretical analysis and time complexity to illustrate the effectiveness of the proposed design. We conduct extensive experiments on six public datasets and demonstrate that our method could achieve up to 6.3× speedup compared to the baseline, with negligible influence on the original learning task.

Covert channel, which can break the logical protections of the computer system and leak confidential or sensitive information, has long been considered a security issue in the network research community. However, recent research has shown that cooperative agents can use the “covert” channel to augment the communication of legitimate applications, rather than by adversaries seeking to compromise computer security. This further broadens the potential applications of covert channels. Despite this, the design and implementation of covert channels in the context of Low Power Wide Area Networks (LPWANs) have not been widely discussed. Current state-of-the-art uses On-off keying (OOK) on LoRa PHY to create a covert channel, but this channel has limited transmission distance and capacity. In this paper, we proposeLoPhy, a resilient and fast covert channel over LoRa physical layer (PHY).LoPhyuses the Chirp Spreading Spectrum (CSS) modulation scheme to increase its resilience and explore the trade-off between the covert channel’s capacity and the legitimate channel’s resilience. We implement the proposed covert channel on off-the-shelf devices and software-defined radios and show thatLoPhyachieves a 0.57% bit error rate at a distance of$700\,\text {m}$with slight impact on legitimate channel’s performance. Moreover, we present two applications enabled byLoPhyto demonstrate the potential ofLoPhy. Compared with the state-of-the-art,LoPhybrings up to$18\times $reduction of bit errors and$63\times $gain on noise resilience.

The Virtual Private Cloud (VPC) service enables users to configure shared resources within public clouds on demand, providing isolation between users. However, configuring the VPC network is a complex and error-prone task, and misconfiguration has been the leading cause of cloud network security issues. The large number of complex network components and configurations makes it difficult to perform scalable, efficient, and accurate fault verification of the network behavior. To address this issue, we design a comprehensive and automated fault diagnosis and localization tool, calledLFVeri, which is built upon an innovative modular network model that accurately captures the logic functions of real components within VPC networks, and propose eleven functions to verify network reachability and security requirements. We conduct performance testing ofLFVerion various datasets and compared it with other verification tools. The experiments show thatLFVerioutperforms in modeling and analyzing real VPC scenarios while also possessing the fastest verification speed. It can model and analyze large VPC networks with tens of thousands of components and millions of configuration rules in less than half an hour.

Flow scheduling plays a pivotal role in enabling Time-Sensitive Networking (TSN) applications. Current flow scheduling mainly adopts a centralized scheme, posing challenges in adapting to dynamic network conditions and scaling up for larger networks. To address these challenges, we first thoroughly analyze the flow scheduling problem and find the inherent locality nature of time scheduling tasks. Leveraging this insight, we introduce the first distributed framework for IEEE 802.1Qbv TSN flow scheduling. In this framework, we further propose a multi-agent flow scheduling method by designing Deep Reinforcement Learning (DRL)-based route and time agents for route and time planning tasks. The time agents are deployed on field devices to schedule flows in a distributed way. Evaluations in dynamic scenarios validate the effectiveness and scalability of our proposed method. It enhances the scheduling success rate by 20.31% compared to state-of-the-art methods and achieves substantial cost savings, reducing transmission costs by 410× in large-scale networks. Additionally, we validate our approach on edge devices and a TSN testbed, highlighting its lightweight nature and ease of deployment.

Multivariate time series anomaly detection has been extensively studied under the one-class classification setting, where a training dataset with all normal instances is required. However, preparing such a dataset is very laborious since each single data instance should be fully guaranteed to be normal. It is, therefore, desired to explore multivariate time series anomaly detection methods based on the dataset without any label knowledge. In this paper, we propose MTGFlow, an unsupervised anomaly detection approach forMultivariate Time series anomaly detection via dynamic Graph and entityaware normalizing Flow, leaning only on a widely accepted hypothesis that abnormal instances exhibit sparse densities than the normal. However, the complex interdependencies among entities and the diverse inherent characteristics of each entity pose significant challenges to density estimation, let alone to detect anomalies based on the estimated possibility distribution. To tackle these problems, we propose to learn the mutual and dynamic relations among entities via a graph structure learning model, which helps to model the accurate distribution of multivariate time series. Moreover, taking account of distinct characteristics of the individual entities, an entity-aware normalizing flow is developed to describe each entity into a parameterized normal distribution, thereby producing fine-grained density estimation. Incorporating these two strategies, MTGFlow achieves superior anomaly detection performance. Experiments on five public datasets with seven baselines are conducted, MTGFlow outperforms the SOTA methods by up to 5.0 AUROC%.

Programmable logic controllers (PLCs), the essential components of critical infrastructure, play a crucial role in various industrial manufacturing processes. Recent attack events show that attackers have a strong interest in tampering with the controller variables, such as the device status and internal program logic. A typical attack strategy is that the attackers just send malicious network traffic of industrial control protocols (ICPs) to change the controller variables of PLCs. To defend against this attack, a lot of countermeasures have been proposed to detect anomalies in network traffic based on the semantic analysis.

In recent years, semantic communication has been a popular research topic for its superiority in communication efficiency. As semantic communication relies on deep learning to extract meaning from raw messages, it is vulnerable to attacks targeting deep learning models. In this paper, we introduce the model inversion eavesdropping attack (MIEA) to reveal the risk of privacy leaks in the semantic communication system. In MIEA, the attacker first eavesdrops the signal being transmitted by the semantic communication system and then performs model inversion attack to reconstruct the raw message, where both the white-box and black-box settings are considered. Evaluation results show that MIEA can successfully reconstruct the raw message with good quality under different channel conditions. We then propose a defense method based on random permutation and substitution to defend against MIEA in order to achieve secure semantic communication. Our experimental results demonstrate the effectiveness of the proposed defense method in preventing MIEA.

With the popularity of implicit neural representations, or neural radiance fields (NeRF), there is a pressing need for editing methods to interact with the implicit 3D models for tasks like post-processing reconstructed scenes and 3D content creation. While previous works have explored NeRF editing from various perspectives, they are restricted in editing flexibility, quality, and speed, failing to offer direct editing response and instant preview. The key challenge is to conceive a locally editable neural representation that can directly reflect the editing instructions and update instantly. To bridge the gap, we propose a new interactive editing method and system for implicit representations, called Seal-3D1, which allows users to edit NeRF models in a pixel-level and free manner with a wide range of NeRF-like backbone and preview the editing effects instantly. To achieve the effects, the challenges are addressed by our proposed proxy function mapping the editing instructions to the original space of NeRF models in the teacher model and a two-stage training strategy for the student model with local pretraining and global finetuning. A NeRF editing system is built to showcase various editing types. Our system can achieve compelling editing effects with an interactive speed of about 1 second.

Multi-object tracking (MOT) aims to build moving trajectories for number-agnostic objects. Modern multi-object trackers commonly follow the tracking-by-detection strategy. Therefore, fooling detectors can be an effective solution but it usually requires attacks in multiple successive frames, resulting in low efficiency. Attacking association processes improves efficiency but may require model-specific design, leading to poor generalization. In this paper, we propose a novel False negative and False positive attack (F&F attack) mechanism: it perturbs the input image to erase original detections and to inject deceptive false alarms around original ones while integrating the association attack implicitly. The mechanism can produce effective identity switches against multi-object trackers by only fooling detectors in a few frames. To demonstrate the flexibility of the mechanism, we deploy it to three multi-object trackers (ByteTrack, SORT, and CenterTrack) which are enabled by two representative detectors (YOLOX and CenterNet). Comprehensive experiments on MOT17 and MOT20 datasets show that our method significantly outperforms existing attackers, revealing the vulnerability of the tracking-by-detection paradigm to detection attacks.

3D human reconstruction from RGB images achieves decent results in good weather conditions but degrades dramatically in rough weather. Complementary, mmWave radars have been employed to reconstruct 3D human joints and meshes in rough weather. However, combining RGB and mmWave signals for robust all-weather 3D human reconstruction is still an open challenge, given the sparse nature of mmWave and the vulnerability of RGB images. In this paper, we present ImmFusion, the first mmWave-RGB fusion solution to reconstruct 3D human bodies in all weather conditions robustly. Specifically, our ImmFusion consists of image and point backbones for token feature extraction and a Transformer module for token fusion. The image and point backbones refine global and local features from original data, and the Fusion Transformer Module aims for effective information fusion of two modalities by dynamically selecting informative tokens. Extensive experiments on a large-scale dataset, mmBody, captured in various environments demonstrate that ImmFusion can efficiently utilize the information of two modalities to achieve a robust 3D human body reconstruction in all weather conditions. In addition, our method's accuracy is significantly superior to that of state-of-the-art Transformer-based LiDAR-camera fusion methods.

Implicit neural representations have shown promising potential for 3D scene reconstruction. Recent work applies it to autonomous 3D reconstruction by learning information gain for view path planning. Effective as it is, the computation of the information gain is expensive, and compared with that using volumetric representations, collision checking using the implicit representation for a 3D point is much slower. In the paper, we propose to 1) leverage a neural network as an implicit function approximator for the information gain field and 2) combine the implicit fine-grained representation with coarse volumetric representations to improve efficiency. Further with the improved efficiency, we propose a novel informative path planning based on a graph-based planner. Our method demonstrates significant improvements in the reconstruction quality and planning efficiency compared with autonomous reconstructions with implicit and explicit representations. We deploy the method on a real UAV and the results show that our method can plan informative views and reconstruct a scene with high quality.

3D grasp synthesis generates grasping poses given an input object. Existing works tackle the problem by learning a direct mapping from objects to the distributions of grasping poses. However, because the physical contact is sensitive to small changes in pose, the high-nonlinear mapping between 3D object representation to valid poses is considerably non-smooth, leading to poor generation efficiency and restricted generality. To tackle the challenge, we introduce an intermediate variable for grasp contact areas to constrain the grasp generation; in other words, we factorize the mapping into two sequential stages by assuming that grasping poses are fully constrained given contact maps: 1) we first learn contact map distributions to generate the potential contact maps for grasps; 2) then learn a mapping from the contact maps to the grasping poses. Further, we propose a penetration-aware optimization with the generated contacts as a consistency constraint for grasp refinement. Extensive validations on two public datasets show that our method outperforms state-of-the-art methods regarding grasp generation on various metrics.

Covert channel, which can break the logical protections of the computer system and leak confidential or sensitive information, has long been considered a security issue in the network research community. However, recent research has shown that cooperative agents can use the "covert" channel to augment the communication of legitimate applications, rather than by adversaries seeking to compromise computer security. This further broadens the potential applications of covert channels. Despite this, the design and implementation of covert channels in the context of Low Power Wide Area Networks (LPWANs) have not been widely discussed. Current state-of-the-art uses On-off keying (OOK) on LoRa PHY to create a covert channel, but this channel has limited transmission distance and capacity. In this paper, we propose LoPhy, a resilient and fast covert channel over LoRa physical layer (PHY). LoPhy uses the Chirp Spreading Spectrum (CSS) modulation scheme to increase its resilience and explore the trade-off between the covert channel’s capacity and the legitimate channel’s resilience. We implement the proposed covert channel on off-the-shelf devices and software-defined radios and show that LoPhy achieves a 0.57% bit error rate at a distance of 700 m without affecting the legitimate channel’s performance. Moreover, we present two applications enabled by LoPhy to demonstrate the potential of LoPhy. Compared with the state-of-the-art, LoPhy brings up to 18 × reduction of bit errors and 63 × gain on noise resilience.

Robotic dexterous grasping is a challenging problem due to the high degree of freedom (DoF) and complex contacts of multi-fingered robotic hands. Existing deep re-inforcement learning (DRL) based methods leverage human demonstrations to reduce sample complexity due to the high dimensional action space with dexterous grasping. However, less attention has been paid to hand-object interaction representations for high-level generalization. In this paper, we propose a novel geometric and spatial hand-object interaction representation, named DexRep, to capture object surface features and the spatial relations between hands and objects during grasping. DexRep comprises Occupancy Feature for rough shapes within sensing range by moving hands, Surface Feature for changing hand-object surface distances, and LocalGeo Feature for local geometric surface features most related to potential contacts. Based on the new representation, we propose a dexterous deep reinforcement learning method DexRepNet to learn a generalizable grasping policy. Experimental results show that our method outperforms baselines using existing representations for robotic grasping dramatically both in grasp success rate and convergence speed. It achieves a 93% grasping success rate on seen objects and higher than 80% grasping success rates on diverse objects of unseen categories in both simulation and real-world experiments.

Understanding human interaction with objects is an important research topic for embodied Artificial Intelligence and identifying the objects that humans are interacting with is a primary problem for interaction understanding. Existing methods rely on frame-based detectors to locate interacting objects. However, this approach is subjected to heavy occlusions, background clutter, and distracting objects. To address the limitations, in this paper, we propose to leverage spatio-temporal information of hand-object interaction to track interactive objects under these challenging cases. Without prior knowledge of the general objects to be tracked like object tracking problems, we first utilize the spatial relation between hands and objects to adaptively discover the interacting objects from the scene. Second, the consistency and continuity of the appearance of objects between successive frames are exploited to track the objects. With this tracking formulation, our method also benefits from training on large-scale general object-tracking datasets. We further curate a video-level hand-object interaction dataset for testing and evaluation from 100DOH. The quantitative results demonstrate that our proposed method outperforms the state-of-the-art methods. Specifically, in scenes with continuous interaction with different objects, we achieve an impressive improvement of about 10% as evaluated using the Average Precision (AP) metric. Our qualitative findings also illustrate that our method can produce more continuous trajectories for interacting objects.

Autonomous drone racing is becoming an excellent platform to challenge quadrotors' autonomy techniques including planning, navigation and control technologies. However, most research on this topic mainly focuses on single drone scenarios. In this paper, we describe a novel time-optimal trajectory generation method for generating time-optimal trajectories for a swarm of quadrotors to fly through pre-defined waypoints with their maximum maneuverability without collision. We verify the method in the Gazebo simulations where a swarm of 5 quadrotors can fly through a complex 6-waypoint racing track in a$35m\times 35m$space with a top speed of 14m/s. Flight tests are performed on two quadrotors passing through 3 waypoints in a$4m\times 2m$flight arena to demonstrate the feasibility of the proposed method in the real world. Both simulations and real-world flight tests show that the proposed method can generate the optimal aggressive trajectories for a swarm of autonomous racing drones. The method can also be easily transferred to other types of robot swarms.

Human activity recognition (HAR) will be an essential function of various emerging applications. However, HAR typically encounters challenges related to modality limitations and label scarcity, leading to an application gap between current solutions and real-world requirements. In this work, we propose MESEN, a multimodal-empowered unimodal sensing framework, to utilize unlabeled multimodal data available during the HAR model design phase for unimodal HAR enhancement during the deployment phase. From a study on the impact of supervised multimodal fusion on unimodal feature extraction, MESEN is designed to feature a multi-task mechanism during the multimodal-aided pre-training stage. With the proposed mechanism integrating cross-modal feature contrastive learning and multimodal pseudo-classification aligning, MESEN exploits unlabeled multimodal data to extract effective unimodal features for each modality. Subsequently, MESEN can adapt to downstream unimodal HAR with only a few labeled samples. Extensive experiments on eight public multimodal datasets demonstrate that MESEN achieves significant performance improvements over state-of-the-art baselines in enhancing unimodal HAR by exploiting multimodal data.

Semantic communication has emerged as a promising approach for improving efficient transmission in the next generation of wireless networks. Inspired by the success of semantic communication in different areas, we aim to provide a new semantic communication scheme from the semantic level. In this paper, we propose a novel DL-based semantic communication system for video transmission, which compacts semantic-related information to improve transmission efficiency. In particular, we utilize the Bi-optical flow to estimate residual information of inter-frame details. We also propose a feature choice module and a feature fusion module to drop semantically redundant features while paying more attention to the important semantic-related content. We employ a frame prediction module to reconstruct semantic features of the prediction frame from the received signal at the receiver. To enhance the system’s robustness, we propose a noise attention module that assigns different importance weights to the extracted features. Simulation results indicate that our proposed method outperforms existing approaches in terms of transmission efficiency, achieving about 33.3% reduction in the number of transmitted symbols while improving the peak signal-to-noise ratio (PSNR) performance by an average of 0.56dB.

Recently, much attention has been paid to segmenting subtle unknown defect regions by knowledge distillation in an unsupervised setting. Most previous studies concentrated on guiding the student network to learn the same representations on the normality, neglecting the different behaviors of the abnormality. This leads to a high probability of false detection of subtle defects. To address such an issue, we propose to push representations on abnormal areas of the teacher and student network as far as possible while pulling representations on normal areas as close as possible. Based on this idea, we design an efficient teacher-student model for anomaly detection and localization, which maximizes pixel-wise discrepancies for anomalous regions approximated by data augmentation and simultaneously minimizes discrepancies for pixel-wise normal regions between these two networks. The explicit differential knowledge distillation enlarges the margin between normal representations and abnormal ones in favour of discriminating them. Then, the appropriate small student network is not only efficient, but more importantly, helps inhibit the generalization ability of anomalous patterns when learning normal patterns, facilitating the precise decision boundary. The experimental results on the MVTec AD, Fashion-MNIST, and CIFAR-10 datasets demonstrate that our proposed method achieves better performance than current state-of-the-art (SOTA) approaches. Especially, For the MVTec AD dataset with high resolution images, we achieve 98.1 AUROC% and 93.6 AUPRO% in anomaly localization, outperforming knowledge distillation based SOTA methods by 1.1 AUROC% and 1.5 AUPRO% with a lightweight model.

Time-sensitive networking (TSN), an emerging network technology, requires high-performance scheduling mechanisms to deliver deterministic service in Industry 5.0. Cyclic queuing and forwarding (CQF) is launched to simplify the configuration complexity of the early stage mechanism time-aware shaper in TSN flow scheduling. Previous CQF studies adopt an inflexible incremental flow scheduling scheme, which consists of flow sorting, offset search, and resource judgment. However, we observe that flow sorting and offset search are mutually interdependent. The offset of a flow helps determine the resource status on the flow path, which can guide flow sorting. By utilizing the interaction between flow and offset, we design a novel scheduling approach that achieves high scheduling performance and time efficiency. Specifically, the proposed approach combines flow sorting and offset search together to select flow and its offset (i.e., (flow, offset)) simultaneously. To effectively determine the selecting priority and select the potential optimal flow-offset combination, we define a unified metric,$mapping\,score$, to quantify the schedulability of different flow and offset combinations. The extensive experiments demonstrate that the scheduling success rate of our proposed approach is on average 31.69% higher than the baseline and 4.57% higher than the state-of-the-art flow judgement approach (FLJ) method. Moreover, it outperforms the state-of-art FLJ method by 7.62% in large-scale linear topologies, indicating its great scalability in different network scales and complex topologies.

Modeling and predicting the radio spectrum is vital for spectrum management, such as spectrum sharing and anomaly detection. Nevertheless, the precise spectrum prediction is challenging due to the interference from both intra-spectrum and external factors. To tackle these complex internal and external correlations, we develop a model named TF$^2$AN, consisting of three components: 1) a robust signal detection algorithm based on image processing, 2) an attention-based Long Short-term Memory network to capture the temporal-frequency correlations, 3) a generalized fusion module to take the heterogeneous external factors into account. This structure shows prominent effectiveness for spectrum prediction on a single monitoring station with sufficient data. However, when the data derived from a single station is insufficient, the performance of the deep learning model will decline a lot. Considering that more than one monitoring station is deployed in practice, the new challenge becomes how to enhance our model by leveraging the data from multiple stations or frequency bands. Therefore, we further propose T-TF$^2$AN, a transfer learning-based framework for data augmentation and knowledge sharing in spectrum prediction. Compared to TF$^2$AN, better performance is achieved. Besides, the model interpretability and training efficiency are also discussed with two case studies, respectively.

Millimeter Ware (mmWave) Radar is gaining popularity as it can work in adverse environments like smoke, rain, snow, poor lighting, etc. Prior work has explored the possibility of reconstructing 3D skeletons or meshes from the noisy and sparse mmWare Radar signals. However, it is unclear how accurately we can reconstruct the 3D body from the mmWave signals across scenes and how it performs compared with cameras, which are important aspects needed to be considered when either using mmWave radars alone or combining them with cameras. To answer these questions, an automatic 3D body annotation system is first designed and built up with multiple sensors to collect a large-scale dataset. The dataset consists of synchronized and calibrated mmWave radar point clouds and RGB(D) images in different scenes and skeleton/mesh annotations for humans in the scenes. With this dataset, we train state-of-the-art methods with inputs from different sensors and test them in various scenarios. The results demonstrate that 1) despite the noise and sparsity of the generated point clouds, the mmWave radar can achieve better reconstruction accuracy than the RGB camera but worse than the depth camera; 2) the reconstruction from the mmWave radar is affected by adverse weather conditions moderately while the RGB(D) camera is severely affected. Further, analysis of the dataset and the results shadow insights on improving the reconstruction from the mmWave radar and the combination of signals from different sensors.

Neural Architecture Search (NAS) is a powerful tool for automating effective image processing DNN designing. The ranking has been advocated to design an efficient performance predictor for NAS. The previous contrastive method solves the ranking problem by comparing pairs of architectures and predicting their relative performance. However, it only focuses on the rankings between two involved architectures and neglects the overall quality distributions of the search space, which may suffer generalization issues. A predictor, namely Neural Architecture Ranker (NAR) which concentrates on the global quality tier of specific architecture, is proposed to tackle such problems caused by the local perspective. The NAR explores the quality tiers of the search space globally and classifies each individual to the tier they belong to according to its global ranking. Thus, the predictor gains the knowledge of the performance distributions of the search space which helps to generalize its ranking ability to the datasets more easily. Meanwhile, the global quality distribution facilitates the search phase by directly sampling candidates according to the statistics of quality tiers, which is free of training a search algorithm, e.g., Reinforcement Learning (RL) or Evolutionary Algorithm (EA), thus it simplifies the NAS pipeline and saves the computational overheads. The proposed NAR achieves better performance than the state-of-the-art methods on two widely used datasets for NAS research. On the vast search space of NAS-Bench-101, the NAR easily finds the architecture with top 0.01 performance only by sampling. It also generalizes well to different image datasets of NAS-Bench-201, i.e., CIFAR-10, CIFAR-100, and ImageNet-16-120 by identifying the optimal architectures for each of them.

Multi-object tracking (MOT) in the scenario of low-frame-rate videos is a promising solution for deploying MOT methods on edge devices with limited computing, storage, power, and transmitting bandwidth. Tracking with a low frame rate poses particular challenges in the association stage as objects in two successive frames typically exhibit much quicker variations in locations, velocities, appearances, and visibilities than those in normal frame rates. In this paper, we observe severe performance degeneration of many existing association strategies caused by such variations. Though optical-flow-based methods like CenterTrack can handle the large displacement to some extent due to their large receptive field, the temporally local nature makes them fail to give correct displacement estimations of objects whose visibility flip within adjacent frames. To overcome the local nature of optical-flow-based methods, we propose an online tracking method by extending the CenterTrack architecture with a new head, named APP, to recognize unreliable displacement estimations. Then we design a two-stage association policy where displacement estimations or historical motion cues are leveraged in the corresponding stage according to APP predictions. Our method, with little additional computational overhead, shows robustness in preserving identities in low-frame-rate video sequences. Experimental results on public datasets in various low-frame-rate settings demonstrate the advantages of the proposed method.

Semantic attacks have incurred increasing threats to Industrial Control Systems (ICSs), which manipulate targeted system modules by identifying the physical semantics of variables in Programmable Logic Controllers (PLCs) programs, i.e., the sensing/actuating modules represented by the variables. This is usually (and inefficiently) achieved via manual examination of system documents and long-term observation of system behavior. In this paper, we design ARES, a method that Automatically Reverse Engineers the Semantics of variables in PLC programs without requiring any domain knowledge. ARES is built on the fact that the Supervisory Control And Data Acquisition (SCADA) system monitors the behavior of PLC using a fixed mapping between the variables of program code and data log, and the data log variables are marked with physical semantics. By identifying the mapping between PLC code and SCADA data (i.e., the code-data mapping), ARES reverse engineers the physical semantics of program variables. ARES also sheds light on the preferred practices in implementing control rules that improve the resistance of PLC programs to semantic attacks. We have experimentally evaluated ARES and the recommended implementation practices on two ICS platforms.

Backboned by smart meter networks, Advanced Metering Infrastructures (AMIs) play a critical role in smart grids. This paper studies a new False Data Injection Attack (FDIA) scenario targeting AMIs, in which the attacker injects and propagates computer worms (i.e., false data codes) to maliciously increase the readings of networked smart meters and create economic loss to the end customers. This paper establishes the false data code propagation and attack models in such a scenario; based on this, this paper proposes a differential game model for describing the attack and defense process for FDIA against AMIs. A computationally efficient algorithm is developed to solve the proposed differential model and obtain the potential Nash equilibrium (NE) strategy pair. Extensive numerical simulations are conducted to validate the effectiveness of the proposed method under different energy tariff structures.

Recent years have witnessed the fast proliferation of camera sensors networks (CSNs) in numerous Internet of Things (IoT) applications. In order to a capture distinct image of targets from interesting directions, we leverage a special type of coverage called full-view coverage. Full-view coverage guarantees to obtain the images of a point from every direction, whereas it demands much more sensors than a conventional coverage. To this end, we investigate the problem of deploying the minimum number of rotatable camera sensors to achieve the full-view coverage of a set of target points, namely, optimal deployment for the full-view point coverage (OFP) problem. In this work, camera sensors are capable of rotating freely with infinite orientations, thus not only the deployment locations but also the orientations for each camera sensor are required to be optimized. To tackle this challenging problem, we first prove that the OFP problem is NP-hard. Then, we propose two approximation algorithms—iterative screening algorithm (ISA) and improved ISA (IISA) to solve the OFP. We further perform extensive simulations and conduct physical testings to demonstrate the superiority and effectiveness of our proposed solutions. Experimental results show that IISA can generally reduce the total number of required camera sensors by more than 20% compared with the state-of-the-art work.

Programmable logic controllers (PLCs), i.e., the core of control systems, are well-known to be vulnerable to a variety of cyber attacks. To mitigate this issue, we designPLC-Sleuth, a novel noninvasive intrusion detection/localization system for PLCs, which is built on a set of control invariants—i.e., the correlations between sensor readings and the concomitantly triggered PLC commands—that exist pervasively in all control systems. Specifically, taking the system’s supervisory control and data acquisition log as input,PLC-Sleuthabstracts/identifies the system’s control invariants as a control graph using data-driven structure learning, and then monitors the weights of graph edges to detect anomalies thereof, which is in turn, a sign of intrusion. We have implemented and evaluatedPLC-Sleuthusing both a platform of ethanol distillation system (EDS) and a realistically simulated Tennessee Eastman (TE) process. The results show thatPLC-Sleuthcan: 1) identify control invariants with 100%/98.11% accuracy for EDS/TE; 2) detect PLC intrusions with 98.33%/0.85 ‰ true/false positives (TPs/FPs) for EDS and 100%/0% TP/FP for TE; and 3) localize intrusions with 93.22%/96.76% accuracy for EDS/TE.

Detecting small low-contrast targets in the airspace is an essential and challenging task. This article proposes a simple and effective data-driven support vector machine (SVM)-based spatiotemporal feature fusion detection method for small low-contrast targets. We design a novel pixel-level feature, called a spatiotemporal profile, to depict the discontinuity of each pixel in the spatial and temporal domains The spatiotemporal profile is a local patch of the spatiotemporal feature maps concatenated by the spatial feature maps and temporal feature maps in channelwise, which are generated by the morphological black-hat filter and a ghost-free dark-focusing frame difference methods, respectively. Instead of the handcrafted feature fusion mechanisms in previous works, we use the labeled spatiotemporal profiles to train an SVM classifier to learn the spatiotemporal feature fusion mechanism automatically. To speed up detection for high-resolution videos, the serial SVM classification process on central processing units (CPUs) is reformed as parallel convolution operations on graphics processing unit (GPUs), which exhibits over 1000+ times speedup in our real experiments. Finally, blob analysis is applied to generate final detection results. Elaborate experiments are conducted, and experimental results demonstrate that the proposed method performs better than 12 baseline methods for the small low-contrast target detection. The field tests manifest that the parallel implementation of the proposed method can realize real-time detection at 15.3 FPS for videos at a resolution of 2048×1536 and the maximum detection distance can reach 1 km for drones in sunny weather.

Deploying machine learning (ML)-based intrusion detection systems (IDS) is an effective way to improve the security of industrial control systems (ICS). However, ML models themselves are vulnerable to adversarial examples, generated by deliberately adding subtle perturbation to the input sample that some people are not aware of, causing the model to give a false output with high confidence. In this article, our goal is to investigate the possibility of stealthy cyber attacks towards IDS, including injection attack, function code attack and reconnaissance attack, and enhance its robustness to adversarial attack. However, adversarial algorithms are subject to communication protocol and legal range of data in ICS, unlike only limited by the distance between original samples and newly generated samples in image domain. We propose two strategies - optimal solution attack and GAN attack - oriented to flexibility and volume of data, formulating an optimization problem to find stealthy attacks, where the former is appropriate for not too large and more flexible samples while the latter provides a more efficient solution for larger and not too flexible samples. Finally, we conduct experiments on a semi-physical ICS testbed with a high detection performance ensemble ML-based detector to show the effectiveness of our attacks. The results indicate that new samples of reconnaissance and function code attack produced by both optimal solution and GAN algorithm possess 80 percent higher probability to evade the detector, still maintaining the same attack effect. In the meantime, we adopt adversarial training as a method to defend against adversarial attack. After training on the mixture of orginal dataset and newly generated samples, the detector becomes more robust to adversarial examples.

Nowadays, accurate and real-time vehicle tracking is critical to ensure the safety of intelligent vehicles. However, tracking in the complex traffic environments still remains a challenging issue. In this article, we present a road-map aided Gaussian mixture probability hypothesis density (RA-GMPHD) filter for multivehicle tracking with automotive radar. Since the road-map is commonly available in traffic scenarios, we focus on leveraging road-map information to enhance the tracking performance. We first model the vehicle dynamics in a 2-D road coordinates, then approximatively map it onto ground coordinates considering map errors. Additionally, we integrate the variable structure interacting multiple model into the RA-GMPHD filter considering both the dynamic uncertainty of targets and the road geographic constraints. Furthermore, we perform extensive simulations and conduct physical testings to demonstrate the superiority of our approaches compared with state-of-the-art method. Experimental results show our methods enhance both the tracking quality and tracking continuity.

Industrial robots are prototypical cyber-physical systems widely deployed in (smart) manufacturing, which operate according to the operation code uploaded by the human operator and are monitored in real-time based on their movement data. However, industrial robots suffer from replay attacks, via which attackers can manipulate the robot operation without being observed by the monitoring system. To mitigate this vulnerability, we design a novel intrusion detection system for industrial robots using their power fingerprint, calledPIDS(Power-basedIntrusionDetectionSystem), and deliverPIDSas abump-in-the-wiremodule installed at the powerline of commodity robots. The foundation ofPIDSis the physically-induced dependency between the robot movement and the concomitant power consumption, whichPIDScaptures via joint physical analysis and (cyber) data-driven modeling.PIDSthen fingerprints the robot movements observed by the monitoring system using their expected power consumption, and cross-validates the fingerprints with empirically collected power information — a mismatch thereof flags anomalies of the observed movements (i.e., evidence of replay attack). We have evaluatedPIDSusing three models of robots from different vendors — i.e., ABB IRB120, KUKA KR6 R700, and Universal Robots UR5 robots — with over 2,000 operation cycles. Experimental results show thatPIDSdetects replay attacks at an average rate of 96.5 percent (up to 99.9 percent) and a 0.1s latency.

Persistent packet loss in the cloud-scale overlay network severely compromises tenant experiences. Cloud providers are keen to diagnose such problems efficiently. However, existing work is either designed for the physical network or insufficient to present the concrete reason of packet loss. We propose to record and analyze the on-site forwarding condition of packets during packet-level tracing. The cloud-scale overlay network presents great challenges to achieve this goal with its high network complexity, multi-tenant nature, and diversity of root causes. To address these challenges, we present VTrace, an automatic diagnostic system for persistent packet loss over the cloud-scale overlay network. Utilizing the “fast path-slow path” structure of virtual forwarding devices (VFDs), e.g., vSwitches, VTrace installs several “coloring-matching-logging” rules in VFDs to selectively track the target packets and inspect them in depth. The detailed forwarding situation at each hop is logged and then assembled to perform analysis with an efficient path reconstruction scheme. Experiments are conducted to demonstrate VTrace’s low overhead and quick response. Besides, based on the idea “coloring-matching-counting”, VTrace can be easily extended toVTrace-statsto identify the culprit device for transient packet loss. We share experiences of how VTrace andVTrace-statsefficiently work after deploying them in Alibaba Cloud for years.

Incentive mechanism design and quality control are two key challenges in data crowdsourcing, because of the need for recruitment of crowd users and their limited capabilities. Without considering users’ social influences, existing mechanisms often result in low efficiency in terms of the platform’s cost. In this paper, we exploit social influences among users as incentives to motivate users’ participation, in order to reduce the cost of recruiting users. Based on social influences, we design incentive mechanisms with the goal of achieving high quality of crowdsourced data and low cost of incentivizing users’ participation. Specifically, we consider three scenarios. In the full information scenario, we design task assignment and user recruitment mechanisms to optimize the data quality while reducing the incentive cost. In the partial information scenario, users’ qualities and costs are unknown. We exploit the correlation between tasks to overcome the information asymmetry, for both cases of opportunistic crowdsourcing and participatory crowdsourcing. Further, in the dynamic social influence scenario, we investigate the dynamics of users’ social influences and design extra rewards for users to make full use of the social influence and achieve maximum cost saving. We evaluate the incentive mechanisms using numerical results, which demonstrate their effectiveness.

For protecting users' private data, local differential privacy (LDP) has been leveraged to provide the privacy-preserving range query, thus supporting further statistical analysis. However, existing LDP-based range query approaches are limited by their properties, ie, collecting user data according to a pre-defined structure. These static frameworks would incur excessive noise added to the aggregated data especially in the low privacy budget setting. In this work, we propose an Adaptive Hierarchical Decomposition (AHEAD) protocol, which adaptively and dynamically controls the built tree structure, so that the injected noise is well controlled for maintaining high utility. Furthermore, we derive a guideline for properly choosing parameters for AHEAD so that the overall utility can be consistently competitive while rigorously satisfying LDP. Leveraging multiple real and synthetic datasets, we extensively show the effectiveness of AHEAD in both low and high dimensional range query scenarios, as well as its advantages over the state-of-the-art methods. In addition, we provide a series of useful observations for deploying \myahead in practice.

Hybrid long-range (LoRa) network is a promising approach to overcome the half-duplex issue in traditional LoRa networks, increasing the network efficiency and confidence for today's fast-developing smart city services. Gateways (GWs) in hybrid LoRa networks link the end devices (EDs) and the Netserver and have a great impact on the system performance. Previous results on GW planning cannot be directly applied to hybrid LoRa networks since the heterogeneous EDs require different redundancy of coverage. Furthermore, the spreading factors (SFs) which determine the system performance should be considered concurrently. In this article, in order to find the optimal planning scheme, i.e., deciding the number and locations of GWs in the hybrid LoRa network, we propose a heterogeneous redundant coverage solution to meet the requirements of the heterogeneous EDs using the same or different frequencies for uplink and downlink. Specifically, we formulate this problem as a point coverage problem that meets the requirements of EDs. The deleted greedy algorithm (DGA) and the nondeleted greedy algorithm (NDGA) are designed to solve this problem, in which the DGA shows better performance when compared to NDGA. Furthermore, we build models of system performance and analyze the system throughput and energy efficiency based on SFs. The simulation results show that our solution gains more system throughput and energy efficiency than a one-coverage solution.

Imagine when you line up in a store, the person in front of you can make you pay her bill by using a passive wearable device that forces a scan of your credit card or mobile phones without your awareness. An important assumption of today's near-field communication (NFC)-enabled cards is the limited communication range between the commercial reader and the NFC cards. Previous approaches effectively used mobile phones and active relays to break the range limit of NFC propagation for the NFC attack. However, these approaches require a power supply and protocol modification when mobile phones or active relays transmit NFC signals. We propose ReCoil, a system that uses passive relays to attack NFC-enabled mobile phones or cards by expanding the communication range of NFC to 49.6 cm, an obvious improvement over its intended commercial distance. ReCoil is a magnetically coupled resonant wireless power transfer system, which optimizes the energy transfer by searching the optimal geometry parameters. Specifically, we first narrow down the feasible area reasonably and design the ReCoil-greedy algorithm such that the relays absorb the maximum energy from the reader. In order to reroute the signal to pass over the surface of the human body, we then design a half waistband by carefully analyzing the impact of the distance and orientation between two coils on the mutual inductance. Then, three more coils are added to the system to keep enlarging the communication range. Finally, extensive experiment results validate our analysis, showing that our passive relays consisting of common copper wires and tunable capacitors can expand the range of NFC to 49.6 centimeters.

Recently, cloud computing has become a vital part that supports people’s normal lives and production. However, accompanied by the increasing complexity of the cloud network, failures constantly keep coming up and cause huge economic losses. Thus, to guarantee the cloud network performance and prevent execrable effects caused by failures, cloud network diagnostics has become of great interest for cloud service providers. Due to the characteristics of cloud network (e.g., virtualization and multi-tenancy), transplanting traditional network diagnostic tools to the cloud network face several difficulties. Additionally, many existing tools cannot solve problems in the cloud network. In this paper, we summarize and classify the state-of-the-art technologies of cloud diagnostics which can be used in the production cloud network according to their features. Moreover, we analyze the differences between cloud network diagnostics and traditional network diagnostics based on the characteristics of the cloud network. Considering the operation requirements of the cloud network, we propose the points that should be cared about when designing a cloud network diagnostic tool. Also, we discuss the challenges that cloud network diagnostics will face in future development.

Running at a fast speed, the high-speed train is prone to be interrupted by the surrounding strong wind. To ensure the safety of the trains, an effective approach is to deploy anemometers alongside the railway, such that the real-time and short-term predicted wind speed can be reported, and be further used by dispatchers to take protective actions in advance. However, in certain situations, the solely predicted wind speed is not informative enough to describe the wind status. It is difficult to tell if a strong wind incident could happen when the predicted wind speed is slightly lower than the strong wind threshold. We take the first attempt to predict the strong wind risk alongside the high-speed railway (HSR). A new model, called Multiple Attention Layer based Multi-Instance Learning (MAL-MIL), is proposed to address this problem. The key idea is to estimate the possibility that the actual wind speed exceeds the threshold conditionally on the predicted wind status. Based on attention mechanisms and long-short term memory network, the model can firstly generate deep representations of the future wind status. Then, though there is a lack of the risk ground truth, the multi-instance learning process facilitates the training procedure so that the relationships between these deep representations and the strong wind incidents could be quantified. Furthermore, considering the practicality of the model, we also design a result justification module to explain the reported risk. The superior performance is finally verified based on a real-world dataset.

RF power transfer is becoming a reliable solution to energy supplement of Internet of Things (IoT) in recent years, thanks to the emerging off-the-shelf wireless charging and sensing platforms. However, as a core component of IoT, sensor nodes mounted with these platforms can not work and harvest energy simultaneously, due to the low-manufacture-cost requirement. This leads to a new design challenge of optimally scheduling sensor nodes’ operation states: working or recharging, to achieve a desirable network utility. In our design, we first consider a single-hop special case of small-scale networks. We transform the operation state scheduling problem into a linear programming problem, and obtain an optimal analytical solution. Then a general case of large-scale multi-hop networks is investigated. The multi-hop operation state scheduling problem is proved to be NP-hard. We show that the spatiotemporal coupling caused by time-varying network topology makes the problem quite challenging. Based on Lyapunov optimization technique, we design a State Scheduling Algorithm (SSA) with a proved performance guarantee. Our algorithm decouples the primal problem by defining a dynamic energy threshold vector, which successfully schedules each sensor node to the desirable state according to its energy level. To verify our design, the SSA is implemented on a Powercast wireless charging and sensing testbed, achieving about 85 percent of the theoretical optimal with quite low time complexity. Furthermore, numerous simulation results demonstrate that the SSA outperforms the baseline algorithms and achieves good performance under different network settings.

Information barrier coverage has been widely adopted to prevent unauthorized invasion of important areas in Internet of Things. As sensors are typically placed outdoors, they are susceptible to getting faulty. Previous works assumed that faulty sensors are easy to recognize, e.g., they may stop functioning or output apparently deviant sensory data. In practice, there exist multiple types of fault that sensors may have during operation. It is, thereby, difficult to recognize faulty sensors as well as their invalid output and attain accurate intrusion detection. We, in this paper, propose a novel fault-tolerant intrusion detection algorithm (TrusDet) based on trust management to address this challenging issue. TrusDet comprises of three steps: i) sensor-level detection, ii) sink-level decision by collective voting, and iii) trust management and fault determination. In the Step i) and ii), TrusDet divides the surveillance area into a set of fine-grained subareas and exploits temporal and spatial correlation of sensory output among sensors in different subareas to yield a more accurate and robust performance of information barrier coverage. In the Step iii), TrusDet builds a trust management based framework to determine the confidence level of sensors being faulty. We implement TrusDet on HC-SR501 infrared sensors, and design hardware and software to build a practical detection system. Extensive experimental results and simulation results validate the information coverage model and demonstrate that TrusDet has a very low false alarm rate.

In this paper, we investigate localizing multiple wire-less devices in the context of Internet of Things (IoTs). Considering the massive amount of IoT devices deployed in an IoT system, we establish an economical and lightweight Moving-Anchor multitar-Get IoT loCalization system (MAGIC), which is capable of localizing multiple targets simultaneously in a low-power and low-cost manner. To be specific, MAGIC utilizes a single mobile anchor with GPS and LoRa gateway to localize multiple IoT devices embedded with LoRa tags. The single anchor moves to different positions and makes location estimations at each step based on the distance measurements between each target and itself. To find the optimal moving strategy of the anchor, we formulate an optimal path problem aiming to minimize the total length of the path with guaranteed localization accuracy. Since solving the optimal path problem requires the localization information at all the positions, which is unavailable during the moving process, we instead solve an optimal step problem in each step that minimizes the length of the next step without relying on future information. We address the non-convex issue of the problem by decomposing it into a set of convex subproblems by partitioning the feasible domain, which yields a suboptimal solution. Simulation results and practical experiments validate the effectiveness of the proposed MAGIC system and demonstrate its practical value in realistic IoT scenarios.

Outlier detection is of vital importance for various fields and applications. Existing works mainly focus on identifying outliers from underlying datasets, while how to provide sense-making explanations is largely ignored. In this paper, we propose to visualize data points in a set of scatter plots on two-dimensional (2-D) feature spaces that can provide meaningful explanations about the outlying behavior of outliers. Data are typically multidimensional and the number of 2-D combinations could be huge. Also, outliers may have diverse characteristics, and thus the global scatter plots containing all of outliers may degrade the explanation effectiveness for those outliers having idiosyncratic abnormal 2-D spaces. To address this problem, we propose a new outlier explanation approach, called LP-Explain, which tries to identify the set of best Local Pictorial explanations (defined as the scatter plots in the 2-D space of the feature pairs) that can Explain the behavior for cluster of outliers. We first define an effective measure to quantify the similarity between outliers, and then cluster outliers into different groups based on their abnormal feature pairs. We then propose to weigh the importance of feature pairs within each cluster through a multi-task learning framework to select the set of top feature pairs that best explain various outlier clusters. By adjusting a user-defined parameter indicating the “localization level”, the proposed method can attain both global and local results for the explanation of the outliers. 2-D visual explanations can be plotted for the top-weighted feature pairs of each cluster. We conduct experiments on various public datasets, which show that the proposed approach can provide more meaningful explanations about the outlying behavior in a dataset.

Radio map, serving as an efficient indicator of wireless environments, has been widely used in smart-city applications, including network monitoring/planning, anomaly signal detection, and indoor/outdoor localization. It is hard to maintain an update-to-date fine-grained radio map within a large area, since the radio map changes rapidly due to the internal and external factors. Previous studies usually relied on time-consuming site surveys at densely predefined reference points, leading to either coarse-grained or out-of-date radio maps. In this paper, we propose a fine-grained radio map reconstruction framework, called Supreme, based on crowd-sourced data in an image super-resolution manner. Specifically, Supreme explores spatial-temporal relationships in historical coarse-grained radio maps and builds a real-time fine-grained radio map using deep spatial-temporal reconstruction networks. Furthermore, a heterogeneous data fusion module is devised to make full use of external information. To evaluate the performance of Supreme, we conduct extensive experiments and ablation studies on a large-scale dataset with a total of six-month data collected from two university campuses. Besides, we investigate the transferability of Supreme in different locations and service networks, showing that the fine-tuned model can largely reduce the training time and achieve better performance. Experimental results demonstrate that our model outperforms state-of-the-art baselines and a case study on the localization is enhanced with marginal improvements on accuracy.

Vision-based dynamic pedestrian intrusion detection (PID), judging whether pedestrians intrude an area-of-interest (AoI) by a moving camera, is an important task in mobile surveillance. The dynamically changing AoIs and a number of pedestrians in video frames increase the difficulty and computational complexity of determining whether pedestrians intrude the AoI, which makes previous algorithms incapable of this task. In this paper, we propose a novel and efficient multi-task deep neural network, PIDNet, to solve this problem. PIDNet is mainly designed by considering two factors: accurately segmenting the dynamically changing AoIs from a video frame captured by the moving camera and quickly detecting pedestrians from the generated AoI-contained areas. Three efficient network designs are proposed and incorporated into PIDNet to reduce the computational complexity: 1) a special PID task backbone for feature sharing, 2) a feature cropping module for feature cropping, and 3) a lighter detection branch network for feature compression. In addition, considering there are no public datasets and benchmarks in this field, we establish a benchmark dataset to evaluate the proposed network and give the corresponding evaluation metrics for the first time. Experimental results show that PIDNet can achieve 67.1% PID accuracy and 9.6 fps inference speed on the proposed dataset, which serves as a good baseline for the future vision-based dynamic PID study.

Core devices form the critical components of the cloud network and provide service to multiple tenants simultaneously. The anomalies that happened in core devices impact network availability of a large number of users, meanwhile, lead to the degradation of cloud providers’ profits. However, direct monitoring of core devices needs to deploy massive heartbeat checking tools on numerous related components, which will be extremely laborious. In this paper, we deploy RAIN to reduce the number of devices that need to be detailed investigated for anomalies. The session traffic data among core devices and served virtual machines are utilized to conduct the analyzing. To guarantee near real-time monitoring, RAIN is designed as a two-step structure and incorporating four feature-based detection methods. RAIN has been deployed in Alibaba’s production cloud network for over 6 months and is analyzing terabytes of traffic flow metrics per day.

Industrial robots have been shown to suffer from replay attacks, via which adversaries not only manipulate the robot operation by downloading malicious code, but also prevent the detection of this manipulation by replaying recorded (and normal) movement data to the monitoring system. To protect industrial robots from replay attacks, we design a novel intrusion detection system using the power fingerprint of robots, called PIDS (Power-based Intrusion Detection System), and deliver PIDS as a bump-in-the-wire module installed at the powerline of commodity robots. The foundation of PIDS is the physically-induced dependency between the robot movement and the concomitant electrical power consumption, which PIDS captures via joint physical analysis and (cyber) data-driven modeling. PIDS then fingerprints the robot movements observed by the monitoring system using their expected power consumption, and cross-validates the fingerprints with empirically collected power information --- a mismatch thereof flags anomalies of the observed movements (i.e., evidence of replay attack). We have evaluated PIDS using three models of robots from different vendors --- i.e., ABB IRB120, KUKA KR6 R700, and Universal Robots UR5 robots --- with over 2, 000 operation cycles. The experimental results show that PIDS detects replay attacks with an average rate of 96.5% (up to 99.9%) and a 0.1s latency.

Persistent packet loss in the cloud-scale overlay network severely compromises tenant experiences. Cloud providers are keen to automatically and quickly determine the root cause of such problems. However, existing work is either designed for the physical network or insufficient to present the concrete reason of packet loss. In this paper, we propose to record and analyze the on-site forwarding condition of packets during packet-level tracing. The cloud-scale overlay network presents great challenges to achieve this goal with its high network complexity, multi-tenant nature, and diversity of root causes. To address these challenges, we present VTrace, an automatic diagnostic system for persistent packet loss over the cloud-scale overlay network. Utilizing the "fast path-slow path" structure of virtual forwarding devices (VFDs), e.g., vSwitches, VTrace installs several "coloring, matching and logging" rules in VFDs to selectively track the packets of interest and inspect them in depth. The detailed forwarding situation at each hop is logged and then assembled to perform analysis with an efficient path reconstruction scheme. Experiments are conducted to demonstrate VTrace's low overhead and quick responsiveness. We share experiences of how VTrace efficiently resolves persistent packet loss issues after deploying it in Alibaba Cloud for over 20 months.

The leakage of driving positions or traces poses a serious privacy threat upon the users in the vehicular ad-hoc networks (VANETs). A series of pseudonym changing approaches have been proposed to achieve unlinkability between the users' identities and their driving information. To investigate the effectiveness of the changing strategies on user anonymity, it is important to stand at the side of an adversary to implement posterior linking between different pseudonyms. In this paper, we remove the assumption of motion models commonly used in existing works, and propose a novel pseudonym linking scheme by focusing on the prediction of acceleration and direction angle. Further, we plug several side information (e.g., road structure, traffic signal) into the proposed scheme to improve the linking performance. Finally, based on five representative pseudonym changing strategies, extensive experiments are conducted to evaluate the performance of the proposed linking scheme. The experimental results show that the side-information assisted pseudonym linking scheme achieves success rates of over 74%.

Offloading cellular traffic through WiFi Access Points (APs) has been a promising way to relieve the overload of cellular networks. However, data offloading process consumes a lot of resources (e.g., energy, bandwidth, etc.). Given that the owners of APs are rational and selfish, they will not participate in the data offloading process without receiving the proper reward. Hence, there is an urgent need to develop an effective incentive mechanism to stimulate APs to take part in the data offloading process. This paper proposes a novel Delay-constraint and Reverse Auction-based Incentive Mechanism, named DRAIM. In DRAIM, we model the reverse auction-based incentive problem as a nonlinear integer problem from the business perspective, aiming to maximize the revenue of the Mobile Network Operator (MNO), and jointly consider the delay constraint of different applications in the optimization problem. Then, two low-complexity methods: Greedy Winner Selection Method (GWSM), and Dynamic Programming Winner Selection Method (DPWSM) are proposed to solve the optimization problem. Furthermore, an innovative standard Vickrey-Clarke-Groves scheme-based payment rule is proposed to guarantee the individual rationality and truthfulness properties of DPWSM. At last, extensive simulation results show that the proposed DPWSM is superior to the proposed GWSM and the Random Winner Selection Method in terms of the MNO’s utility and traffic load under different scenarios.

Recent studies have exploited moving target defense (MTD) for thwarting false data injection (FDI) attacks against the state estimation (SE) by actively perturbing branch parameters (i.e., impedance or admittance) in power grids. To hide the activation of MTD from attackers, a new strategy named hidden MTD has been proposed by the latest literature. A hidden MTD can increase the defender’s chance to detect FDI attacks and avoid the attacker from inferring new branch parameters. However, by using an MTD-confirming detector like the bad data detection (BDD) checker in SE, we observe that it is still possible for the attacker to detect this hidden MTD when the power flows change with time. To uncover the insight of MTD’s hiddenness, we study the conditions needed for achieving a hidable MTD. We find that the hiddenness of MTD is closely related to the branch perturbations, system topology, and attacker’s knowledge. From the attacker’s perspective, we prove that an MTD can be detected by the attacker only if he/she knows the previous parameters of a set of branches that forms a circle and the measurements corresponding to those branches after MTD. But once the attacker has full knowledge of branch parameters before MTD and has obtained all measurements after MTD, it is proved that we can never achieve a hidable and effective MTD. From the defender’s perspective, since it is impossible to know the attacker’s capability, we cannot determine whether a constructed MTD is hidable or not by purely depending on the MTD design. To address this issue, we propose that, by protecting a basic set of measurements, we always can achieve a hidable and effective MTD regardless of the changes of power flows, the attacker’s knowledge, and the branch perturbations. Furthermore, we validate our findings with the IEEE standard test power systems.

Recent studies have considered thwarting false data injection (FDI) attacks against state estimation in power grids by proactively perturbing branch susceptances. This approach is known as moving target defense (MTD). However, despite of the deployment of MTD, it is still possible for the attacker to launch stealthy FDI attacks generated with former branch susceptances. In this paper, we prove that, an MTD has the capability to thwart all FDI attacks constructed with former branch susceptances only if (i) the number of branches l in the power system is not less than twice that of the system states n (i.e., l ≥ 2n, where n + 1 is the number of buses); (ii) the susceptances of more than n branches, which cover all buses, are perturbed. Moreover, we prove that the state variable of a bus that is only connected by a single branch (no matter it is perturbed or not) can always be modified by the attacker. Nevertheless, in order to reduce the attack opportunities of potential attackers, we first exploit the impact of the susceptance perturbation magnitude on the dimension of the stealthy attack space, in which the attack vector is constructed with former branch susceptances. Then, we propose that, by perturbing an appropriate set of branches, we can minimize the dimension of the stealthy attack space and maximize the number of covered buses. Besides, we consider the increasing operation cost caused by the activation of MTD. Finally, we conduct extensive simulations to illustrate our findings with IEEE standard test power systems.

The increasing popularity of drones is bringing many public security and privacy breach issues, such as smuggling, intrusion, and illegal surveillance. Traditional approaches to detecting and localizing drones such as radar and computer vision incur high costs and hence are not desirable for large-scale applications. In this paper, we propose a cost-effective crowdsensing system named CEDAR to achieve such a goal. Specifically, we introduce a novel way of detecting drones by smartphones, exploiting the fact that most drones adopt Wi-Fi for communications with ground control stations. We design an efficient detection algorithm that takes advantage of historical Wi-Fi beacon information and MAC address encoding mechanisms used by drone manufacturers. Using received signal strength, we can also localize the detected drones. Further, to encourage participants' involvement, we design an incentive mechanism based on online auction that guarantees truthfulness and consumer sovereignty. CEDAR can be directly applied to multiple drone scenarios. We implement the system based on Android for the client and Spring, Spring MVC, and Mybatis (SSM) for the centralized platform that supports scalability and hierarchical structure, and enables the coordination between clients and the platform. We perform extensive experiments to validate our analysis. Particularly, the detection rate in the experiments reaches 86.7 percent even without any prior information about drones.

Node location information is very important to many novel applications of Internet of Things (IoT). Typically, IoT nodes are resource-constrained, and thus costly and energy-hungry localization techniques fall short. In this paper, we present iLoc, a low-cost, low-power and wide-area localization system for IoT applications. iLoc is built on the emerging LoRa technology and overcomes the disadvantage of many short-range localization techniques. Central to iLoc is a mobile anchor node comprising of a simplified LoRa gateway and a smartphone. To locate an IoT node, the anchor node moves around, during which the LoRa gateway receives its locations from the smartphone, and communicates with the IoT node for the information of time of flight (ToF) as well as received signal strength indication (RSSI). In order to obtain a better distance estimation, both RSSI and ToF are integrated in the regression analysis of distance between the anchor node and the IoT node. We further design an iterative localization algorithm by judiciously deciding the locations of the anchor node step by step. The LoRa gateway and tag we prototype cost less than 10 and 5 dollars, respectively. We conduct extensive experiments and the results demonstrate that iLoc achieves an average localization error of 1.33m and power consumption of 0.25mAh in an open environment.

We study a novel problem of sponsored search (SS) for E-Commerce platforms: how we can attract query users to click product advertisements (ads) by presenting them features of products that attract them. This not only benefits merchants and the platform, but also improves user experience. The problem is challenging due to the following reasons: (1) We need to carefully manipulate the ad content without affecting user search experience. (2) It is difficult to obtain users' explicit feedback of their preference in product features. (3) Nowadays, a great portion of the search traffic in E-Commerce platforms is from their mobile apps (e.g., nearly 90% in Taobao). The situation would get worse in the mobile setting due to limited space. We are focused on the mobile setting and propose to manipulate ad titles by adding a few selling point keywords (SPs) to attract query users. We model it as a personalized attractive SP prediction problem and carry out both large-scale offline evaluation and online A/B tests in Taobao. The contributions include: (1) We explore various exhibition schemes of SPs. (2) We propose a surrogate of user explicit feedback for SP preference. (3) We also explore multi-task learning and various additional features to boost the performance. A variant of our best model has already been deployed in Taobao, leading to a 2% increase in revenue per thousand impressions and an opt-out rate of merchants less than 4%.

Modeling and predicting radio spectrum are significant for better understanding the behavior of spectrum, managing their usage as well as optimizing the performance of dynamic spectrum access. Most of the existing works concentrate on predicting the occupation status of the spectrum via threshold-based binary time series, ignoring abundant frequency correlations. In fact, precisely predicting the energy level of the radio spectrum can provide richer information for applications such as characterizing the spectrum trending for earlier anomaly detection and estimating the channel quality for efficient spectrum sharing. However, the precise prediction is challenging due to the interference from both intra-spectrum and external factors. In this paper, we propose a temporal-frequency fusion attention network to model the complex internal and external correlations for precise prediction. More specifically, our framework consists of three major components: 1) an image processing based robust signal detection algorithm to locate the signal as model input. 2) an attention-based Long Short-term Memory network to model the temporal-frequency correlation of the spectrum. 3) a generalized fusion module to take in the external factors from heterogeneous domains. Extensive experiments are conducted on real-world datasets collected by our spectrum monitoring station deployed in the city of Hangzhou, China, which shows that the proposed signal detection algorithm is robust for frequency bands with different signal to noise ratios. Furthermore, experimental results demonstrate that our method outperforms seven baseline methods in terms of prediction accuracy. The sensitivities of hyper-parameters are analyzed and the interpretability is also well discussed to prove the effectiveness of our method.

Recently, full-view coverage has been introduced to capture intruders from multiple directions in the camera sensor networks. It is more efficient than traditional coverage in identifying the intruders. However, full-view coverage typically calls for a large number of camera sensors. Hence, we exploit limited mobility or orientation to improve the performance of full-view coverage since camera sensors typically can rotate to cover more areas without being relocated after installation. Observing that target points may not be full-view covered constantly due to the sensor rotation, we emphasize the importance of the fairness-based coverage maximization problem, i.e., how to schedule the orientations of camera sensors to maximize the minimum cumulative full-view coverage time of target points. To solve this issue, we first try to reduce the dimension space of orientations by dividing the orientation space into a set of discrete directions. We then study how to select the minimum number of sensing regions that camera sensors should rotate to cover in order to ensure the full-view coverage of all target points. Next, we unveil the relationship between the full-view coverage and target points, which are spatially correlated. Based on these results, we devise a centralized algorithm to solve the problem based on “largest demand first serve” principle, by which the target points with less cumulative full-view coverage time will be preferentially selected to be full-view covered with a higher probability. We further design a distributed solution as a counterpart of the centralized algorithm. Extensive simulations are presented to show the performances of the proposed algorithms. Results show that exploiting limited mobility of sensor rotation has good potential in promoting the efficiency and reducing the cost of ensuring full-view coverage.

Advanced traveler information systems (ATISs) provide travelers with public transportation information to improve the quality of individual life and alleviate congestion as well as air pollution. However, existing works have not fully incorporated bike sharing systems within ATIS, providing no interaction with other modalities nor taking bike stocks into account. In addition, the uncertainty of traffic conditions and multimodal routing makes it challenging to accurately estimate the travel time. In this paper, we leverage large-scale historical data collected in London and construct a multimodal transportation network, including bus, tube, public bikes, and walking. We solve the modalities aggregation problem by practically modeling the travel time, arrival time, bike stock, and transfer time between different transport modalities. Furthermore, we propose TERP, a time-event-dependent route planner that optimizes both trip duration and reliability. We conduct experiments on extensive real-world data with over 23 million arrival records and 15 million stock records on more than 10 000 stations from transport for London platform (TfL). The results validates 14.91% reduction of actual total trip duration and 56.28% improvement in terms of route reliability in rush hours comparing with TfL.

As an innovative mobility strategy, public bike-sharing has grown dramatically worldwide. Though it provides convenient, low-cost, and environmental-friendly transportation, the unique features of bike-sharing systems give rise to problems for both users and operators. The primary issue is the uneven distribution of bikes caused by ever-changing usage and (available) supply. This imbalance necessitates efficient bike rebalancing strategies, which depends highly on bike mobility modeling and prediction. In this paper, a trace-driven simulation-based prediction approach is proposed by simultaneously taking user mobility demand and real-time status of stations into consideration. We extensively evaluate the performance of our design with the dataset from one of the world's largest public bike-sharing systems located in Hangzhou, China, which owns more than 2800 stations. The evaluation results show an 85 percentile relative error of 0.6 for checkout and 0.4 for checkin prediction. The preliminary results on how the predictions can be used for bike rebalancing are also provided. We believe that this new mobility modeling and prediction approach can improve the bike-sharing system operation algorithm design and pave the way for rapid deployment and adoption of bike-sharing systems across the globe.

The rapid development of bike-sharing systems has brought people enormous convenience during the past decade. On the other hand, high transport flexibility gives rise to problems for both users and operators. For users, dynamic distribution of shared bikes caused by uneven user demand often leads to the check in or check out service unavailable at some stations. For operators, unbalanced bike usage comes with more bike broken and growing maintenance cost. In this paper, we consider enhancing user experiences and rebalance bicycle utilization by directing users to different stations with a higher success rate of rental and return. For the first time, we devise a trip advisor that recommends bike check-in and check-out stations with joint consideration of service quality and bicycle utilization. To ensure service quality, we firstly predict the user demand of each station to obtain the success rate of rental and return in the future. Experiments indicate that the precision of our method is as much as 0.826, which has raised by 25.9 percent as compared with that of the historical average method. To rebalance bike usage, from historical data, we identify that biased bike usage is rooted from circumscribed bicycle circulation among few active stations. Therefore, with defined station activeness, we optimize the bike circulation by leading users to shift bikes between highly active stations and inactive ones. We extensively evaluate the performance of our design through real-world datasets. Evaluation results show that the percentage of frequently used bikes decreases by 33.6 percent on usage number and 28.6 percent on usage time.

Marginal tables are the workhorse of capturing the correlations among a set of attributes. We consider the problem of constructing marginal tables given a set of user's multi-dimensional data while satisfying Local Differential Privacy (LDP), a privacy notion that protects individual user's privacy without relying on a trusted third party. Existing works on this problem perform poorly in the high-dimensional setting; even worse, some incur very expensive computational overhead. In this paper, we propose CALM, Consistent Adaptive Local Marginal, that takes advantage of the careful challenge analysis and performs consistently better than existing methods. More importantly, CALM can scale well with large data dimensions and marginal sizes. We conduct extensive experiments on several real world datasets. Experimental results demonstrate the effectiveness and efficiency of CALM over existing methods.

Customer churn is one of the fundamental problems in telecommunications industry. Identifying potential churners in the early stage is an effective approach to preventing customer churn. Previous studies largely focused on churners from one operator to another. In this paper, we consider an interesting scenario where customer churn occurs within a specific operator (intra-operator), i.e., customers of China Mobile switch their telecommunication services from fourth generation (4G) to third generation/second generation (3G/2G). Since mechanism for intra-operator customer churn is quite different, previous studies fall short for this new problem. We propose a novel framework to address the emerging intra-operator customer churn problem by investigating the relations between \pmb4G service plans and switching behaviors of customers, unveiling the underpinned cause of the relations. Specifically, we first establish a classification criterion to estimate current service usage status of each customer. Then, we assign switching score to each customer which can be used to reflect switching likelihood of the customer. Finally, we establish the relations between 4G service plans and switching behaviors of customers by introducing two new concepts: changing trend and design evaluation score. We find that some features of 4G service plans indeed affect switching behaviors of customers significantly. Our framework can provide insight into the reasonable design of 4G service plans. Experimental results based on real data demonstrate the effectiveness of our framework.

RF power transfer is becoming a reliable solution to energy supplement of Internet of Things (IoT) in recent years, thanks to the emerging off-the-shelf wireless charging and sensing platforms. As a core component of IoT, sensor nodes mounted with these platforms can not work and harvest energy simultaneously, due to the low-manufacture-cost requirement. This leads to a new design challenge of optimally scheduling sensor nodes' operation states: working or recharging, to achieve a desirable network utility. We show that the operation state scheduling problem is quite challenging, since the time-varying network topology leads to spatiotemporal coupling of scheduling strategies. We first consider a single-hop special case of small-scale networks. We employ geometric programming to transfer it into a convex optimization problem, and obtain an optimal analytical solution. Then a general case of large-scale multi-hop networks is investigated. Based on Lyapunov optimization technique, we design a State Scheduling Algorithm (SSA) with a proved performance guarantee. Our algorithm decouples the primal problem by defining a dynamic energy threshold vector, which successfully schedules each sensor node to the desirable state according to its energy level. To verify our design, the SSA is implemented on a Powercast wireless charging and sensing testbed, achieving about 85% of the theoretical optimal with quite low time complexity. Furthermore, numerous simulation results demonstrate that the SSA outperforms the baseline algorithms and achieves good performance under different network settings.

The wide proliferation of drones has posed great threats to personal privacy and public security, which makes it urgent to monitor and locate intruding drones in sensitive areas. In Direction of Arrival (DOA) based localization, the estimation accuracy of DOA directly affects the localization accuracy. In this paper, we propose a novel algorithm to estimate the DOA of an intruding drone by exploiting its acoustic feature, which is mainly reflected in the strength distribution of the harmonics of the received acoustic signal. Specifically, this algorithm first estimates the harmonic frequencies of the drone's acoustic signal in frequency domain. Then, multiple signal classification is used to estimate the DOAs of all the selected harmonics. Furthermore, weighted sum of these DOA estimates will be taken as the drone's DOA estimate, where the weights are in proportional to the energy of the corresponding harmonics. The performance of the proposed algorithm is verified by both simulation and field experiments.

Recently, a considerable research interest has grown up in the millimetre wave wireless system as the most promising technologies in the next generation communication. Since high‐frequency channels of the millimetre wave are easily attenuated in space, beamforming technology relying on the massive multi‐input‐multi‐output system is introduced to transmit the millimetre wave in a very narrow directional beam, so as to greatly improve the transmit efficiency. Then a challenging problem lies in that how to optimise the overall throughput by allocating the antenna resources to different mobile users in the massive MIMO antenna system. In this study, the authors handle such a difficult problem in two different cases. They first begin with the one‐direction case, i.e. all sub‐arrays are deployed in several parallel rows along the edge of a rectangle antenna array. They decompose the problem and solve it gradually. Then they generalise the authors' result to the two‐dimensional case, where the sub‐arrays can be deployed in orthogonal directions. They apply the similar scheme, decompose the problem and solve each sub‐problem progressively. Both NP‐hard problems are solved with time efficient approximation algorithms. Simulation results demonstrate the efficiency of the proposed algorithms in different cases.

Recently, demands for low-power wide-area (LPWA) machine-type communications have increased dramatically. It is expected that LPWA connections will reach 2 billion in 2020, exceeding the number of traditional cellular users. Narrowband Internet of Things (NB-IoT), a new radio access technology, has been released by the Third Generation Partnership Project for such demands. NB-IoT supports super coverage extension, massive number of connections and long user lifetime with low power cost and low device complexity. With such prominent features, NB-IoT has become one of the dominating technologies in LPWA networks, applicable to a large range of IoT application scenarios such as smart meter, smart parking, smart home, smart tracking, e-health, etc. However, NB-loT is still in its infancy, needing deep theoretical investigation of modeling and optimizing system performance. Also, emerging applications that can be enabled by NB-loT and implementation challenges therein need further exploration.

No abstract available.

Incentive mechanism plays a critical role in privacy-aware crowdsensing. Most previous studies assume a trustworthy fusion center (FC) in their co-design of incentive mechanism and privacy preservation. Very recent work has taken the step to relax the assumption on trustworthy FC and allowed participatory users (PUs) to randomly report their binary sensing data, whereas the focus is to examine PUs' equilibrium behavior. Making a paradigm shift, this paper aims to study the privacy compensation for continuous data sensing while allowing FC to directly control PUs. There are two conflicting objectives in such a scenario: FC desires better quality data in order to achieve higher aggregation accuracy whereas PUs prefer injecting larger noises for higher privacy-preserving levels (PPLs). To strike a good balance therein, we propose an efficient incentive mechanism named REAP to reconcile FC's aggregation accuracy and individual PU's data privacy. Specifically, we adopt the celebrated notion of differential privacy to quantify PUs' PPLs and characterize their impacts on FC's aggregation accuracy. Then, appealing to contract theory, we design an incentive mechanism to maximize FC's aggregation accuracy under a given budget. The proposed incentive mechanism offers different contracts to PUs with different privacy preferences, by which FC can directly control them. It can further overcome the information asymmetry problem, i.e., FC typically does not know each PU's precise privacy preference. We derive closed-form solutions for the optimal contracts in both complete information and incomplete information scenarios. Further, the results are generalized to the continuous case where PUs' privacy preferences take values in a continuous domain. Extensive simulations are provided to validate the feasibility and advantages of our proposed incentive mechanism.

Finding the k smallest/largest element of a large array, i.e., k-selection is a fundamental supporting algorithm in data analysis. Due to the fact that big data born in geo-distributed environments, it especially requires communication-efficient distributed k-selection, besides typical computation and memory efficiency. Moreover, sensitive organizations make data privacy a rigorous precondition for their participation in such distributed statistical analysis for common profit. To this end, we propose a Distributed Privacy-Aware Median (DPAM) selection algorithm for median selection in distributed large-scale data while preserving local statistics privacy, and extend it to arbitrary k-selection. DPAM utilizes mean to approximate median, via contraction of the standard deviation. It is the theoretical fastest with a worst computation complexity of O(N), and also highly efficient in communication overhead (in logarithm of data range). To preserve ε-differential privacy of local statistics, DPAM randomly adds dummy elements (the number follows a rounded Laplacian distribution) to local data. The noise does not degrade the estimation precision or convergence rate. Performance of DPAM is compared with centralized/distributed quick select and optimization, in terms of complexity and privacy preserving ability. Extensive simulation and experiment results show the higher efficiency of DPAM.

With a growing demand for mobile data usage, cellular networks are facing the challenge of severe traffic overload. Device-to-Device (D2D) multicast communication, a proximity communication technique that leverages the spatial-temporal locality of mobile data usage to achieve one-to-many simultaneous transmission, provides an efficient solution to offloading heavy traffic. However, D2D multicast communication relies on users' sharing behavior, and multicasting data incur costs such as energy, which prevents the popularity of such user-driven technique. Thus, in this paper, we study the problem of incentive design for promoting D2D multicast communication in cellular networks. Specifically, we propose a contract-based incentive mechanism to optimize the operator's expected profit from motivating D2D multicast communication for content sharing with guaranteed service quality. We consider both complete and incomplete information scenarios. The proposed mechanism can provide efficient incentives under information asymmetry by delivering contracts, which satisfy nice properties such as individual rationality and incentive compatibility. Greedy algorithms with low complexity are developed based on local optimization to obtain fast solutions for contract design. A Lagrange multiplier method based iterative algorithm that can be proved to obtain optimal contracts under information asymmetry is also proposed. Numerical results show that the proposed mechanism can handle information asymmetry better and has a better performance than linear and step pricing schemes, increasing the expected profit by up to 2.49 times and 1.8 times, respectively.

Rapid development of bike-sharing systems has brought people enormous convenience during the past decade. On the other hand, high transport flexibility comes with dynamic distribution of shared bikes, leading to an unbalanced bike usage and growing maintenance cost. In this paper, we consider to rebalance bicycle utilization by means of directing users to different stations. For the first time, we devise a trip advisor that recommends bike check-in and check-out stations with joint consideration of service quality and bicycle utilization. From historical data, we firstly identify that biased bike usage is rooted from circumscribed bicycle circulation among few active stations. Therefore, with defined station activeness, we optimize the bike circulation by leading users to shift bikes between highly active stations and inactive ones. We extensively evaluate the performance of our design through real-world datasets. Evaluation results show that the percentage of frequent used bikes decreases by 33.6% on usage number and 28.6% on usage time.

In this demo, we propose I-Navi, an Indoor Navigation system which leverages the gradient WiFi signal. To be more adaptive to time-variant RSSI and enrich information dimension, I-Navi exploits a three-step backward gradient binary method. Meanwhile, we adopt a lightweight online dynamic time warping (DTW) algorithm to achieve real-time navigation. We fully implemented I-Navi on smartphones and conducted extensive experiments in a five-story campus building and a newly opened two-floor shopping mall with a 90% accuracy of 2m and 3.2m achieved at two places.

Barrier coverage has been widely adopted to prevent unauthorized invasion of important areas in sensor networks. As sensors are typically placed outdoors, they are susceptible to getting faulty. Previous works assumed that faulty sensors are easy to recognize, e.g., they may stop functioning or output apparently deviant sensory data. In practice, it is, however, extremely difficult to recognize faulty sensors as well as their invalid output. We, in this paper, propose a novel fault-tolerant intrusion detection algorithm (TrusDet) based on trust management to address this challenging issue. TrusDet comprises of three steps: i) sensor-level detection, ii) sink-level decision by collective voting, and iii) trust management and fault determination. In the Step i) and ii), TrusDet divides the surveillance area into a set of fine- grained subareas and exploits temporal and spatial correlation of sensory output among sensors in different subareas to yield a more accurate and robust performance of barrier coverage. In the Step iii), TrusDet builds a trust management based framework to determine the confidence level of sensors being faulty. We implement TrusDet on HC- SR501 infrared sensors and demonstrate that TrusDet has a desired performance.

Recently, narrowband Internet of Things (NB-IoT), one of the most promising low power wide area (LPWA) technologies, has attracted much attention from both academia and industry. It has great potential to meet the huge demand for machine-type communications in the era of IoT. To facilitate research on and application of NB-IoT, in this paper, we design a system that includes NB devices, an IoT cloud platform, an application server, and a user app. The core component of the system is to build a development board that integrates an NB-IoT communication module and a subscriber identification module, a micro-controller unit and power management modules. We also provide a firmware design for NB device wake-up, data sensing, computing and communication, and the IoT cloud configuration for data storage and analysis. We further introduce a framework on how to apply the proposed system to specific applications. The proposed system provides an easy approach to academic research as well as commercial applications.

Pricing and task allocation are vital to improving the efficiency in mobile crowdsensing, an emerging human-in-the-loop application paradigm. Previous studies focused on incentive mechanism design for specific sensing applications where one party (either task initiators or platform) can dominate the pricing and task allocation process. These results, however, are not applicable to a free crowdsensing market where multiple task initiators and task participants (mobile users), as peers, are engaged to maximize their own interests. New incentive mechanisms are pressingly needed to produce a solution, so that the interests of all participating parties can be considered. In this paper, appealing to exchange economy theory, we employ the notion of “Walrasian Equilibrium” as a comprehensive metric, at which there exists a price vector for mobile users and an allocation for task initiators such that the allocation is Pareto optimal and the market gets cleared (i.e., all sensing tasks are performed). We consider a standard model where the utility function for sensing quality is monotonically increasing, differentiable, and concave, and the payoff function for a mobile user is linear. To address the problem, we first characterize the supply-demand pattern for a given price vector, which is the subset of mobile users selected by each task initiator to perform the task. We then devise methods for validating the existence of a Walrasian Equilibrium within each supply-demand pattern. One key step is to divide the space of prices into a collection of appropriate cells, based on the hyperplane arrangement, so that each cell has a unique supply-demand pattern. We devise an algorithm that can find a Walrasian Equilibrium in polynomial time, for a case of practical interest where the classes of mobile devices are bounded. Based on the insight, we further consider the general case and design an efficient pattern search (EPS) algorithm to reduce the search space, thus accelerating the search process accordingly. This is realized by choosing the supply-demand pattern which is closer to the “Walrasian Equilibrium” than the pattern in previous iteration in the search process. Our results show that EPS can find an $\epsilon $ -approximation Walrasian Equilibrium in polynomial time for the general case, given a constant $\epsilon $ .

Jamming is a typical attack by exploiting the nature of wireless communication. Lots of researchers are working on improving energy-efficiency of jamming attack from the attacker’s view. Whereas, in the low-duty-cycle wireless sensor networks where nodes stay asleep most of time, the design of jamming attack becomes even more challenging especially when considering the stochastic transmission pattern arising from both the clock drift and other uncertainties. In this paper, we propose LearJam, a novel learning-based jamming attack strategy against low-duty-cycle networks, which features the two-phase design consisting of the learning phase and attacking phase. Then in order to degrade the network throughput to the maximal degree, LearJam jointly optimizes these two phases subject to the energy constraint. Moreover, such process of optimization is operated iteratively to accommodate the requirement of practical implementation. Conversely, we also discuss how the state-of-the-art mechanisms can defend against LearJam, which will aid the researchers to improve the security of low-duty-cycle networks. Extensive simulations show that our design achieves significantly higher number of successful attacks and reduces the network’s throughput considerably, especially in a sparse low-duty-cycle network, compared with some typical jamming strategies.

Wireless charging is a promising way to solve the energy constraint problem in sensor networks. While extensive efforts have been made to improve the performance of charging and communication in wireless rechargeable sensor networks (WRSNs), little has been done to address the operation scheduling problem. To fill this void, we propose a joint energy replenishment and scheduling mechanism so as to maximize the network lifetime while making strict sensing guarantees in the WRSN. We first formulate the problem in a general 2-D space and prove its NP-completeness. We then devise an f-approximate scheduling mechanism by transforming the classical minimum set cover problem and develop an optimal energy-replenish strategy based on the energy consumption of nodes returned by the scheduling mechanism. Large-scale simulation results validate our design and show a 39.2% improvement of network lifetime over a baseline method.

We study data gathering problem in Rechargeable Sensor Networks (RSNs) with a mobile sink, where rechargeable sensors are deployed into a region of interest to monitor the environment and a mobile sink travels along a pre-defined path to collect data from sensors periodically. In such RSNs, the optimal data gathering is challenging because the required energy consumption for data transmission changes with the movement of the mobile sink and the available energy is time-varying. In this paper, we formulate data gathering problem as a network utility maximization problem, which aims at maximizing the total amount of data collected by the mobile sink while maintaining the fairness of network. Since the instantaneous optimal data gathering scheme changes with time, in order to obtain the globally optimal solution, we first transform the primal problem into an approximate network utility maximization problem by shifting the energy consumption conservation and analyzing necessary conditions for the optimal solution. As a result, each sensor does not need to estimate the amount of harvested energy and the problem dimension is reduced. Then, we propose a Distributed Data Gathering Approach (DDGA), which can be operated distributively by sensors, to obtain the optimal data gathering scheme. Extensive simulations are performed to demonstrate the efficiency of the proposed algorithm.

RFID technology has been widely adopted for real-world applications, such as warehouse management, logistic control, and object tracking. This paper focuses on a new angle of applying RFID technology-monitoring the temporal change of a tag set in a certain region, which is called churn estimation. This problem is to provide quick estimations on the number of new tags that have entered a monitored region, and the number of pre-existing tags that have departed from the region, within a predefined time interval. The traditional cardinality estimator for a single tag set cannot be applied here, and the conventional tag identification protocol that collects all tag IDs takes too much time, especially when the churn estimation needs to perform frequently to support real-time monitoring. This paper will take a new solution path, in which a reader periodically scans the tag set in a region to collect their compressed aggregate information in the form of empty/singleton/collision time slots. This protocol can reduce the time cost of attaining pre-set accuracy by at least 35%, when comparing with a previous work that uses only the information of idle/busy slots. Such a dramatic improvement is due to our awareness of collision slot state and the full utilization of slot state changes. Our proposed churn estimator, as shown by the extensive analysis and simulation studies, can be configured to meet any pre-set accuracy requirement with a statistical error bound that can be made arbitrarily small.

Cooperative geolocation has attracted significant research interests in recent years. A large number of localization algorithms rely on the availability of statistical knowledge of measurement errors, which is often difficult to obtain in practice. Compared with the statistical knowledge of measurement errors, it can often be easier to obtain the measurement error bound. This paper investigates a localization problem assuming unknown measurement error distribution except for a bound on the error. We first formulate this localization problem as an optimization problem to minimize the worst case estimation error, which is shown to be a nonconvex optimization problem. Then, relaxation is applied to transform it into a convex one. Furthermore, we propose a distributed algorithm to solve the problem, which will converge in a few iterations. Simulation results show that the proposed algorithms are more robust to large measurement errors than existing algorithms in the literature. Geometrical analysis providing additional insights is also provided.

Electric bikes (e-bikes) are booming in China, providing a flexible and energy-efficient trip mode for people all around the country. However, the problems such as traffic accidents and disrupting social order caused by e-bikes bring much worry to the government operators. And the behavior of e-bike users also attracts the sociological researchers' interest due to their characteristics (e.g. lower income and higher mobility). In order to facilitate government operators in monitoring e-bikes and to help sociological researchers study the behavior of e-bike users, we develop a data analysis and visualization system based on large-scale e-bike data. We analyze the e-bike mobility and e-bike user behavior, and design a visual interface that allows interaction with the analysis results. Our system not only provides the function of e-bike monitoring but also serves as a platform for mobility analysis and user behavior analysis.

In this paper, we study full-view coverage in camera sensor networks, by exploiting their limited mobility of orientation rotation. We focus on the fairness based coverage maximization problem, i.e., how to schedule the orientations of the camera sensors to maximize the minimum accumulated full-view coverage time of target points. To solve this problem, we first try to reduce the space dimension of orientation search by dividing the orientation space into a set of discrete regions. We select the minimum number of sensing regions that camera sensors should rotate to in order to ensure the full-view coverage of all target points. Next, we attempt to understand the relationship of full-view coverage among the target points, which are spatially coupled. Based on results in these two steps, we devise an algorithm to solve the problem based on "largest demand first serve" principle. We provide extensive simulations to demonstrate the desired performance of the proposed algorithms.

Recently, millimeter wave wireless communications have emerged as one of the most promising technologies to significantly improve the throughput of massive multiple-input multiple-output (MIMO) system. Since high-frequency channels are quite easily attenuated in space, beamforming technology based on the massive MIMO is introduced to transmit the millimeter waves in a very narrow directional beam. One challenging problem in this is how to optimize the overall throughput by allocating the available antenna resources to different mobile users. In this paper, we tackle such a difficult problem and formulate it as an antenna selection combinatorial optimization, which is NP-hard. We first begin with the simplified one-dimension case, i.e., all antennas are deployed on a single line segment. We design a novel iterative greedy antenna selection algorithm (iGAS), that allocates antennas to different users in an iterative way, with each iteration maximizing the marginal increase of overall throughput. We then generalize our result to the two-dimension case. Simulation results are provided to demonstrate the efficiency of the proposed algorithms.

Non-line-of-sight (NLOS) propagation, which widely exists in wireless systems, will degrade the performance of wireless positioning system if it is not taken into consideration in the localization algorithm design. The 3rd Generation Partnership Project (3GPP) suggests that the probabilities of line-of-sight (LOS) and NLOS are related to the distance between the receiver and the transmitter. In this paper, we propose a Maximum Likelihood Estimator (MLE) for localization, which incorporates the distance dependent LOS/NLOS probabilities. Then, the position error bound is derived using Cramer-Rao Lower Bound (CRLB). Through numerical analysis, the impact of NLOS propagation on the position error bound is evaluated. The performance of our proposed algorithm is verified by real world experimental data.

As an innovative mobility strategy, public bike-sharing has grown dramatically worldwide. Though providing convenient, low-cost and environmental-friendly transportation, the unique features of bike-sharing systems give rise to problems to both users and operators. The primary issue among these problems is the uneven distribution of bicycles caused by the ever-changing usage and (available) supply. This bicycle imbalance issue necessitates efficient bike re-balancing strategies, which depends highly on bicycle mobility modeling and prediction. In this paper, for the first time, we propose a spatio-temporal bicycle mobility model based on historical bike-sharing data, and devise a traffic prediction mechanism on a per-station basis with sub-hour granularity. We extensively evaluated the performance of our design through a one-year dataset from the world's largest public bike-sharing system (BSS) with more than 2800 stations and over 103 million check in/out records. Evaluation results show an 85 percentile relative error of 0.6 for both check in and check out prediction. We believe this new mobility modeling and prediction approach can advance the bike re-balancing algorithm design and pave the way for the rapid deployment and adoption of bike-sharing systems across the globe.

Micro-powered wireless embedded devices are widely used in many application domains. Their efficiency in practice, however, is significantly constrained by the dual limitations of low harvesting rates and tiny energy buffer. Recent research presents a network stack that efficiently fragments a large packet into many smaller packets that can fit within the available energy in the energy buffer of limited size. While this fragmentation technique represents a major step forward in solving the minuscule energy budget problem, it also introduces a tremendous practical challenge where potentially many fragmented packets belonging to different devices may contend for the communication channel. Designing purely heuristic-based packet transmission protocol is undesirable because the resulting per-packet and end-to-end transmission delay are unknown, thus causing unpredictable system performance which is unacceptable for many applications with real-time constraints. In this paper, we first formulate this packet transmission scheduling problem considering physical properties of the charging and transmission processes. We then develop a novel packet prioritization and transmission protocol NERF that yields tight and predictable delay bounds for transmitting packets from multiple micropowered devices to a charger. We have implemented our protoco on top of the WISP 4.1 platform and the SPEEDWAY RFID READER, and conducted validation experiments. Our experiments validate the correctness of our implementation and show that NERF can reduce the total collection delay by 40% when compared to an existing protocol ALOHA. We have also performed extensive data trace-driven simulations. Simulation results demonstrate the effectiveness of our proposed protocol. On average, our protocol yields an over 30%improvement in terms of runtime transmission delay compared to existing methods, while being able to guarantee tight and provable response time bounds.

We present a system utilizing narrow band software defined radio to detect the moving human through walls, and give some motion details, such as motion orientation which includes relative moving direction. To achieve high accuracy, FindIt applies Short Time Fourier Transform (STFT) and statistical methods to received signals. In order to adapt to different environments, FindIt uses clustering and classification methods to determine thresholds. Moreover, FindIt provides user-friendly real-time detection results, which can be used as a trigger of high-level functions.

ZigBee has been widely recognized as an important enabling technique for Internet of Things (IoT). However, the ZigBee nodes are normally resource-limited, making the network susceptible to a variety of security threats. This paper closely investigates a severe attack on ZigBee networks termed as ghost, which leverages the underlying vulnerabilities of the IEEE 802.15.4 security suites to deplete the energy of the nodes. We show that the impact of ghost is very large and that it can facilitate a variety of threats including denial of service and replay attacks. We highlight that merely deploying a standard suite of advanced security techniques does not necessarily guarantee improved security, but instead might be leveraged by adversaries to cause severe disruption in the network. We propose several recommendations on how to localize and withstand the ghost and other related attacks in ZigBee networks. Extensive simulations are provided to show the impact of the ghost and the performance of the proposed recommendations. Moreover, physical experiments also have been conducted and the observations confirm the severity of the impact by the ghost attack. We believe that the presented work will aid the researchers to improve the security of ZigBee further.

This paper studies the network utility maximization (NUM) problem in static-routing rechargeable sensor networks (RSNs) with the link and battery capacity constraints. The NUM problem is very challenging as these two constraints are typically coupling in RSNs, which cannot be directly tackled. Existing works either do not fully consider the two coupled constraints together, or heuristically remove the temporally coupled part, both of which are not practical, and will also degrade the network performance. In this paper, we attempt to jointly optimize the sampling rate and battery level by carefully tackling the spatiotemporally coupled link and battery capacity constraints. To this end, we first decouple the original problem equivalently into separable subproblems by means of dual decomposition. Then, we propose a distributed algorithm in the context of joint rate and battery control, called decouple spatiotemporally-coupled constraint (DSCC), which can converge to the globally optimal solution. Numerical results, based on the real solar data, demonstrate that the proposed algorithm always achieves higher network utility than existing approaches. In addition, the impact of link/battery capacity and initial battery level on the network utility is further investigated.

We investigate a localization problem using time-difference-of-arrival measurements with unknown and bounded measurement errors. Different from most existing algorithms, we consider the minimization of the worst-case position estimation error to improve the robustness of the algorithm. The localization problem is formulated as a nonconvex optimization problem. We adopt semidefinite relaxation to relax the original problem into a convex optimization problem, which can be solved using existing semidefinite program solvers. Simulation results show that our proposed algorithm has lower worst-case position estimation error than other existing algorithms.

Most existing works investigate consensus with noise following a certain distribution, e.g., Gaussian distribution, with fixed expectation and variance, which may not be satisfied in practical applications. This paper investigates the discrete system consensus under bounded noise, which is important and practical problem. We first provide necessary and sufficient conditions for the convergence of consensus under bounded noise. To be more general, we derive an analytical bound to show the max-min difference between the nodes' states when the general consensus algorithm converges to a stable state. Then, a novel consensus algorithm, fast consensus under bounded noise (FCBN), is proposed to eliminate the accumulative error caused by the bounded noise. It is proved that FCBN has a faster convergence speed and a higher consensus accuracy than general consensus algorithms. Extensive simulations demonstrate the effectiveness of the proposed algorithm.

With the increasing trend in adoption of plug-in hybrid and plug-in electric vehicles, they will play a prominent role in the future electric energy market by acting as responsive loads to increase the grid stability and facilitate the integration of renewables. However, due to the large number of controllable devices in the future grid, central vehicle to grid (V2G) management would be challenging and vulnerable to single points of failure. This paper introduces a novel distributed approach for optimal management of unidirectional V2G considering multiple energy suppliers. Each charging station as well as each energy supplier is equipped with a local price regulator to control the price paid to the energy suppliers and the price paid by the vehicles through coordination with their neighbors. In response to the updated prices, the vehicles adjust their charging rates and energy suppliers adjust their production to maximize their benefit. The main advantages of the proposed approach are that it manages unidirectional V2G in a fully distributed way considering multiple energy suppliers and vehicles, and it converges to the global optimum despite the greedy behavior of the individuals.

Wireless sensor networks have been used in many mobile applications such as wildlife tracking and participatory urban sensing. Because of the combination of high mobility and low-duty-cycle operations, it is a challenging issue to reduce discovery delay among mobile nodes, so that mobile nodes can establish connection quickly once they are within each other's vicinity. Existing discovery designs are essentially pairwise based, in which discovery is passively achieved when two nodes are prescheduled to wake up at the same time. In contrast, this work reduces discovery delay significantly by proactively referring wake-up schedules among a group of nodes. Since proactive references incur additional overhead, we introduce a novel selective reference mechanism based on spatiotemporal properties of neighborhood and the mobility of nodes. Our quantitative analysis indicates that the discovery delay of our group-based mechanism is significantly smaller than that of the pairwise one. Our testbed experiments using 40 sensor nodes and extensive simulations confirm the theoretical analysis, showing one order of magnitude reduction in discovery delay compared with legacy pairwise methods in dense, uniformly distributed sensor networks with at most 8.8 percent increase in energy consumption.

Limited energy in each node is the major design constraint in wireless sensor networks (WSNs). To overcome this limit, wireless rechargeable sensor networks (WRSNs) have been proposed and studied extensively over the last few years. In a typical WRSN, batteries in sensor nodes can be replenished by a mobile charger that periodically travels along a certain trajectory in the sensing area. To maximize the charged energy in sensor nodes, one fundamental question is how to control the traveling velocity of the charger. In this paper, we first identify the optimal velocity control as a key design objective of mobile wireless charging in WRSNs. We then formulate the optimal charger velocity control problem on arbitrarily-shaped irregular trajectories in a 2D space. The problem is proved to be NP-hard, and hence a heuristic solution with a provable upper bound is developed using novel spatial and temporal discretization. We also derive the optimal velocity control for moving the charger along a linear (1D) trajectory commonly seen in many WSN applications. Extensive simulations show that the network lifetime can be extended by 2.5× with the proposed velocity control mechanisms.

In rechargeable sensor networks (RSNs), energy harvested by sensors should be carefully allocated for data sensing and data transmission to optimize data gathering due to time-varying renewable energy arrival and limited battery capacity. Moreover, the dynamic feature of network topology should be taken into account, since it can affect the data transmission. In this paper, we strive to optimize data gathering in terms of network utility by jointly considering data sensing and data transmission. To this end, we design a data gathering optimization algorithm for dynamic sensing and routing (DoSR), which consists of two parts. In the first part, we design a balanced energy allocation scheme (BEAS) for each sensor to manage its energy use, which is proven to meet four requirements raised by practical scenarios. Then in the second part, we propose a distributed sensing rate and routing control (DSR2C) algorithm to jointly optimize data sensing and data transmission, while guaranteeing network fairness. In DSR2C, each sensor can adaptively adjust its transmit energy consumption during network operation according to the amount of available energy, and select the optimal sensing rate and routing, which can efficiently improve data gathering. Furthermore, since recomputing the optimal data sensing and routing strategies upon change of energy allocation will bring huge communications for information exchange and computation, we propose an improved BEAS to manage the energy allocation in the dynamic environments and a topology control scheme to reduce computational complexity. Extensive simulations are performed to demonstrate the efficiency of the proposed algorithms in comparison with existing algorithms.

Abstract Energy detection is widely used in cognitive radio due to its low complexity. One fundamental challenge is that its performance degrades in the presence of noise uncertainty, which inevitably occurs in practical implementations. In this work, three novel detectors based on uniformly distributed noise uncertainty as the worst‐case scenario are proposed. Numerical results show that the new detectors outperform the conventional energy detector with considerable performance gains. Copyright © 2015 John Wiley & Sons, Ltd.

Recently, the industrial wireless protocols have been widely used around the world. However, the unreliable communication media between the sensors and the central controller renders the wireless signal channel vulnerable to many attacks. Various efforts have been devoted to study the influence of specific malicious attacks from the aspect of theoretical investigation based on different assumptions. This paper focuses on verifying the optimal Denial-of-Service (DoS) jamming attack strategy on a class of wireless industrial control system from the view of experiments. We first introduce typical control system model and DoS attack model, and an optimal DoS attack schedule against LQG control based on these models. Then, we establish a semi-physical security testbed which consists of virtual plant, physical controller and communication process. We also realize wireless DoS attacks by exploiting the USRP device. Through extensive experiments and analysis, we investigate the performance of different DoS attack strategies on the LQG control system over an inverted pendulum.

The size of target will induce a degradation of tracking performance, which has been neglected for simplicity in most previous studies. In multiple target tracking, occlusions will be caused by target size effect, one target can become a moving obstacle blocking the direct channel between the anchor and another target. In this paper, the data association problem in multiple target tracking is investigated. To reduce the computational complexity of traditional Joint Probabilistic Data Association (JPDA) algorithm, a modified JPDA algorithm is proposed to execute data association in multiple target tracking by utilizing the information of occlusion conditions, which is identified by a three-step algorithm. Simulation results show that the proposed algorithm is with good tracking performance and low computational complexity.

By taking advantage of active radio waves, radar sensors can provide high-accuracy target detection over traditional passive sensors. In this paper, we study barrier coverage in bistatic radar sensor networks (BRSNs), which consist of a set of transmitter radars and receiver radars. Barrier coverage in BRSNs is much more difficult than that in traditional sensor networks as the sensing area of a bistatic radar depends on the positions of both transmitter and receiver, and is typically a Cassini oval. Moreover, different transmitters and receivers can pair with each other by choosing the same frequency and thus the sensing network topology can be quite different in different time slots. To tackle this challenge, we first investigate the characteristic of the ε-covered area of a bistatic radar, then we represent a bistatic radar with a virtual point at the middle point of the line segment formed by the transmitter and receiver. With these representations, we formulate the barrier coverage problem in BRSNs as (k, ε)-Minimum Weight Barrier Coverage Problem ((k, ε)-MWBCP). By constructing a directed coverage graph, we transform the (k, ε)-MWBCP into finding k node-disjoint shortest paths and propose an energy-efficient algorithm called (k, ε)-MWBCA to solve the problem within polynomial time. Extensive simulations are conducted to demonstrate the performance of our proposed algorithm.

Wireless charging is a promising technology for provisioning dynamic power supply in wireless rechargeable sensor networks (WRSNs). The charging equipment can be carried by some mobile nodes to enhance the charging flexibility. With such mobile chargers (MCs), the charging process should simultaneously address the MC scheduling, the moving and charging time allocation, while saving the total energy consumption of MCs. However, the efficient solutions that jointly solve those challenges are generally lacking in the literature. First, we investigate the multi-MC coordination problem that minimizing the energy expenditure of MCs while guaranteeing the perpetual operation of WRSNs, and formulate this problem as a mixed-integer linear program (MILP). Second, to solve this problem efficiently, we propose a novel decentralized method which is based on Benders decomposition. The multi-MC coordination problem is then decomposed into a master problem (MP) and a slave problem (SP), with the MP for MC scheduling and the SP for MC moving and charging time allocation. The MP is being solved by the base station (BS), while the SP is further decomposed into several sub-SPs and being solved by the MCs in parallel. The BS and MCs coordinate themselves to decide an optimal charging strategy. The convergence of proposed method is analyzed theoretically. Simulation results demonstrate the effectiveness and scalability of the proposed method.

In this demo, we propose Phone meter which leverages the RF energy harvesting technologies to measure the strength of Electromagnetic Field (EMF). To this end, Phone meter combines EMF sensor with the smartphone through audio interface without any modifications to the phone. We fully implement the low-cost Phone meter and conduct extensive experiments to prove the functionality of Phone meter. Phone meter achieves about 13:7% relative error in average compared with the industrial-grade spectrum analyzer with significantly reduced the costs.

Although GPS has become a standard component of smartphones, providing accurate navigation during the last portion of a trip remains an important but unsolved problem. Despite extensive research on localization, the limited resolution of a map imposes restrictions on the navigation engine in both indoor and outdoor environments. To bridge the gap between the end position obtained from legacy navigation services and the real destination, we propose FollowMe, a "last-mile" navigation system to enable plug-and-play navigation in indoor and semi-outdoor environments. FollowMe exploits the ubiquitous, stable geomagnetic field and natural walking patterns to navigate the users to the same destination taken by an earlier traveler. Unlike existing localization and navigation systems, FollowMe is infrastructure-free, energy-efficient and cost-saving. We implemented FollowMe on smartphones, and evaluated it in a four-story campus building with a testing area of 2000m2. Our experimental results with 5 users show that 95% of spatial errors during navigation were 2m or less with at least 50% energy savings over a benchmark system.

This paper explores the correlation among three kinds of social relationships: face-to-face social relationship, online social relationship, and self-report social relationship. An experiment was carried out to collect users' three-dimensional social data: real-world mobile trace data, virtual-world online social data, and self-report social data. By analyzing network structure, we find that friendship in online social networks can better describe self-report friendship compared to friendship created by frequent physical encounters. Several supervised classifiers with the combination of features extracted from mobile trace data and online social data are used to predict the self-report social relationship under different social strengths. Results show that the proposed model can correctly predict more than 80% friends under strongest social tie strength. What is more, we define social popularity according to social relationships self-reported by users. By comparing social popularity with online and offline social behaviors, we find diversity in weekend is a good measure to describe social popularity.

As the next generation power grid, smart grid is characterized as an informationized system, and demand response is one of its important features to deal with the ever-increasing peak energy usage. However, the supply capacity and required demand make the demand response problem with both spatially and temporally coupled constraints, which, to the best of our knowledge, has not been thoroughly investigated in a distributed manner. The complexity lies in how to guarantee privacy and convergence of distributed algorithms. Aiming at this challenge, in this paper, we first propose a distributed algorithm, which is based on dual decomposition and does not require each user to reveal his/her private information. Then, the convergence analysis is conducted to provide guidance on how to choose the proper step size; through which, we notice that the convergence speed of the subgradient projection method is not fast enough and it is highly dependent on the choice of the step size. Therefore, to increase the convergence rate of the distributed algorithm, we further propose a fast approach based on binary search. Finally, the distributed algorithms are illustrated by numerical simulations and the extensive comparison results validate the better performance of the fast approach.

The smart grid is widely considered to be the informationization of the power grid. As an essential characteristic of the smart grid, demand response can reschedule the users' energy consumption to reduce the operating expense from expensive generators, and further to defer the capacity addition in the long run. This survey comprehensively explores four major aspects: 1) programs; 2) issues; 3) approaches; and 4) future extensions of demand response. Specifically, we first introduce the means/tariffs that the power utility takes to incentivize users to reschedule their energy usage patterns. Then we survey the existing mathematical models and problems in the previous and current literatures, followed by the state-of-the-art approaches and solutions to address these issues. Finally, based on the above overview, we also outline the potential challenges and future research directions in the context of demand response.

The wireless rechargeable sensor network is a promising platform for long-term applications such as inventory management, supply chain monitoring, and so on. For these applications, sensor localization is one of the most fundamental challenges. Different from a traditional sensor node, a wireless rechargeable sensor has to be charged above a voltage level by the wireless charger in order to support its sensing, computation, and communication operations. In this work, we consider the scenario where a mobile charger stops at different positions to charge sensors and propose a novel localization design that utilizes the unique Time of Charge (TOC) sequences among wireless rechargeable sensors. Specifically, we introduce two efficient region dividing methods, Internode Division and Interarea Division , to exploit TOC differences from both temporal and spatial dimensions to localize individual sensor nodes. To further optimize the system performance, we introduce both an optimal charger stop planning algorithm for the single-sensor case and a suboptimal charger stop planning algorithm for the generic multisensor scenario with a provable performance bound. We have extensively evaluated our design by both testbed experiments and large-scale simulations. The experiment and simulation results show that by as less as five stops, our design can achieve sub-meter accuracy and the performance is robust under various system conditions.

Multiple channels in Wireless Sensor Networks (WSNs) are often exploited to support parallel transmission and to reduce interference. However, the extra overhead posed by the multi-channel usage coordination dramatically challenges the energy-constrained WSNs. In this paper, we propose a Regret Matching based Channel Assignment algorithm (RMCA) to address this challenge, in which each sensor node updates its choice of channels according to the historical record of these channels’ performance to reduce interference. The advantage of RMCA is that it is highly distributed and requires very limited information exchange among sensor nodes. It is proved that RMCA converges almost surely to the set of correlated equilibrium. Moreover, RMCA can adapt the channel assignment among sensor nodes to the time-variant flows and network topology. Simulations show that RMCA achieves better network performance in terms of both delivery ratio and packet latency than CONTROL, MMSNand randomized CSMA. In addition, real hardware experiments are conducted to demonstrate that RMCA is easy to be implemented and performs better.

Recently, the content-based publish/subscribe (pub/sub) paradigm has been gaining popularity in opportunistic mobile networks (OppNets) for its flexibility and adaptability. Since nodes in OppNets are controlled by humans, they often behave selfishly. Therefore, stimulating nodes in selfish OppNets to collect, store, and share contents efficiently is one of the key challenges. Meanwhile, guaranteeing the freshness of contents is also a big problem for content dissemination in OppNets. In this paper, in order to solve these problems, we propose an incentive-driven and freshness-aware pub/sub Content Dissemination scheme, called ConDis, for selfish OppNets. In ConDis, the Tit-For-Tat (TFT) scheme is employed to deal with selfish behaviors of nodes in OppNets. Moreover, a novel content exchange protocol is proposed when nodes are in contact. Specifically, during each contact, the exchange order is determined by the content utility, which represents the usefulness of a content for a certain node, and the objective of nodes is to maximize the utility of the content inventory stored in their buffer. Extensive realistic trace-driven simulation results show that ConDis is superior to other existing schemes in terms of total freshness value, total delivered contents, and total transmission cost.

Scalar field mapping has many applications including environmental monitoring, search and rescue, etc. In such applications, there is a need to achieve a certain level of confidence regarding the estimates of the scalar field. In this paper, a cooperative and active sensing framework is developed to enable scalar field mapping using multiple mobile sensor nodes. The cooperative and active controller is designed via the real-time feedback of the sensing performance to steer the mobile sensors to new locations in order to improve the sensing quality. During the movement of the mobile sensors, the measurements from each sensor node and its neighbors are fused with the corresponding confidences using distributed consensus filters. As a result, an online map of the scalar field is built while achieving a certain level of confidence of the estimates. We conducted computer simulations to validate and evaluate our proposed algorithms.

The IEEE 802.15.4 standard, which supports low-cost communications, has been applied in a variety of wireless networks. Developing accurate analytical models for the IEEE 802.15.4 medium access control (MAC) protocol is critical for the design and performance evaluation of such networks. Periodic traffic is a common traffic pattern generated in many practical application scenarios, for which most existing analytical models assuming either saturated or random network traffic patterns become inapplicable. In this paper, we develop an accurate and scalable analytical model to analyze the IEEE 802.15.4 MAC protocol with the periodic traffic. Our model can accurately capture the protocol stochastic behavior in each period in scenarios such as with or without retransmissions and with single clear channel assessment (CCA) or double CCAs. Extensive simulations are conducted to validate the proposed model by both transient and aggregate performance evaluations, and the results show that the model captures MAC behavior with periodic traffic accurately. We also discuss about extending the proposed model to account for heterogeneous scenarios and the hidden node problem.

Three new approximations to the probability density function (PDF) of the symmetric alpha stable (SαS) distribution are proposed. The first two approximations use rational functions while the third approximation uses power functions. Using these approximations, new detectors for signals in symmetric alpha stable noise are also derived. Numerical results show that all these new approximations have good accuracies. Numerical results also show that the new detectors based on these approximations outperform the existing detectors, especially when the characteristic exponent of the symmetric alpha stable distribution is small.

Abstract This paper considers the problem of localizing a group of targets whose number is unknown by wireless sensor networks. At each time slot, to save energy and bandwidth resources, only part of sensor nodes are scheduled to activate to remain continuous monitoring of all the targets. The localization problem is formulated as a sparse vector recovery problem by utilizing the spatial sparsity of targets’ location. Specifically, each activated sensor records the RSS values of the signals received from the targets and sends the measurements to the sink node where a compressive sampling‐based localization algorithm is conducted to recover the number and locations of targets. We decompose the problem into two sub‐problems, namely, which sensor nodes to activate, and how to utilize the measurements. For the first subproblem, to reduce the effect of measurement noise, we propose an iterative activation algorithm to re‐assign the activation probability of each sensor by exploiting the previous estimate. For the second subproblem, to further improve the localization accuracy, a sequential recovery algorithm is proposed, which conducts compressive sampling on the least squares residual of the previous estimate such that all the previous estimate can be utilized. Under some mild assumptions, we provide the analytical performance bound of our algorithm, and the running time of proposed algorithm is given subsequently. Simulation results demonstrate the effectiveness of our algorithms.Copyright © 2013 John Wiley & Sons, Ltd.

In wireless rechargeable sensor networks (WRSN), rechargeable sensor nodes harvest ambient RF energy from power sources such as the RFID readers. However, the simultaneous transmissions may cause severe communication collisions. Different from traditional approaches which mainly resolve such collisions at the MAC layer, in this work we optimize the deployment of RFID reader in order to avoid the communication collisions by exploiting the differences in the time of charge among rechargeable sensor nodes. Specifically, when the reader is able to cover the whole deployment field, an efficient collision-free solution with proved optimality is presented to minimize the communication delay in the network. Simulation results are employed to verify the proposed algorithm.

Barrier coverage in sensor networks has attracted much attention in recent years. Existing results revealed that sensor mobility can remarkably improve the coverage performance of sensor networks. Considering the high manufacture cost of mobile sensors, in this paper we propose to tradeoff the barrier coverage performance and deployment budget by employing a wireless sensor and actor network (WSAN), wherein an actor is used to move static sensors around in order to enhance the barrier coverage performance. We first formulate the barrier coverage problem in WSAN and propose a new coverage metric to evaluate the barrier coverage performance. Then we design an efficient actor movement scheme, S-AMS, for the case where the number of monitoring points can be divided by the number of available sensors. By exploiting the actor's mobility and clustering procedure, S-AMS is able to significantly improve barrier coverage. Based on the insight from S-AMS, we design G-AMS for the general case. We show that S-AMS achieves asymptotically optimal solution for the special case and G-AMS obtains close-to-optimal solution for the general case. Extensive simulations are conducted to demonstrate the performance of our proposed schemes.

Data collection by multiple sinks is a fundamental problem in wireless sensor networks. Existing work focused on designing optimal offline algorithms provided that the number and positions of sensors and sinks are predetermined. This may not be practical as, though sensors are cheap, sinks are quite expensive in reality. A more practical scenario is that sinks are deployed step by step during the network operation due to the budget constraint, and we do not know the number, positions and capacities of sinks in prior. In this paper we investigate such an optimal data collection problem by multiple sinks, and design a near-optimal online algorithm via primal-dual approach, requiring very little priori knowledge. We theoretically derive the competitive ratio and show how to improve it by finding the optimal sink location region with an approximation ratio. Extensive simulations are conducted to verify the performance of the proposed online algorithm.

Crowdsensing offers an efficient approach to meet the demand in large scale sensing applications. In crowdsensing, it is of great interest to find the optimal task allocation, which is challenging since sensing tasks with different requirements of quality of sensing are typically associated with specific locations and mobile users are constrained by time budgets. We show that the allocation problem is NP hard. We then focus on approximation algorithms, and devise an efficient local ratio based algorithm (LRBA). Our analysis shows that the approximation ratio of the aggregate rewards obtained by the optimal allocation to those by LRBA is 5. This reveals that LRBA is efficient, since a lower (but not tight) bound on the approximation ratio is 4. We also discuss about how to decide the fair prices of sensing tasks to provide incentives since mobile users tend to decline the tasks with low incentives. We design a pricing mechanism based on bargaining theory, in which the price of each task is determined by the performing cost and market demand (i.e., the number of mobile users who intend to perform the task). Extensive simulation results are provided to demonstrate the advantages of our proposed scheme.

Wireless rechargeable sensor network is a promising platform for long-term applications such as inventory management, supply chain monitoring and so on. For these applications, sensor localization is one of the most fundamental challenges. Different from traditional sensor node, wireless rechargeable sensor has to be charged above a voltage level by the wireless charger in order to support its sensing, computation and communication operations. In this work, we consider the scenario where a mobile charger stops at different positions to charge sensors, and propose a novel localization design that utilizes the unique Time of Charge (TOC) sequences among wireless rechargeable sensors. Specifically, we introduce two efficient region dividing methods, Inter-node Division and Inter-area Division, to exploit TOC differences from both temporal and spatial dimensions to localize individual sensor nodes. To further optimize the system performance, we introduce both an optimal charger stop planning algorithm for single sensor case and a suboptimal charger stop planning algorithm for the generic multisensor scenario with a provable performance bound. We have extensively evaluated our design by both testbed experiments and large-scale simulations. The experiment and simulation results show that by as less as 5 stops, our design can achieve sub-meter accuracy and the performance is robust under various system conditions.

Low-duty-cycle network plays an crucial role in improving energy efficiency of wireless communication, where nodes stay asleep most of time. Despite energy saving, the security of low-duty-cycle networks is of great concern. The attacking strategy design becomes even more challenging considering the stochastic transmission patterns arising from both the clock drift and other uncertainties. In this paper, we propose LearJam, a novel two-phase energy-efficient learning-based jamming attack strategy against low-duty-cycle networks, where the attacker estimates the distribution of transmission period in the learning phase, and schedules its jamming attacks in the attacking phase based on this estimated distribution. We jointly optimize the learning duration and the attacking duration under the energy constraint in order to degrade the network throughput to the maximal degree. We propose simple yet effective methods to solve both the single-node and multi-node scenarios. We further discuss a state-of-the-art mechanism defending against LearJam by re-scheduling transmission pattern, which will aid the researchers to improve the security of low-duty-cycle networks. Extensive simulations show that our design achieves significantly higher number of successful attacks (increasing 38%-762%) in a sparse low-duty-cycle network compared with some traditional jamming strategies.

Different from energy harvesting which generates dynamic energy supplies, the mobile charger is able to provide stable and reliable energy supply for sensor nodes, and thus enables sustainable system operations. While previous mobile charging protocols either focus on the charger travel distance or the charging delay of sensor nodes, in this work we propose a novel Energy Synchronized Charging (ESync) protocol, which simultaneously reduces both of them. Observing the limitation of the Traveling Salesman Problem (TSP)-based solutions when nodes energy consumptions are diverse, we construct a set of nested TSP tours based on their energy consumption rates, and only nodes with low remaining energy are involved in each charging round. Furthermore, we propose the concept of energy synchronization to synchronize the charging requests sequence of nodes with their sequence on the TSP tours.

Different from energy harvesting which generates dynamic energy supplies, the mobile charger is able to provide stable and reliable energy supply for sensor nodes, and thus enables sustainable system operations. While previous mobile charging protocols either focus on the charger travel distance or the charging delay of sensor nodes, in this work we propose a novel Energy Synchronized Charging (ESync) protocol, which simultaneously reduces both of them. Observing the limitation of the Traveling Salesman Problem (TSP)-based solutions when nodes energy consumptions are diverse, we construct a set of nested TSP tours based on their energy consumptions, and only nodes with low remaining energy are involved in each charging round. Furthermore, we propose the concept of energy synchronization to synchronize the charging re- quests sequence of nodes with their sequence on the TSP tours. Experiment and simulation demonstrate ESync can reduce charger travel distance and nodes charging delay by about 30% and 40% respectively.

The Wireless Identification and Sensing Platform (WISP) has become a very promising experimental platform of wireless rechargeable sensor networks (WRSN), which integrates the sensing and computation capabilities to the traditional RFID tags. In such kind of networks, the simultaneous transmission may introduce severe communication collisions, which have attracted various research efforts for resolving such collisions at the MAC layer. However, different from existing works, we avoid such communication collisions through proper reader movement by exploiting the differences in the time of charge among rechargeable sensor nodes. We formulate the optimization problem and prove that complexity of the optimal solution is NP-hard, and propose a simple yet effective algorithm to optimize both the reader stop location and stop time for minimizing the total communication delay. Extensive simulation under different system settings show that our design can largely reduce the communication delay and outperform the baseline design by at least 20%.

Integrated with low-power micro-controllers and sensors, RFID-based wireless rechargeable sensor node is a very promising platform for applications such as inventory management, supply chain monitoring etc. Among other major research challenges, one of the most essential problems in such wireless rechargeable sensor networks is how to minimize the communication delay among RFID readers and RFID-based rechargeable nodes. While the existing works have mostly focused on the collision avoidance among RFID-based nodes, in this work we study an orthogonal approach which focuses on how to optimally plan the movement of the reader so as to minimize the communication delay in the network. To solve this problem, we introduce both an optimal solution for the linear reader movement pattern and an approximation solution for the generic two-dimensional reader move pattern with a provable approximation ratio. In addition, we also provide a solution for guaranteeing the quality of communication while minimizing the communication delay. We verify our observations through testbed experiments and extensively evaluate our design by both emulations and large-scale simulations. The results show our design can effectively reduce communication delay in wireless rechargeable sensor networks when compared with baseline solutions.

Rational approximation to the non‐linear score function used in the locally optimum detector is derived for signals corrupted by the impulsive symmetric alpha stable noise. The new approximation uses a third‐order polynomial in the numerator and a fourth‐order polynomial in the denominator, compared with the existing approximation that uses a first‐order polynomial in the numerator and a second‐order polynomial in the denominator. The parameters of the polynomials are derived using non‐linear least squares curve fitting. The relationships between the polynomial parameters and the value of the characteristic exponent are also obtained. Numerical results show that the proposed approximation has superior accuracy to the existing approximations. The proposed new approximation is then applied to the locally optimum detector by replacing the score function in the decision variable. Numerical results show that the proposed detector, optimised according to a curve‐fitting approach, outperforms previous approximations of the locally optimum detector, also optimised according to a curve‐fitting approach, for binary phase shift keying signals and in some cases for on–off keying signals.

A robust and resilient medium access control (MAC) protocol is crucial for numerous machine-type devices to concurrently access the channel in a machine-to-machine (M2M) network. Simplex (reservation- or contention-based) MAC protocols are studied in most literatures which may not be able to provide a scalable solution for M2M networks with large number of heterogeneous devices. In this paper, a scalable hybrid MAC protocol, which consists of a contention period and a transmission period, is designed for heterogeneous M2M networks. In this protocol, different devices with preset priorities (hierarchical contending probabilities) first contend the transmission opportunities following the convention-based$p$-persistent carrier sense multiple access (CSMA) mechanism. Only the successful devices will be assigned a time slot for transmission following the reservation-based time-division multiple access (TDMA) mechanism. If the devices failed in contention at previous frame, to ensure the fairness among all devices, their contending priorities will be raised by increasing their contending probabilities at the next frame. To balance the tradeoff between the contention and transmission period in each frame, an optimization problem is formulated to maximize the channel utility by finding the key design parameters: the contention duration, initial contending probability, and the incremental indicator. Analytical and simulation results demonstrate the effectiveness of the proposed hybrid MAC protocol.

We investigate the state estimation problem in cyber-physical systems (CPS) where the dynamical physical process is measured by a wireless sensor and the measurements are transmitted to a remote state estimator. It has been shown that the estimation performance strongly depends on the wireless communication quality. To enhance the estimation performance, we apply the cognitive radio technique to the system and propose a CHAnnel seNsing and switChing mEchanism (CHANCE) to explore opportunistic accessibility of multiple channels. We consider two types of wireless channels, i.e., one unlicensed channel which can be accessed freely and several licensed channels which have been pre-assigned to primary users. For the single-licensed-channel case, we develop a necessary condition for the estimation stability based on the physical process dynamics, channel quality and the channel sensing accuracy. This condition becomes also sufficient under certain conditions. We also derive the conditions under which the estimation performance is guaranteed to be improved by CHANCE. The above results are then extended to multi-licensed-channel cases. Simulations based on a particular linear system show that, the long-run mean estimation error covariance with CHANCE is at least 63% less than that without CHANCE. It is also shown that CHANCE outperforms the existing RANDOM mechanism in terms of estimation performance.

Time synchronization is a fundamental service for wireless sensor networks (WSNs). Although a number of message passing protocols can achieve satisfactory synchronization accuracy, they suffer poor scalability and high transmission overhead. An alternative approach is to utilize the global time references such as those induced by GPS and timekeeping radios. However, they require the hardware receiver to decode the out of band clock signal, which introduces extra cost and design complexity. This paper proposes a novel WSN time synchronization approach by exploiting the existing Wi-Fi infrastructure. Our approach leverages the fact that 802.15.4 sensors and Wi-Fi nodes often occupy the same or overlapping radio frequency bands in the 2.4 GHz unlicensed spectrum. As a result, a 802.15.4 node can detect and synchronize to the periodic beacons broadcasted by Wi-Fi access points (APs). A key advantage of our approach is that, due to the long communication range of Wi-Fi, a large number of 802.15.4 sensors can synchronize clock rates to the same beacons without any message exchange. This paper makes several key contributions. First, we experimentally characterize the spatial and temporal characteristics of Wi-Fi beacons in an enterprise Wi-Fi network consisting of over 50 APs deployed in a 300,000 square foot office building. Motivated by our measurement results, we design a novel synchronization protocol called WizSync. WizSync employs digital signal processing (DSP) techniques to detect periodic Wi-Fi beacons and use them to calibrate the frequency of native clocks. WizSync can intelligently predict the clock skew and adaptively schedules nodes to sleep to conserve energy. We implement WizSync in TinyOS 2.1.1 and conduct extensive evaluation on a testbed consisting of 19 TelosB motes. Our results show that WizSync can achieve an average synchronization error of 0.12 milliseconds over a period of 10 days with radio power consumption of 50.9 microwatts/node.

The barrier coverage problem in emerging mobile sensor networks has been an interesting research issue due to many related real-life applications. Existing solutions are mainly concerned with deciding one-time movement for individual sensors to construct as many barriers as possible, which may not be suitable when there are no sufficient sensors to form a single barrier. In this paper, we aim to achieve barrier coverage in the sensor scarcity scenario by dynamic sensor patrolling. Specifically, we design a periodic monitoring scheduling (PMS) algorithm in which each point along the barrier line is monitored periodically by mobile sensors. Based on the insight from PMS, we then propose a coordinated sensor patrolling (CSP) algorithm to further improve the barrier coverage, where each sensor's current movement strategy is derived from the information of intruder arrivals in the past. By jointly exploiting sensor mobility and intruder arrival information, CSP is able to significantly enhance barrier coverage. We prove that the total distance that sensors move during each time slot in CSP is the minimum. Considering the decentralized nature of mobile sensor networks, we further introduce two distributed versions of CSP: S-DCSP and G-DCSP. We study the scenario where sensors are moving on two barriers and propose two heuristic algorithms to guide the movement of sensors. Finally, we generalize our results to work for different intruder arrival models. Through extensive simulations, we demonstrate that the proposed algorithms have desired barrier coverage performances.

Wireless Sensor Networks (WSN) promise researchers a powerful instrument for observing sizable phenomena with fine granularity over long periods. Since the accuracy of data is important to the whole system's performance, detecting nodes with faulty readings is an essential issue in network management. As a complementary solution to detecting nodes with functional faults, this article, proposes FIND, a novel method to detect nodes with data faults that neither assumes a particular sensing model nor requires costly event injections. After the nodes in a network detect a natural event, FIND ranks the nodes based on their sensing readings as well as their physical distances from the event. FIND works for systems where the measured signal attenuates with distance. A node is considered faulty if there is a significant mismatch between the sensor data rank and the distance rank. Theoretically, we show that average ranking difference is a provable indicator of possible data faults. FIND is extensively evaluated in simulations and two test bed experiments with up to 25 MicaZ nodes. Evaluation shows that FIND has a less than 5% miss detection rate and false alarm rate in most noisy environments.

Time synchronization is a fundamental requirement for the wide spectrum of applications with wireless sensor networks (WSNs). However, most existing time synchronization protocols are likely to deteriorate or even to be destroyed when the WSNs are attacked by malicious intruders. This paper is concerned with secure time synchronization for WSNs under message manipulation attacks. Specifically, the theoretical analysis and simulation results are first provided to demonstrate that the maximum consensus based time synchronization (MTS) protocol would be invalid under message manipulation attacks. Then, a novel secured maximum consensus based time synchronization (SMTS) protocol is proposed to detect and invalidate message manipulation attacks. Furthermore, we prove that SMTS is guaranteed to converge with simultaneous compensation of both clock skew and offset. Extensive numerical results show the effectiveness of our proposed protocol.

We consider the problem of event capture by a rechargeable sensor network. We assume that the events of interest follow a renewal process whose event inter-arrival times are drawn from a general probability distribution, and that a stochastic recharge process is used to provide energy for the sensors' operation. Dynamics of the event and recharge processes make the optimal sensor activation problem highly challenging. In this paper we first consider the single-sensor problem. Using dynamic control theory, we consider a full-information model in which, independent of its activation schedule, the sensor will know whether an event has occurred in the last time slot or not. In this case, a simple and optimal greedy policy for the solution is developed. We then further consider a partial-information model where the sensor knows about the occurrence of an event only when it is active. This problem falls into the class of partially observable Markov decision processes (POMDP). Since the POMDP's optimal policy has exponential computational complexity and is intrinsically hard to solve, we propose an efficient heuristic clustering policy and evaluate its performance. Finally, our solutions are extended to handle a network setting in which multiple sensors collaborate to capture the events. We also provide extensive simulation results to evaluate the performance of our solutions.

Access card authentication is critical and essential for many modern access control systems, which have been widely deployed in various government, commercial, and residential environments. However, due to the static identification information exchange among the access cards and access control clients, it is very challenging to fight against access control system breaches due to reasons such as loss, stolen or unauthorized duplications of the access cards. Although advanced biometric authentication methods such as fingerprint and iris identification can further identify the user who is requesting authorization, they incur high system costs and access privileges cannot be transferred among trusted users. In this work, we introduce a dynamic authentication with sensory information for the access control systems. By combining sensory information obtained from onboard sensors on the access cards as well as the original encoded identification information, we are able to effectively tackle the problems such as access card loss, stolen, and duplication. Our solution is backward-compatible with existing access control systems and significantly increases the key spaces for authentication. We theoretically demonstrate the potential key space increases with sensory information of different sensors and empirically demonstrate simple rotations can increase key space by more than 1,000,000 times with an authentication accuracy of 90 percent. We performed extensive simulations under various environment settings and implemented our design on WISP to experimentally verify the system performance.

Due to the limited power constraint in sensors, dynamic scheduling with data quality management is strongly preferred in the practical deployment of long-term wireless sensor network applications. We could reduce energy consumption by turning off (i.e., duty cycling) sensor, however, at the cost of low-sensing fidelity due to sensing gaps introduced. Typical techniques treat data quality management as an isolated process for individual nodes. And existing techniques have investigated how to collaboratively reduce the sensing gap in space and time domain; however, none of them provides a rigorous approach to confine sensing error is within desirable bound when seeking to optimize the tradeoff between energy consumption and accuracy of predictions. In this paper, we propose and evaluate a scheduling algorithm based on error inference between collaborative sensor pairs, called CIES. Within a node, we use a sensing probability bound to control tolerable sensing error. Within a neighborhood, nodes can trigger additional sensing activities of other nodes when inferred sensing error has aggregately exceeded the tolerance. The main objective of this work is to develop a generic scheduling mechanism for collaborative sensors to achieve the error-bounded scheduling control in monitoring applications. We conducted simulations to investigate system performance using historical soil temperature data in Wisconsin-Minnesota area. The simulation results demonstrate that the system error is confined within the specified error tolerance bounds and that a maximum of 60 percent of the energy savings can be achieved, when the CIES is compared to several fixed probability sensing schemes such as eSense. And further simulation results show the CIES scheme can achieve an improved performance when comparing the metric of a prediction error with baseline schemes. We further validated the simulation and algorithms by constructing a lab test bench to emulate actual environment monitoring applications. The results show that our approach is effective and efficient in tracking the dramatic temperature shift in dynamic environments.

This paper studies deterministic sensor deployment for barrier coverage in wireless sensor networks. Most of existing works focused on line-based deployment, ignoring a wide spectrum of potential curve-based solutions. We, for the first time, extensively study the sensor deployment under a general setting. We first present a condition under which the line-based deployment is suboptimal, revealing the advantage of curve-based deployment. By constructing a contracting mapping, we identify the characteristics for a deployment curve to be optimal. Based on the optimal deployment curve, we design sensor deployment algorithms by introducing a new notion of distance-continuous. Our findings show that i) when the deployment curve is distance-continuous, the proposed algorithm is optimal in terms of the vulnerability corresponding to the deployment, and ii) when the deployment curve is not distance-continuous, the approximation ratio of the vulnerability corresponding to the deployment by the proposed algorithm to the optimal one is upper bounded by min (π, ||ÃB̃||/||ÃG̃B̃|| 2n+√2-1/2n ), where ||ÃB̃|| and ||ÃG̃B̃|| are some constants, and n is the number of sensors. We generalize the study to the heterogeneous sensing model, and show that the proposed algorithm can provide close-to-optimal performance. Extensive numerical results corroborate our analysis.

Recently, various consensus-based protocols have been developed for time synchronization in wireless sensor networks. However, due to the uncertainties lying in both the hardware fabrication and network communication process, it is not clear how most of the protocols will perform in real implementations. In order to reduce such gap, this paper investigates whether and how the typical consensus-based time synchronization protocols can tolerate the uncertainties in practical sensor networks through extensive testbed experiments. For two typical protocols, i.e., Average Time Synchronization (ATS) and Maximum Time Synchronization (MTS), we first analyze how the time synchronization accuracy will be affected by various uncertainties in the system. Then, we implement both protocols on our sensor network testbed consisted of Micaz nodes. We further investigate the time synchronization performance and robustness under various settings. The extensive experimental results demonstrate the advantages of MTS over ATS.

In this paper, the localization accuracy of range-only sensors is investigated. Being different from most previous studies, we consider a more general measurement model with both additive and multiplicative noise other than with only additive noise. The main contributions lie in twofold. First, a CRLB(Cramer-Rao Lower Bound)-based metric is proposed to evaluate the localization accuracy. In addition, the analytical relationships between target-sensor distance and localization accuracy, and between noise and localization accuracy are derived. Second, numerical analysis is executed to evaluate the localization accuracy for three important regular patterns of sensor deployment, i.e., triangle, square and hexagon. Two aspects have been examined and discussed, including (a) the geometric distribution of localization accuracy, (b) the average localization accuracy. Both theoretical and numerical results show that the multiplicative noise will influence significantly the localization accuracy. This study also provides important guidelines for optimal sensor deployment.

In this paper we study area coverage in bistatic radar sensor networks (BRSN), which is composed of a collection of transmitters and receivers. Coverage in BRSN is much more difficult than that in traditional sensor networks as the sensing area of a bistatic radar depends on the positions of its component transmitter and receiver, and is in general of an elliptical shape. We first investigate the geometrical relationship between the c-coverage area of a bistatic radar and the distance between its component transmitter and receiver, based on which we reduce the number of candidate bistatic radars from all transmitter-receiver pairs. Then we reduce the problem dimension by transforming the area coverage problem to point coverage problem by employing intersection point concept. Finally we propose an efficient algorithm to solve the Point Coverage Problem, which thus solves the area coverage problem. We perform extensive simulations to validate our analysis and the performance of the proposed algorithm.

In this paper, we develop a smart phone-based driving behavior evaluation system, named Join Driving, which helps drivers notice how aggressive their driving behaviors are and be aware of the riding comfort level of passengers. The proposed evaluation system is made of two parts: driving events detection and evaluation part and riding comfort level evaluation part. In driving events detection and evaluation part, the proposed system, Join Driving, first presents a model to detect drivers' driving events, based on the data collected from the acceleration, orientation and GPS sensors in smart phones. Then, based on the detected drivers' driving events, Join Driving implements a novel scoring mechanism to quantitatively evaluate how aggressive these driving events are. In riding comfort level evaluation part, the proposed system gives the specific scores to rate passengers' riding comfort level based on ISO 2631. Finally, several practical experiments are conducted to evaluate the effectiveness of the proposed scoring system.

The identification of vessel collision risk for a Maritime Intelligent Transport System (MITS) is crucial for maritime safety and management. This paper considers the identification of the Systematic Collision Risk (SCR) for an MITS based on AIS data, which is obtained by wireless communication among vessels and between vessels and shore-based stations. SCR is modeled as a function of the collision risk of each vessel. A computing method for the SCR of a two-vessel case is proposed. Meanwhile, a hierarchical clustering based simplification algorithm is provided and applied to transform the topology of an MITS, thus simplifying the computing of the SCR. Based on the two-vessel case and transformation, a bottom-to-top weighted fusion method is employed to calculate the SCR for an MITS. Extensive numerical examples of simulative and real AIS data verify the effectiveness of our modeling and computing.

In Opportunistic Mobile Networks (OppNets), a large amount of energy is consumed by idle listening, instead of infrequent data exchange. This makes energy saving a challenging and fundamental problem in OppNets, since nodes are typically battery-powered. Asynchronous duty-cycle operation is a promising approach for energy saving in OppNets, however, if its working schedule is not effectively designed, it may also cause significant network performance degradation. Therefore, it is pressing to design an energy-efficient working schedule for duty-cycle OppNets. In this paper, we first analyze the contact process in duty-cycle OppNets, then propose an adaptive working schedule for duty-cycle OppNets. The proposed schedule uses the past recorded contact histories to predict the future contact information, so as to adaptively configure the working schedule of each node in the network. Finally, extensive real trace-driven simulations are conducted to evaluate the performance of our proposed schedule. The results show that our proposed adaptive working schedule is superior to the random working schedule in terms of delivery ratio and delivery delay.

In Opportunistic Mobile Networks (OppNets), data is opportunistically exchanged between nodes who encounter each other. In order to enable such data exchanges, nodes in the network have to probe their environment continually, so as to discover neighbor nodes. This can be an extremely energy-consuming process. If nodes probe very frequently, they will consume a lot of energy, and might be energy inefficient. On the other hand, infrequent contact probing might cause nodes to miss many of their contacts, and thus opportunities to exchange data are lost. Therefore, there exists a trade-off between energy efficiency and the contact opportunities in OppNets. In this paper, in order to investigate this trade-off, we first propose a model to quantify the detecting probability in OppNets, using the Random WayPoint (RWP) model. Then, extensive simulations are conducted to validate the correctness of our proposed model. Finally, based on the proposed model, we analyze the trade-off between energy efficiency and the total number of effective contacts under different situations. Our results show that the good trade-off points are obviously different when the speed of nodes is different. Moreover, the detecting probability increases as the speed of nodes decreases, while the total number of effective contacts increases as the speed of nodes increases.

Efficient demand management policies at the grid side are required for large scale charging of Plug-in Hybrid Electric Vehicles and Plug-in Electric vehicles (PHEVs/PEVs). The SoC level and Charging Cost should be optimized while the aggregate load is kept under a safety limit to avoid overloads. Conventionally, optimal managing of the charging rates requires gathering and processing data in a center. However, as the scale of the problem increases to consider thousands of charging stations distributed over a vast geographical area, the central approach suffers from vulnerability to single node/link failures as well as scalability. This paper introduces a novel decentralized network cooperative approach for controlling the PHEV/PEV charging rates. In this approach, each charging station acts as a local retailer of energy, selling the power to the plugged in vehicle while coordinating the price with its neighbors. In response to the offered price, the Smart-Charger of the vehicle adjusts the charging current to maximize the utility of the PHEV/PEV user. By iteratively repeating this process, the convergence to the global optimum is attained without the requirement for any central unit. Robustness to single link/node failures is another advantage of our method.

As a pioneering experimental platform of wireless rechargeable sensor networks, the Wireless Identification and Sensing Platform (WISP) is an open-source platform that integrates sensing and computation capabilities to the traditional RFID tags. Different from traditional tags, a RFID-based wireless rechargeable sensor node needs to charge its onboard energy storage above a threshold in order to power its sensing, computation and communication components. Consequently, such charging delay imposes a unique design challenge for deploying wireless rechargeable sensor networks. In this paper, we tackle this problem by planning the optimal movement strategy of the RFID reader, such that the time to charge all nodes in the network above their energy threshold is minimized. We first propose an optimal solution using the linear programming method. To further reduce the computational complexity, we then introduce a heuristic solution with a provable approximation ratio of (1 + θ)/(1 - ε) by discretizing the charging power on a two-dimensional space. Through extensive evaluations, we demonstrate that our design outperforms the set-cover-based design by an average of 24.7% while the computational complexity is O((N/ε)2).

This paper studies deterministic sensor deployment to ensure barrier coverage in wireless sensor networks. Most of existing work focused on line-based deployment, ignoring a wide spectrum of potential curve-based solutions. We, for the first time, extensively study the sensor deployment under general settings. We first present a condition under which line-based deployment is suboptimal, pointing to the advantage of curve-based deployment. By constructing a contracting mapping, we identify the characteristics for a deployment curve to be optimal. We then design sensor deployment algorithms for the optimal deployment curve by introducing a new notion of distance-continuous. Our findings show that i) when the deployment curve is distance-continuous, the proposed algorithm is optimal in terms of the vulnerability corresponding to the deployment, and ii) when the deployment curve is not distance-continuous, the approximation ratio of the vulnerability corresponding to the deployment by the proposed algorithm to the optimal one is upper bounded by min (π, ||AB||/||AGB|| 2n+√(2-1)/2n), where ||AB||, ||AGB|| and n are constants. Extensive numerical results corroborate our analysis.

Energy constraint is a critical hurdle hindering the practical deployment of long-term wireless sensor network applications. Turning off (that is, duty cycling) sensors could reduce energy consumption, however, this would occur at the cost of low sensing fidelity due to sensing gaps introduced. Existing techniques focus mainly on scheduling a network with static anchors. Few methods provides a rigorous approach to confining sensing errors within desirable bounds while seeking to optimize the tradeoff between energy consumption and accuracy of predictions. In this work, we propose a sensing scheduling scheme, called MAS, to support mobile anchors in sensor networks. Within a node, we use a sensing probability bound to control tolerable sensing errors. While communicating with the mobile anchor, nodes trigger additional sensing activities to accommodate the QoS requirement in mobile communication. We validated the concept by constructing a lab-grade mobile anchor that fully supports 4G-LTE communications for monitoring applications. We further conducted simulations to investigate system performance. The simulation results demonstrated that the MAS achieved enhancement performance compared to several other sensing schemes.

Recently, the content-based publish/subscribe (pub/sub) paradigm is gaining popularity in opportunistic mobile networks (OppNets) for its flexibility and adaptability. Since nodes in OppNets is controlled by humans, they often behave selfishly with an aim to maximize their own revenues without considering the performance of others. Therefore, stimulating nodes in OppNets to collect, store, and share content efficiently is one of the key challenges under this scheme. Meanwhile, guaranteeing the freshness of content is also a big problem for content dissemination in OppNets. In this paper, in order to solve these problems, we propose an incentive-driven and freshness-aware pub/sub content dissemination scheme, called ConDis (Content Dissemination), for selfish OppNets. In ConDis, the Tit-For-Tat (TFT) scheme is employed to deal with selfish behaviors of nodes in OppNets. ConDis also implements a novel content exchange protocol when nodes are in contact. Specifically, during each contact, the exchange order is determined by the content utility, which is calculated by the direct subscribed value and the indirect subscribed value, and the objective of nodes is to maximize the utility of the content inventory stored in their buffer. Extensive realistic trace-driven simulation results show that ConDis is superior to other existing schemes in terms of total freshness value, total delivered contents, and total transmission cost.

Data gathering in wireless sensor networks typically involves two steps: data sensing and data transmission, which dominate the energy consumption of each sensor. In Rechargeable Sensor Networks (RSNs), in order to optimize data gathering, energy should be carefully allocated to data sensing and data transmission due to time-varying renewable energy arrival and limited battery capacity. Moreover, the dynamic feature of network topology should be taken into account, since it can affect the optimal data transmission. In this paper, we strive to optimize data gathering by jointly considering data sensing and transmission. To this end, we first design a Balanced Energy Allocation Scheme (BEAS) for each sensor to manage its energy use, which is proven to meet four requirements raised by practical scenarios. Then we propose a Distributed Sensing Rate and Routing Control (DS2RC) algorithm to jointly optimize data sensing and transmission, while guaranteeing network fairness. In DS2RC, each sensor can adaptively adjust its transmit energy consumption during network operation according to the amount of available energy, and select the optimal sensing rate and routing, which can efficiently improve data gathering. We theoretically prove the optimality and the convergence of the proposed algorithms. Extensive simulations are performed to demonstrate the efficiency of BEAS and DS2RC by comparing with existing algorithms.

In Machine to Machine (M2M) networks, a robust Medium Access Control (MAC) protocol is crucial to enable numerous machine-type devices to concurrently access the channel. Most literatures focus on developing simplex (reservation or contention based) MAC protocols which cannot provide a scalable solution for M2M networks with large number of devices. In this paper, a frame-based Hybrid MAC scheme, which consists of a contention period and a transmission period, is proposed for M2M networks. In the proposed scheme, the devices firstly contend the transmission opportunities during the contention period, only the successful devices will be assigned a time slot for transmission during the transmission period. To balance the tradeoff between the contention and transmission period in each frame, an optimization problem is formulated to maximize the system throughput by finding the optimal contending probability during contention period and optimal number of devices that can transmit during transmission period. A practical hybrid MAC protocol is designed to implement the proposed scheme. The analytical and simulation results demonstrate the effectiveness of the proposed Hybrid MAC protocol.

In this paper, we address the problem of peer-to-peer networking for data dissemination among actors in wireless sensor and actor networks (WSANs), which consist of static sensors, responsible for environment monitoring, and mobile actors, in charge of data collection and task performing. This problem has not been received much attention although peer-to-peer networking has achieved great successes in other networks such as the Internet and mobile ad hoc networks (MANETs). Unlike the Internet and MANETs, WSANs contain static sensors that are energy-constrained and actors that cannot communicate with each other directly. These unique characteristics make the data dissemination problem in WSANs extremely challenging. We present an Energy-Efficient Message Dissemination protocol (EMD) to solve this problem in delay-tolerant WSANs. EMD is grounded on a novel principle of "Carry-Disseminate-Store-and-Forward" proposed for the first time here. While traveling, a source actor disseminates messages (data) to sensors upon contact, which will store the messages and forward them to other actors when they come into communication range. The actors receiving the messages from sensors work as source actors and help to distribute the messages. We theoretically analyze the data dissemination strategy under which the original source actor can distribute its messages to all other actors at minimum communication cost within a given delay bound. Through extensive simulations we demonstrate the performance of EMD.

Recently, content-based publish/subscribe (pub/sub) services have become a significant research field in opportunistic mobile networks (OppNets). Pub/sub is an asynchronous messaging paradigm, in which content transmissions are guided by the interest. Since selfish behavior is common in reality, nodes often behave selfishly with an aim to maximize their own utilities without considering performance of other nodes. Therefore, how to encourage nodes to collect, store and share network content efficiently is one of the key challenges under this paradigm. In this paper, we propose an incentive-based pub/sub scheme, called ConSub, for OppNets. In ConSub, Tit-For-Tat (TFT) mechanism is employed to deal with selfish behavior. ConSub also implements a content exchange protocol between two interacting node, thus encouraging them to play as businessmen and carry contents to satisfy each other's interest. Specifically, the exchange order is determined by the content utility, which is calculated by contact probability and cooperation level between the current node and its neighbors subscribing to the interest. Extensive realistic trace-driven simulation results show that ConSub is superior to existing schemes in terms of delivered packets and transmission hops with reasonable transmission cost.

Networked cyber-physical systems (NCPS), where control and communication are closely integrated, have been envisioned to have a large number of high-impact applications. In this paper, a joint optimization framework is presented, which combines the objective of control as well as other relevant system objectives and constraints such as communication errors, delays and the limited capabilities (e.g., energy capacities) of devices. The problem is solved by an online optimization approach, which consists of a communication protocol and a simulated annealing based control algorithm. Meanwhile, by taking into account the communication cost, we optimize the control intervals by integrating two kinds of acceptances, i.e., cyber and physical acceptances, into the control algorithm. Numerical results show the effectiveness of the proposed approach.

Wireless rechargeable sensor networks (WRSNs) have emerged as an alternative to solving the challenges of size and operation time posed by traditional battery-powered systems. In this paper, we study a WRSN built from the industrial wireless identification and sensing platform (WISP) and commercial off-the-shelf RFID readers. The paper-thin WISP tags serve as sensors and can harvest energy from RF signals transmitted by the readers. This kind of WRSNs is highly desirable for indoor sensing and activity recognition and is gaining attention in the research community. One fundamental question in WRSN design is how to deploy readers in a network to ensure that the WISP tags can harvest sufficient energy for continuous operation. We refer to this issue as the energy provisioning problem. Based on a practical wireless recharge model supported by experimental data, we investigate two forms of the problem: point provisioning and path provisioning. Point provisioning uses the least number of readers to ensure that a static tag placed in any position of the network will receive a sufficient recharge rate for sustained operation. Path provisioning exploits the potential mobility of tags (e.g., those carried by human users) to further reduce the number of readers necessary: mobile tags can harvest excess energy in power-rich regions and store it for later use in power-deficient regions. Our analysis shows that our deployment methods, by exploiting the physical characteristics of wireless recharging, can greatly reduce the number of readers compared with those assuming traditional coverage models.

In wireless sensor networks (WSNs), trap coverage has recently been proposed to trade off between the availability of sensor nodes and sensing performance. It offers an efficient framework to tackle the challenge of limited resources in large-scale sensor networks. Currently, existing works only studied the theoretical foundation of how to decide the deployment density of sensors to ensure the desired degree of trap coverage. However, practical issues, such as how to efficiently schedule sensor node to guarantee trap coverage under an arbitrary deployment, are still left untouched. In this article, we formally formulate the Minimum Weight Trap Cover Problem and prove it is an NP-hard problem. To solve the problem, we introduce a bounded approximation algorithm, called Trap Cover Optimization (TCO) to schedule the activation of sensors while satisfying specified trap coverage requirement. We design Localized Trap Coverage Protocol as the localized implementation of TCO. The performance of Minimum Weight Trap Coverage we find is proved to be at most O (ρ) times of the optimal solution, where ρ is the density of sensor nodes in the region. To evaluate our design, we perform extensive simulations to demonstrate the effectiveness of our proposed algorithm and show that our algorithm achieves at least 14% better energy efficiency than the state-of-the-art solution.

In this paper, we consider the issue of data broadcasting in mobile social networks (MSNets). The objective is to broadcast data from a superuser to other users in the network. There are two main challenges under this paradigm, namely 1) how to represent and characterize user mobility in realistic MSNets; 2) given the knowledge of regular users' movements, how to design an efficient superuser route to broadcast data actively. We first explore several realistic data sets to reveal both geographic and social regularities of human mobility, and further propose the concepts of geocommunity and geocentrality into MSNet analysis. Then, we employ a semi-Markov process to model user mobility based on the geocommunity structure of the network. Correspondingly, the geocentrality indicating the “dynamic user density” of each geocommunity can be derived from the semi-Markov model. Finally, considering the geocentrality information, we provide different route algorithms to cater to the superuser that wants to either minimize total duration or maximize dissemination ratio. To the best of our knowledge, this work is the first to study data broadcasting in a realistic MSNet setting. Extensive trace-driven simulations show that our approach consistently outperforms other existing superuser route design algorithms in terms of dissemination ratio and energy efficiency.

Intrusion detection is a significant application in wireless sensor networks (WSNs). S. Kumar et al have introduced the concept of barrier coverage, which deploys sensors in a narrow belt region to guarantee that any intrusion across the region is to be detected. However, the practical issues have not been investigated such as scheduling sensors energy-efficiently while guaranteeing the detection probability of any intrusion across the region based on probabilistic sensing model. Besides, the intruders may be humans, animals, fighter planes or other things, which obviously have diverse moving speeds. In this paper, we analyze the detection probability of arbitrary path across the barrier of sensors theoretically and take the maximum speed of possible intruders into consideration since the sensor networks are designed for different intruders in different scenarios. Based on the theoretical analysis of detection probability, we formulate Minimum Weight ε-Barrier Problem about how to schedule sensors energy-efficiently and prove it is NP-hard. We propose both global and local solutions to the problem. The global solution called Minimum Weight Barrier Algorithm is a bounded approximation algorithm, based on which a localized protocol for energy-efficient scheduling is designed. To evaluate our design, we analyze the performance of our approaches theoretically and also perform extensive simulations to demonstrate the effectiveness of our proposed algorithm.

Wireless Rechargeable Sensor Network (WRSN) is an emerging technology to address the energy constraint in sensor networks. The protocol design in WRSN is extremely challenging due to the complicated interactions between rechargeable sensor nodes and readers, capable of mobility and functioning as energy distributors and data collectors. In this paper, we for the first time investigate the optimal scheduling problem in WRSN for stochastic event capture, i.e., how to jointly mobilize the readers for energy distribution and schedule sensor nodes for optimal quality of monitoring (QoM). We analyze the QoM for three application scenarios: i) the reader travels at a fixed speed to recharge sensor nodes and sensor nodes consume the collected energy in an aggressive way, ii) the reader stops to recharge sensor nodes for a predefined time during its periodic traveling and sensor nodes deplete energy aggressively, iii) the reader stops to recharge sensor nodes but sensor nodes can adopt optimal duty cycle scheduling for maximal QoM. We provide analytical results for achieving the optimal QoM under arbitrary parameter settings. Extensive simulation results are offered to demonstrate the correctness and effectiveness of our results.

Energy harvesting is a promising technology for extending the lifetime of battery-powered sensor networks. Due to time variations of harvested energy, one of the main challenging issues is to maximize the uninterrupted sampling rates of all sensor nodes, which represents the network performance. Most of existing works do not consider the limited capacity of rechargeable battery. In this paper, we are concerned with how to adaptively decide the sampling rate for each rechargeable sensor node with a limited battery capacity to maximize the overall network performance. To solve this problem, we firstly propose an adaptive Energy Allocation sCHeme (EACH) for each sensor node to manage its energy use in an efficient way. Then we develop a Distributed Sampling Rate Control (DSRC) algorithm to obtain the optimal sampling rate. Furthermore, an Improved adaptive Energy Allocation sCHeme (IEACH) is proposed to reduce the impact due to imprecise estimation of harvested energy. Extensive simulations using real experimental data obtained from Baseline Measurement System (BMS) of Solar Radiation Research Laboratory are conducted to demonstrate the efficiency of the proposed algorithms.

As technology advancements in robotics and wireless communication, tracking mobile targets using mobile sensors has aroused widespread concern in recent years. In this paper, we propose a novel coordinative moving strategy for autonomous mobile sensor networks to guarantee the target can be detected in each observed step while minimizing the amount of moving sensors. The proposed scheme consists of obtaining the current position of the target, which is then used to predict the next time-step location of the target. Once the uncertainty region of the target's position is defined, the proposed method allows the mobile sensors to cover it in an optimal way. Therefore, we can assign each mobile sensor to an optimal location to cover the uncertainty region while minimizing the total traveled distance of sensors. Extensive simulations are given to evaluated performance and demonstrate the efficiency of the proposed strategy.

In this paper, we study power allocation in cognitive sensor networks where cognitive users (cognitive enabled sensor nodes) opportunistically share a common spectrum with primary users (licensed devices). We define an energy efficiency-oriented utility function as a new metric to evaluate power allocation. Consider that sensor nodes are self-interested to maximize their own utility, we formulate the energy efficient power allocation problem as a non-cooperative game. We firstly prove that there exist Nash equilibriums in the proposed game. Secondly, we prove that the power allocation game is a supermodular game with some conditions. Finally, we use best response algorithm to identify the Nash equilibrium. Simulations are conducted to demonstrate that the proposed power allocation strategy can achieve satisfactory performance in terms of energy efficiency, convergence speed and fairness in cognitive sensor networks.

It is a common class of applications with wireless sensor network to provide full coverage to the region of interest (ROI), such as environment monitoring, military detection and agricultural observation. Existing literatures on full coverage are mostly based on the binary sensing model to simplify the problem. However, the results are far from the reality since binary sensing model as a coarse approximation is too conservative. The probabilistic sensing model has been proposed as a more realistic model to characterize the sensing region. In this paper, we introduce the concept of ε-full coverage based on probabilistic model, i.e., every point in ROI has at least a probability ε of being covered by sensors. We explore the mathematic relationship between the probabilities of two adjacent points being covered and transform ε-full coverage problem into point coverage problem. Then, we design ε-full coverage optimization (FCO) to select a subset of sensors to provide ε-full coverage dynamically so that the lifetime of network is prolonged. This algorithm outperforms the state-of-the-art solution significantly, which we have validated by simulations.

In Opportunistic Mobile Networks (OppNets), a large amount of energy is consumed by idle listening, instead of infrequent data exchanging. This makes energy saving become a serious problem in OppNets, as nodes are typically with limited energy supplies. Duty-cycle operation is a promising approach for energy saving in OppNets, however, it may cause the degradation of network connectivity. Therefore, there exists a tradeoff relationship between energy saving and network connectivity in duty-cycle OppNets. In this paper, we propose a model to quantify the contact probability (a metric of connectivity) in duty-cycle OppNets based on the realistic mobility trace. Then the tradeoff between energy saving and the contact probability under different situations is derived and analyzed. Our results show that the duty-cycled nodes can guarantee an energy saving of around 50% without impacting the contact probability if the period meets a certain condition, and higher energy saving can be achieved at the cost of reducing the contact probability. Finally, realistic trace-driven simulations are performed to validate the correctness of our results.

In wireless sensor and actuator networks (WSAN), both sensory measurements and control signals transmitted through the wireless media are prone to packet losses. Moreover, the distance between the sender and receiver is a critical factor for the loss rate. Therefore, where to place the controller to ensure optimal control performance is an interesting problem. In this paper, we focus on WSAN with one sensor and one actuator residing at different geographic locations. If the controller is constrained at either the sensor side or actuator side, we derive the necessary and sufficient conditions under which the optimal controller location can be directly determined. For the more general case when the controller can be placed anywhere, with mild assumptions on the packet drop model, we also provide the conditions under which the optimal controller location is unique and can be determined. Numerical simulations based on a practical packet loss model verify our results.

We consider the problem of event capture by a rechargeable sensor network. We assume that the events of interest follow a renewal process whose event inter-arrival times are drawn from a general probability distribution, and that a stochastic recharge process is used to provide energy for the sensors' operation. Dynamics of the event and recharge processes make the optimal sensor activation problem highly challenging. In this paper we first consider the single-sensor problem. Using dynamic control theory, we consider a full-information model in which, independent of its activation schedule, the sensor will know whether an event has occurred in the last time slot or not. In this case, the problem is framed as a Markov decision process (MDP), and we develop a simple and optimal policy for the solution. We then further consider a partial-information model where the sensor knows about the occurrence of an event only when it is active. This problem falls into the class of partially observable Markov decision processes (POMDP). Since the POMDP's optimal policy has exponential computational complexity and is intrinsically hard to solve, we propose an efficient heuristic clustering policy and evaluate its performance. Finally, our solutions are extended to handle a network setting in which multiple sensors collaborate to capture the events. We provide extensive simulation results to evaluate the performance of our solutions.

Barrier coverage problem in emerging mobile sensor networks has been an interesting research issue. Existing solutions to this problem aim to decide one-time movement for individual sensors to construct as many barriers as possible, which may not work well when there are no sufficient sensors to form a single barrier. In this paper, we try to achieve barrier coverage in sensor scarcity case by dynamic sensor patrolling. In specific, we design a periodic monitoring scheduling (PMS) algorithm in which each point along the barrier line is monitored periodically by mobile sensors. Based on the insight from PMS, we then propose a coordinated sensor patrolling (CSP) algorithm to further improve the barrier coverage, where each sensor's current movement strategy is decided based on the past intruder arrival information. By jointly exploiting sensor mobility and intruder arrival information, CSP is able to significantly enhance barrier coverage. We prove that the total distance that the sensors move during each time slot in CSP is the minimum. Considering the decentralized nature of mobile sensor networks, we further introduce two distributed versions of CSP: S-DCSP and G-DCSP. Through extensive simulations, we demonstrate that CSP has a desired barrier coverage performance and S-DCSP and G-DCSP have similar performance as that of CSP.

Intrusion detection is a significant application in wireless sensor networks (WSNs). S. Kumar et al have introduced the concept of barrier coverage, which deploys sensors in a narrow belt region to guarantee that any intrusion across the region is to be detected. However, the practical issues have not been investigated such as scheduling sensors energy-efficiently while guaranteeing the detection probability of any intrusion across the region based on probabilistic sensing model, which is a more realistic sensing model. Besides, the intruders may be humans, animals, fighter planes or other things, which obviously have diverse moving speeds. In this paper, we analyze the detection probability of arbitrary path across the barrier of sensors theoretically and take the maximum speed of possible intruders into consideration since the sensor networks are designed for different intruders in different scenarios. Based on the theoretical analysis of detection probability, we formulate a Minimum Weight ∈-Barrier Problem about how to schedule sensors energy-efficiently. We show the problem NP-hard and propose a bounded approximation algorithm, called Minimum Weight Barrier Algorithm (MWBA) to schedule the activation of sensors. To evaluate our design, we analyze the performance of MWBA theoretically and also perform extensive simulations to demonstrate the effectiveness of our proposed algorithm.

Access card authentication is critical and essential for many modern access control systems, which have been widely deployed in various government, commercial and residential environments. However, due to the static identification information exchange among the access cards and access control clients, it is very challenging to fight against access control system breaches due to reasons such as loss, stolen or unauthorized duplications of the access cards. Although advanced biometric authentication methods such as fingerprint and iris identification can further identify the user who is requesting authorization, they incur high system costs and access privileges can not be transferred among trusted users. In this work, we introduce a sensory-data-enhanced authentication for access control systems. By combining sensory-data obtained from onboard sensors on the access cards as well as the original encoded identification information, we are able to effectively tackle the problems such as access card loss and stolen. Our solution is backward-compatible with existing access control systems and significantly increases the key spaces for authentication. We theoretically demonstrate the potential key space increases with simple sensor data and empirically demonstrate simple rotations can increase key space by more than 30, 000 times with an authentication accuracy of 95%. We performed extensive simulations under various environment settings and implemented our design on WISP to experimentally verify the system performance.

Energy harvesting is a promising technology for extending the lifetime of sensor networks with the restrictions of limited battery energy. One of the main challenging issues is to maximize the sampling rates of all sensor nodes. In this paper, we are concerned with how to adaptively decide the sampling rate for each rechargeable sensor node with a limited battery capacity to maximize overall network utility. To solve the problem, we firstly propose an adaptive energy allocation scheme for each node to manage its energy use in an efficient way. Then we develop a distributed sampling rate control (DSRC) algorithm to obtain the optimal sampling rate. Extensive simulations using real experimental data obtained from Baseline Measurement System (BMS) of Solar Radiation Research Laboratory are performed to demonstrate the efficiency of our algorithm.

Wireless Sensor Networks have been used in many mobile applications such as wildlife tracking and participatory urban sensing. Because of the combination of high mobility and low-duty-cycle operations, it is a challenging issue to reduce discovery delay among mobile nodes, so that mobile nodes can establish connection quickly once they are within each other's vicinity. Existing discovery designs are essentially pair-wise based, in which discovery is passively achieved when two nodes are pre-scheduled to wake-up at the same time. In contrast, for the first time, this work reduces discovery delay significantly by proactively referring wake-up schedules among a group of nodes. Because proactive references incur additional overhead, we introduce a novel selective reference mechanism based on spatiotemporal properties of neighborhood and the mobility of the nodes. Our quantitative analysis indicates that the discovery delay of our group-based mechanism is significantly smaller than that of the pair-wise one. Our testbed experiments using 40 sensor nodes confirm our theoretical analysis, showing one order of magnitude reduction in discovery delay compared with traditional pair-wise methods with only 0.5%~8.8% increase in energy consumption.

Nowadays, with the dramatically increased penetration of wireless access, the conflict between spectrum scarcity and under-utilisation is becoming more and more aggravating. A promising technology to tackle such challenge is cognitive radio, of which spectrum sensing is one of the most important functionalities. In this study, the authors consider an essential problem of energy-efficient spectrum sensing in cognitive radio networks. Although most existing works of spectrum sensing mainly focus on determining an optimal sensing time to maximise the detection probability and/or to minimise the false alarm probability, our problem of how to schedule the power-constrained sensor is much more challenging, because of the trade-off among interests of the primary user, secondary user and sensor. The authors formulate it as a non-linear optimisation problem to maximise the sensor lifetime, with necessary constraints of quality and delay of spectrum sensing, and throughput for performance guarantee of primary and secondary users. Moreover, the authors incorporate the distribution information of channel occupancy/vacancy durations into the problem to yield a desirable solution. They propose a novel framework to obtain the optimal energy-efficient periodic scheduling by adopting both non-linear programming and linear programming. Extensive simulation results are provided to validate our theoretical analysis.

We consider the problem of gathering correlated sensor data by a single sink node in a wireless sensor network. We assume that the sensor nodes are energy constrained and design efficient distributed protocols to maximize the network lifetime. Many existing approaches focus on optimizing the routing layer only, but in fact the routing strategy is often coupled with power control in the physical layer and link access in the MAC layer. This paper represents a first effort on network lifetime maximization that jointly considers the three layers. We first assume that link access probabilities are known and consider the joint optimal design of power control and routing. We show that the formulated optimization problem is convex and propose a distributed algorithm, JRPA, for the solution. We also discuss the convergence of JRPA. When the optimal link access probabilities are unknown, as in many practical networks, we generalize the problem formulation to encompass all the three layers of routing, power control, and link-layer random access. In this case, the problem cannot be converted into a convex optimization problem, but there exists a duality gap when the Lagrangian dual method is employed. We propose an efficient heuristic algorithm, JRPRA, to solve the general problem, and show through numerical experiments that it can significantly narrow the gap between the computed and optimal solutions. Moreover, even without a priori knowledge of the best link access probabilities predetermined for JRPA, JRPRA achieves extremely competitive performance with JRPA. Beyond the metric of network lifetime, we also discuss how to solve the problem of correlated data gathering under general utility functions. Numerical results are provided to show the convergence of the algorithms and their advantages over existing solutions.

In this paper, we consider using actors to maintain the quality of sensing in the wireless sensor networks. Due to factors such as battery drainage or physical malfunctions, the number of available sensors normally decreases over time after initial deployment, resulting in performance degradation. To maintain the quality of sensing in the network, actors can be used to allocate spare sensors to sensor-deficient regions (sensor allocation) or to relocate sensors from sensor-abundant regions to sensor-deficient regions (sensor relocation). We first focus on the sensor allocation problem. We introduce a baseline centralized greedy algorithm (GA) for sensor allocation, where global sensor information is communicated to obtain the optimal solution. As GA is only efficient for small networks, we proceed to design a distributed patrolling algorithm for achieving global optimization (DPAG) by using only local information. We then extend our work to the application scenario of sensor relocation by proposing a modified GA and DPAG (M-GA and M-DPAG), respectively. Extensive simulation results are provided to demonstrate the performance of the proposed algorithms.

As sensors are energy constrained devices, one challenge in wireless sensor networks (WSNs) is to guarantee coverage and meanwhile maximize network lifetime. In this paper, we leverage prediction to solve this challenging problem, by exploiting temporal-spatial correlations among sensory data. The basic idea lies in that a sensor node can be turned off safely when its sensory information can be inferred through some prediction methods, like Bayesian inference. We adopt the concept of entropy in information theory to evaluate the information uncertainty about the region of interest (RoI). We formulate the problem as a minimum weight submodular set cover problem, which is known to be NP hard. To address this problem, an efficient centralized truncated greedy algorithm (TGA) is proposed. We prove the performance guarantee of TGA in terms of the ratio of aggregate weight obtained by TGA to that by the optimal algorithm. Considering the decentralization nature of WSNs, we further present a distributed version of TGA, denoted as DTGA, which can obtain the same solution as TGA. The implementation issues such as network connectivity and communication cost are extensively discussed. We perform real data experiments as well as simulations to demonstrate the advantage of DTGA over the only existing competing algorithm [1] and the impacts of different parameters associated with data correlations on the network lifetime.

In duty-cycled wireless sensor networks (WSNs) for stochastic event monitoring, existing efforts are mainly concentrated on energy-efficient scheduling of sensor nodes to guarantee the coverage performance, ignoring another crucial issue of connectivity. The connectivity problem is extremely challenging in the duty-cycled WSNs due to the fact that the link connections between nodes are transient thus unstable. In this paper, we propose a new kind of network, partitioned synchronous network, to jointly address the coverage and connectivity problem. We analyze the coverage and connectivity performances of partitioned synchronous network and compare them with those of existing asynchronous network. We perform extensive simulations to demonstrate that the proposed partitioned synchronous network has a better connectivity performance than that of asynchronous network, while coverage performances of two types of networks are close.

We consider a high density of sensors randomly placed in a geographical area for event monitoring. The monitoring regions of the sensors may have significant overlap, and a subset of the sensors can be turned off to conserve energy, thereby increasing the lifetime of the monitoring network. Prior work in this area does not consider the event dynamics. In this paper, we show that knowledge about the event dynamics can be exploited for significant energy savings, by putting the sensors on a periodic on/off schedule. We discuss energy-aware optimization of the periodic schedule for the cases of an synchronous and a asynchronous network. To reduce the overhead of global synchronization, we further consider a spectrum of regionally synchronous networks where the size of the synchronization region is specifiable. Under the periodic scheduling, coordinated sleep by the sensors can be applied orthogonally to minimize the redundancy of coverage and further improve the energy efficiency. We consider the interactions between the periodic scheduling and coordinated sleep. We show that the asynchronous network exceeds any regionally synchronous network in the coverage intensity, thereby increasing the effectiveness of the event capture, though the opportunities for coordinated sleep decreases as the synchronization region gets smaller. When the sensor density is high, the asynchronous network with coordinated sleep can achieve extremely good event capture performance while being highly energy efficient.

In this paper, we propose a coordinate-free distributed boundary detection algorithm (CDBD). It adopts general sensing and communication models and exploits two centrality measures, i.e., betweenness and closeness. For CDBD, each node only needs to communicate with its $k$-hop neighbors twice and makes decision whether it itself is a boundary node independently. CDBD has advantages of fast convergence and low communication overhead. Extensive simulation demonstrates the desirable performance of CDBD.

In target tracking applications, active ultrasonic sensors can provide satisfactory distance estimations, but also suffer from inter-sensor-interference when they are not well scheduled. In this paper, we propose a distributed saturation degree based algorithm (DSDA), which assigns the TDMA slot in wireless sensor networks distributively in order to avoid the interference. By adopting a graph coloring technique, Saturation Degree Heuristic, this new algorithm can provide near-optimal slot number in a totally distributed way. Simulation results demonstrate the efficiency of DSDA in terms of slot number, system scalability, tracking accuracy and energy consumption.

Wireless rechargeable sensor networks (WRSNs) have emerged as an alternative to solving the challenges of size and operation time posed by traditional battery-powered systems. In this paper, we study a WRSN built from the industrial wireless identification and sensing platform (WISP) and commercial off-the-shelf RFID readers. The paper-thin WISP tags serve as sensors and can harvest energy from RF signals transmitted by the readers. This kind of WRSNs is highly desirable for indoor sensing and activity recognition, and is gaining attention in the research community. One fundamental question in WRSN design is how to deploy readers in a network to ensure that the WISP tags can harvest sufficient energy for continuous operation. We refer to this issue as the energy provisioning problem. Based on a practical wireless recharge model supported by experimental data, we investigate two forms of the problem: point provisioning and path provisioning. Point provisioning uses the least number of readers to ensure that a static tag placed in any position of the network will receive a sufficient recharge rate for sustained operation. Path provisioning exploits the potential mobility of tags (e.g., those carried by human users) to further reduce the number of readers necessary: mobile tags can harvest excess energy in power-rich regions and store it for later use in power-deficient regions. Our analysis shows that our deployment methods, by exploiting the physical characteristics of wireless recharging, can greatly reduce the number of readers compared with those assuming traditional coverage models.

Recently, wireless recharging technologies have merged as a promising approach to address the energy constraint problem in Wireless Sensor Networks (WSNs). Far from other energy-harvesting sensor nodes, wireless rechargeable sensor nodes are thin small-size, enabling a large range of applications such as embedded infrastructure sensing and human activity recognition. A typical Wireless Rechargeable Sensor Network (WRSN) includes two components: i) a collection of rechargeable sensor nodes and ii) several readers, capable of mobility and functioning as energy distributors and data collectors. In this paper, we for the first time investigate the optimal scheduling problem in WRSN for stochastic event capture, i.e., how to jointly mobilize the readers for energy distribution and schedule sensor nodes for efficient event capture. We extensively study the problem and analyze the quality of capture for different application scenarios. At last, numerical results are offered to demonstrate the correctness and effectiveness of our solutions.

Wireless sensor and actor networks (WSANs) are service-oriented environments, where sensors request actors to service their detected events and actors move to deliver the desired services. Because of their openness and unattended nature, these networks are vulnerable to various security attacks. In this paper we address service fraud attacks for the first time, whose objective is to stop the normal use of actor services by fake service requests and/or delivery. To mitigate this type of security attacks, we propose a novel cooperative authentication scheme. With the scheme, a sensor's service request is cooperatively authenticated by the sensors that witness the same event, and an actor's service delivery effort is cooperatively authenticated by the sensors that witness the actor's behavior. Considering the presence of compromised sensor/actor nodes, the trustworthiness of each authenticated service delivery process is subject to location consistency check and witness diversity check. It may then be taken into account to adjust the corresponding actor's trust rating so as to influence future actor service selection. We analyze the communication overhead and the security strength of the scheme. We show that our scheme ensures fraud-resistant actor services in our considered WSAN environment.

In wireless sensor networks (WSNs), trap coverage has recently been proposed to tradeoff between the availability of sensor nodes and sensing performance. It offers an efficient framework to tackle the challenge of limited resources in large scale sensor networks. Currently, existing works only studied the theoretical foundation of how to decide the deployment density of sensors to ensure the desired degree of trap coverage. However, the practical issues such as how to efficiently schedule sensor node to guarantee trap coverage under an arbitrary deployment is still left untouched. In this paper, we formally formulate the Minimum Weight Trap Cover Problem and prove it is an NP-hard problem. To solve the problem, we introduce a bounded approximation algorithm, called Trap Cover Optimization (TCO) to schedule the activation of sensors while satisfying specified trap coverage requirement. The performance of Minimum Weight Trap Coverage we find is proved to be at most O(ρ) times of the optimal solution, where ρ is the density of sensor nodes in the region. To evaluate our design, we perform extensive simulations to demonstrate the effectiveness of our proposed algorithm and show that our algorithm achieves at least 14% better energy efficiency than the state-of-the-art solution.

The copious social and user generated contents, like Facebook and Youtube, are re-shaping the way people share, access, and digest information. Although flourishing in Internet, content sharing services are still considered expensive and not ready for mobile users of vehicular networks. In this paper, we propose VTube, an autonomous and cost-effective infrastructure, to facilitate the localized content publish/subscribe in an urban area. VTube relies on the distributed low-cost light-weight storage buffers, namely roadside buffer, installed in the city facilities, such as stores, museums, cafeteria, etc., to cache and publish contents for mobile users. The contents at different storage buffers are then transported to different locations by moving vehicles and cached collaboratively in both vehicles and storage buffers across the city. In this work, we unfold the design of VTube by first presenting the detailed design principles and practices of VTube. Given the content availability and capacity of the buffer storage, we then develop a mathematical model to evaluate the mean download delay of mobile users. Using the delay as an input, we formulate the content replication problem in roadside buffers as a stochastic programming problem to attain the mean system-wide minimum download delay. Finally, we propose a fully distributed random walk based algorithm to solve the optimization problem. Extensive simulations demonstrate that VTube can minimize the download delay of users because of the exploitation of vehicle mobility and distributed buffer storage at different locations.

Based on spatiotemporal properties of neighborhood and mobile properties of nodes in the networks, we propose Selective Reference Mechanism to trade off between the delay and overhead of neighbor discovery in low-duty-cycle WSNs. Extensive simulation and test-bed experiment confirm our theoretical analysis, showing as much as 35.4% increase in discovery probability, 38.6% reduction in discovery delay and 27.7% reduction in total energy consumption.

To bridge the gap between insufficiency of existing proximity authentication solutions and the increasing demand of high security guarantee for access control systems, we develope a WISP-based access control system combing electronic and mechanical authentication methods. In our authentication, encryption complexity is changeable and trusted users can share privileges with each other. During experiments, our system has achieved 95% authentication accuracy rate with up to 3 different users.

Mobility pattern of device users plays a crucial role in a wide range of mobile computing applications, including data forwarding, content sharing, information search and advertising. Hence, it is important to characterize the mobility path information of users, so as to accurately predict user mobility. In this paper, we introduce two typical user mobility patterns: standard Markov and semi-Markov models. Especially, we experimentally explore the correlation of community and geography information in Mobile Social Networks (MSNets), and analyze user sojourn time distribution over communities. Both of theoretical analysis and trace-driven simulation results show that semi-Markov model is more effective in characterizing user mobility pattern and further making more accurate mobility prediction compared with standard Markov model.

The multidimensional blast furnace system is one of the most complex industrial systems and, as such, there are still many unsolved theoretical and experimental difficulties, such as silicon prediction and blast furnace automation. For this reason, this paper is concerned with developing data-driven models based on the Volterra series for this complex system. Three kinds of different low-order Volterra filters are designed to predict the hot metal silicon content collected from a pint-sized blast furnace, in which a sliding window technique is used to update the filter kernels timely. The predictive results indicate that the linear Volterra predictor can describe the evolvement of the studied silicon sequence effectively with the high percentage of hitting the target, very low root mean square error and satisfactory confidence level about the reliability of the future prediction. These advantages and the low computational complexity reveal that the sliding-window linear Volterra filter is full of potential for multidimensional blast furnace system. Also, the lack of the constructed Volterra models is analyzed and the possible direction of future investigation is pointed out.

In this paper, we investigate the problem of flow routing and fair bandwidth allocation under interference constraints for multihop wireless networks. We first develop a novel isotonic routing metric, RI3M, considering the influence of interflow and intraflow interference. The isotonicity of the routing metric is proved using virtual network decomposition. Second, in order to ensure QoS, an interference-aware max-min fair bandwidth allocation algorithm, LMX:M3F, is proposed where multiple paths (determined by using the routing metric) coexist for each user to the base station. In order to solve the algorithm, we develop an optimization formulation that is modeled as a multicommodity flow problem where the lexicographically largest bandwidth allocation vector is found among all optimal allocation vectors while considering constraints of interference on the flows. We compare our RI3M routing metric and LMX:M3F bandwidth allocation algorithm with various interference-based routing metrics and interference-aware bandwidth allocation algorithms established in the literature. We show that RI3M and LMX:M3F succeed in improving network performance in terms of delay, packet loss ratio, and bandwidth usage.

Privacy threat is one of the critical issues in multi-hop wireless networks, where attacks such as traffic analysis and flow tracing can be easily launched by a malicious adversary due to the open wireless medium. Network coding has the potential to thwart these attacks since the coding/mixing operation is encouraged at intermediate nodes. However, the simple deployment of network coding cannot achieve the goal once enough packets are collected by the adversaries. On the other hand, the coding/mixing nature precludes the feasibility of employing the existing privacy-preserving techniques, such as Onion Routing. In this paper, we propose a novel network coding based privacy-preserving scheme against traffic analysis in multi-hop wireless networks. With homomorphic encryption on Global Encoding Vectors (GEVs), the proposed scheme offers two significant privacy-preserving features, packet flow untraceability and message content confidentiality, for efficiently thwarting the traffic analysis attacks. Moreover, the proposed scheme keeps the random coding feature, and each sink can recover the source packets by inverting the GEVs with a very high probability. Theoretical analysis and simulative evaluation demonstrate the validity and efficiency of the proposed scheme.

Abstract Vehicular Ad hoc Network (VANET) emerges as a promising technology and has chances of very likely to be deployed in the coming years. The security of vehicular networks will be an important way to facilitate road safety. In this paper, we are concerned with the problem of effective and efficient distribution of the certificate revocation units (RSUs) in vehicular networks. We propose a novel distributed approach by introducing mobile nodes that have public safety. An optimal route for mobile nodes is designed to cover blind areas under both delay and cost constraints. The performances of the movement‐assisted approach are measured and evaluated by extensive experiments in large scale networks for Certificate Revocation List (CRL) distribution in VANET. The results show that the proposed movement‐assisted approach obviously improves the performance. Copyright © 2009 John Wiley & Sons, Ltd.

The rapid development of wireless technology plays extremely important roles in monitoring and control related applications nowadays.This special issue aims at bringing together state-of-the-art contributions of wireless monitoring, control, actuator coordination as well as their co-designs.It is shown in this proposal that there are a number of potential contributors, as well as reviewers, with original research results to this special issue, and the guest editors are capable of managing the special issue.In recent years, the demand for wireless communications in many monitoring and control applications has grown tremendously, such as military, aerospace, industrial, commercial, environmental, and health monitoring, etc.Some new technologies like Zigbee, Wi-Fi, Mobile Robots, and Bluetooth have already made significant contribution to data acquisition.At the same time, there arise some new challenges to guarantee a highly reliable, accurate and fault-tolerant process.It is a critical issue to develop innovative approaches to deal with multi-variable, multi-space problem domains (detection, identification, tracking, data fusion, energy-efficiency, and fault-tolerant framework) as well as practical implementation in wireless monitoring and control application.The purpose of the special issue is to focus on the novel ways by which monitoring, detection, identification, coordination, and control schemes are applied in wireless monitoring and control applications.Specific areas of interest include (but are not limited to):

In this paper, we study the maximum throughput of IEEE 802.15.4 enabled wireless sensor networks (WSNs). In general, deploying more sensor nodes would increase the throughput, but if excessive sensor nodes were involved to report data, the throughput might decrease due to packet collisions. In this paper, we analyze the CSMA/CA performance of the IEEE 802.15.4 enabled WSNs, and develop an analytical throughput model between the maximum throughput and the optimal number of deployed sensor nodes. Extensive simulations are conducted to verify our analytical throughput model.

Privacy threat is a very serious issue in multi-hop wireless networks (MWNs) since open wireless channels are vulnerable to malicious attacks. Source unobservability is an attractive and desirable security property for many privacy-sensitive applications, and dummy messages are most commonly used to achieve this property. However, dummy messages may incur severe performance degradation or even service denial due to the explosion of network traffic. In this paper, we propose a novel scheme, called SUNC (Source Unobservability by Network Coding), to prevent traffic explosion while achieving source unobservability. With SUNC, specially designed dummy messages can be absorbed at intermediate nodes, and, thus, traffic explosion can be naturally prevented. In addition, SUNC can offer forwarder blindness, which is an important privacy property for thwarting internal attackers. Security analysis and performance evaluation demonstrate the efficacy and efficiency of the proposed SUNC.

In this paper, we consider the problem of sensor scheduling with limited resources. Two sensors are used to measure the state of a discrete-time linear process. We assume that each sensor has a maximum duty cycle and at most one sensor can communicate with a remote estimator at each time step due to the limited communication bandwidth. When a sensor is scheduled to send data, it sends the most recent D measurement data to the estimator. Upon receiving the measurement data from the sensors, the estimator computes the optimal estimate of the state of the process. We first present a necessary condition for a sensor scheduling scheme to be optimal. Based on this necessary condition, we construct an optimal scheduling scheme that minimizes the estimation error at the estimator and at the same time satisfies the energy and communication bandwidth constraints. We also provide a sufficient condition on the minimum D such that an optimal scheduling scheme can be constructed. Examples are provided throughout the paper to demonstrate the results developed.

Fairness and system throughput, influenced by wireless interference, are major objectives of resource allocation in wireless networks. Whereas traditionally max-min fairness protocols have been developed for wired networks (where interference is not a factor for network performance), in this paper we investigate the problem of flow routing and fair bandwidth allocation under the constraints of the physical interference model for multihop wireless networks. A max-min fair (MMF) routing algorithm under physical interference constraints is proposed where multiple candidate paths coexist for each user to the base station. The algorithm is formulated as a multicommodity flow problem where the lexicographically largest bandwidth allocation vector is found among all optimal allocation vectors. We compare our approach with two MMF interference routing algorithms in the literature that use the protocol interference model to garner fair bandwidth allocation. We show that our algorithm performs better in terms of blocking probability, bandwidth usage and link load.

Multiple channels in Wireless Sensor Networks (WSNs) are often exploited to support parallel transmission and reduce interference. However, there are many challenges, such as extra communication overhead, posed to the energy constraint of WSNs by the multi-channel usage coordination. In this paper, we propose a Regret Matching based Channel Assignment algorithm (RMCA) to address those challenges. The advantage of RMCA is that it is highly distributed and requires very limited information exchanges among sensor nodes. It converges almost surely to the set of correlated equilibrium. Moreover, RMCA can adapt the channel assignment among sensor nodes to the time-variant flows and network topology. Simulations show that RMCA achieves good network performance in terms of both delivery ratio and packet latency.

We propose a steepest descent method to compute optimal control parameters for balancing between multiple performance objectives in stateless stochastic scheduling, wherein the scheduling decision is effected by a simple constant-time coin toss operation only. We apply our method to the scheduling of a mobile sensor's coverage time among a set of points of interest (PoIs). The coverage algorithm is guided by a Markov chain wherein the sensor at PoI i decides to go to the next PoI j with transition probability pij . We use steepest descent to compute the transition probabilities for optimal tradeoff between two performance goals concerning the distributions of per-PoI coverage times and exposure times, respectively. We also discuss how other important goals such as energy efficiency and entropy of the coverage schedule can be addressed. For computational efficiency, we show how to optimally adapt the step size in steepest descent to achieve fast convergence. However, we found that the structure of our problem is complex in that there may exist surprisingly many local optima in the solution space, causing basic steepest descent to get stuck easily at a local optimum. To solve the problem, we show how proper incorporation of noise in the search process can get us out of the local optima with high probability. We provide simulation results to verify the accuracy of our analysis, and show that our method can converge to the globally optimal control parameters under different assigned weights to the performance goals and different initial parameters.

In this paper, we formulate multi-channel assignment in Wireless Sensor Networks (WSNs) as an optimization problem and show it is NP-hard. We then propose a distributed Game Based Channel Assignment algorithm (GBCA) to solve the problem. GBCA takes into account both the network topology information and transmission routing information. We prove that there exists at least one Nash Equilibrium in the channel assignment game. Furthermore, we analyze the sub-optimality of Nash Equilibrium and the convergence of the Best Response in the game. Simulation results are given to demonstrate that GBCA can reduce interference significantly and achieve satisfactory network performance in terms of delivery ratio, throughput, channel access delay and energy consumption.

In mobile social networks (MSNets), data dissemination is an important topic, which has not been widely investigated yet. Active data dissemination is a networking paradigm where a superuser intentionally facilitates the connectivity in the network. One of the key challenges under this paradigm is how to design the most efficient superuser route to achieve certain properties of end-to-end connectivity. Most existing solutions only focus on the network with stationary users or strongly constrained node mobility, and assume the superuser always moves with a fixed route. In this paper, we propose a flexible approach to design the superuser routes, considering the realistic user movements in MSNets. To the best of our knowledge, this work is the first to study active data dissemination from the social network perspective. We explore the geographic regularity of human mobility in the network, employ a semi-Markov analytical model to describe such mobility pattern, and hence formulate the superuser route design as a combinational optimization problem of Convex Optimization and Traveling Salesman Problem by exploiting social network concepts including communities and centrality. Extensive trace-driven simulations show that our approach consistently outperforms other existing superuser route design algorithms in terms of delivery ratio and energy efficiency.

We consider the problem of gathering correlated sensor data by a sink node in a wireless sensor network. We design efficient distributed protocols to maximize the network lifetime subject to nodal energy constraints. Many existing approaches address the routing layer only, but the routing often interacts with physical-layer power control and MAC-layer link access. We present a first effort to maximize the network lifetime by jointly considering the three layers. We first solve the joint power control and routing problem, by assuming that the link access probabilities are known. We show that the problem is convex and propose a distributed algorithm, JRPA, as solution. When the link access probabilities are unknown, we then generalize the problem to encompass all three layers of routing, power control, and link random access. The general problem is non-convex; a duality gap exists when the Lagrangian dual method is employed. We propose an efficient heuristic algorithm, JRPRA, to solve the general problem. Numerical results show that JRPRA is highly effective; particularly, even without the best link access probabilities pre-determined for JRPA, JRPRA achieves extremely competitive performance. Our results also show the convergence of the algorithms and their advantages over existing solutions.

The linear frequency-modulated continuous-wave (LFMCW) millimeter-wave radar has been widely used in automotive industry applications. The motivation of this paper is to propose a segmented range detection (SRD) method on the foundation of plentiful mature research on the target detection of LFMCW radar. According to the SRD method, the detection range (about 150m in the scene of vehicular collision warning) is divided into several segments and triangular modulation waveform which consists of several different frequencies is designed. Compared with typical digital signal processing method, the SRD method can availably decrease the bandwidth of the IF signal, therefore the in-band noise will be significantly reduced and the signal to noise ratio (SNR) improved. Additionally, different range resolutions can be achieved and average range resolution improved. The SRD method is also validated through multiple outdoor experiments with the use of 60 GHz LFMCW millimeter-wave radar system designed by our research group. The efficiency of the SRD method is demonstrated in terms of ranging accuracy and range resolution after the analysis of experiment results.

Technology advancements in robotics and communication make it possible to combine mobility and static wireless sensor networks. Being able to locomote enables sensors to (1) adjust their positions for better sensing quality, (2) ensure field coverage by spreading out and (3) fill up communication gap to guarantee information sharing in wireless environment. Concerning the above three criteria, we propose a autonomous management scheme for mobile sensors, aims to achieve distributed sensor position control. Accordingly, we developed a testbed for mobile sensor network verification, with good interfaces to obtain ranging measurements and control sensor motion. Implementation details of building static and mobile sensors are given. Finally we present experiment results to verify the proposed sensor control scheme.

In this paper, we formulate a flow control optimization problem for wireless sensor networks with lifetime constraint and link interference in an asynchronous setting. Our formulation is based on the network utility maximization framework, in which a general utility function is used to characterize the network performance such as throughput. To solve the problem, we propose a fully asynchronous distributed algorithm based on dual decomposition, and theoretically prove its convergence. The proposed algorithm can achieve the maximum utility. Extensive simulations are conducted to demonstrate the efficiency of our algorithm and validate the analytical results.

Congestion in wireless sensor networks (WSNs) not only causes severe information loss but also leads to excessive energy consumption. To address this problem, a novel scheme for congestion avoidance, detection and alleviation (CADA) in WSNs is proposed in this paper. By exploiting data characteristics, a small number of representative nodes are chosen from those in the event area as data sources, so that the source traffic can be suppressed proactively to avoid potential congestion. Once congestion occurs inevitably due to traffic mergence, it will be detected in a timely way by the hotspot node based on a combination of buffer occupancy and channel utilization. Congestion is then alleviated reactively by either dynamic traffic multiplexing or source rate regulation in accordance with the specific hotspot scenarios. Extensive simulation results under typical congestion scenarios are presented to illuminate the distinguished performance of the proposed scheme.

Abstract With the development of robotics and embedded system, utilizing mobile sensors to capture stochastic events is emerging as a promising method to monitor a region of interest (RoI). In previous work, the quality of monitoring (QoM) is evaluated based on the stochastic events capture without taking the energy of motion into consideration. Since sensor nodes are normally constrained by limited energy capability, it is desirable to guide the mobile sensor in an energy‐efficient motion to capture events information. In this paper, by analyzing the different kinds of surveillance that may result in different required QoM, we obtain the expected Information captured Per unit of Energy consumption (IPE), which is a function with multiple parameters including the event type, the event dynamics, and the velocity of the mobile sensor. Our analysis is based on a realistic energy model of motion, and can achieve suboptimal motion plans by adopting existing typical approximation algorithm, and thus enable the sensor velocity to be optimized for capturing stochastic events information. We propose approximation algorithms to enable the tradeoff between the computation and efficiency, which make motion plans more practical in some realistic scenarios. The efficiency and effectiveness of proposed algorithm are validated by the extensive simulations. Copyright © 2009 John Wiley & Sons, Ltd.

Abstract This paper addresses the localization problem in wireless sensor networks using signal strength. We use a kernel function to measure the similarities between sensor nodes. The kernel matrix can be naturally defined in terms of the signal strength matrix. We show that the relative locations of sensor nodes can be obtained by solving a dimension reduction problem. To capture the structure of the whole network, we use the kernel spectral regression (KSR) method to estimate the relative locations of the sensor nodes. Given sufficient anchor nodes, the relative locations can be aligned to global locations. The key benefits of adopting KSR are that it allows us to define a graph to optimally preserve the topological structure of the sensor network, and a kernel function can capture the nonlinear relationship in the signal space. Simulation results show that we can achieve small average location error with a small number of anchors. We also compare our method with several related methods, and the results show that KSR is more efficient than the others in our simulated sensor networks. Copyright © 2009 John Wiley & Sons, Ltd.

In the paper, we study the utility-lifetime tradeoff in wireless sensor networks (WSNs) by formulating it as a constrained multi-objective optimization problem. Because of the coupling in the objective function, auxiliary variables are introduced to decouple it. We adopt Lagrange duality method to decompose the problem and regulate the rates through the link congestion price. We introduce the concept of inconsistent coordination price to balance the energy consumption of the sensor nodes. Based on the congestion and inconsistent coordination price, a distributed algorithm using gradient projection is proposed. Numerical results show the convergence of our algorithm, the tradeoff of utility-lifetime as well as the necessity of congestion control in WSNs.

As an emerging application, tracking mobile targets using distributed and networked sensors is gaining noticeable attentions in recent years. In this paper, we utilize ultrasonic sensors, which are traditionally viewed as basic ranging components, to build a target detection and tracking system. An integrated sensor node, with one communication/computation module, one Passive InfraRed (PIR) module and three ultrasonic modules, is designed to support local detection/ranging, data filtering and result reporting. Comprised of a set of the designed sensor nodes, our system is able to track the position of a dynamic target, using Extended Kalman Filter. Simulations and experimental results are given to evaluate performance bound and demonstrate the efficiency of our system.

Wireless sensor location estimation is an important area and attracts considerable research interests. In this paper, we present a novel graph embedding method for the localization problem by using signal strengths. We view the wireless sensor nodes as a group of distributed devices, and employ an appropriate kernel function to measure the similarity between sensors. The kernel function can be naturally defined according to the signal strength matrix. Then we formulate the localization problem as a graph embedding problem. Finally, we use the kernel locality preserving projection (KLPP) technique to estimate the relative locations of all sensor nodes. Given sufficient number of anchors, the relative locations can be transformed into physical locations. The main advantage of formulating the localization problem as graph embedding problem is that it allows us to construct a graph to preserve the topological structure of the sensor networks. We evaluate our method based on various network topologies, and analyze its performance. We also compare our method with several existing methods, and demonstrate the high efficiency of our proposed method.

This paper presents an adaptive sensor activation for target tracking in wireless sensor networks by dynamically adjusting the range of sensor selective activation instead of fixed one. A closed-loop control algorithm for the range of adaptive sensor activation is designed according to the online feedback of the tracking quality. The failed tracking case can also be handled by the proposed algorithm. Extensive simulation results show that the adaptive sensor activation achieves higher performance in terms of tracking effect and energy efficiency.

This paper studies the problem of determining the sensors' locations in wireless sensor networks. To alleviate the influence of the noise and the inaccurate measurement in the complicated environment, rather than estimating the pair-wise Euclidean distance between sensors, we use the geodesic distance to measure the dissimilarity between sensors, and employ the isomap algorithm to determine the relative locations of sensors. Given sufficient anchors, the relative locations can be aligned to absolute locations by using coordinate transformation. The coordinate transformation matrix can be obtained by minimizing the sum of squares of the errors between the true locations of the anchors and their transformed locations. Since Isomap is parameter-sensitive, we also present an adaptive parameter selection procedure based on the locations of anchors. Simulation results show that the Isomap algorithm achieves smaller average location error with little quantity of anchors.

Protocol testing has been one of the most active fields in computer networks. In Wireless Sensor Networks (WSNs), before directly employing protocols on hardware testbed, we expect an effective test framework to verify the logical correctness of protocols on computers, which could facilitate the whole test process. Towards this goal, this paper introduces an upper test framework in WSNs, namely Protocol Testing Framework for WSNs (PTFW).

An AMR(Automatic Meter Reading) system is used to automatically collect data from meters. In this paper we design and implement a Wireless Automatic Meter Reading System to solve the problem in existing AMR system. The system consists of wireless modules, measure meters and management center. A general wireless module, is developed to transfer the data gotten from traditional analogue meters or digital meters by using ZigBee communication network. We describe a system framework including the hardware of wireless module and software design of data acquisition. We also develop the operator interface on management center. Finally, we build up a Wireless Automatic voltage Meter Reading System as a case study. The proposed system has board application foreground in industry.

A mobile sensor is used to cover a number of points of interest (PoIs) where dynamic events appear and disappear according to given random processes. It has been shown in [1] that for Step and Exponential utility functions, the quality of monitoring (QoM), i.e., the fraction of information captured about all events, increases as the speed of the sensor increases. This work, however, does not consider the energy of motion, which is an important constraint for mobile sensor coverage. In this paper, we analyze the expected information captured per unit of energy consumption (IPE) as a function of the event type, the event dynamics, and the speed of the mobile sensor. Our analysis uses a realistic energy model of motion, and it allows the sensor speed to be optimized for information capture. We present simulation results to verify and illustrate the analytical results.

We analyze the ability of a stochastic coverage algorithm to achieve both accurate threat-based coverage and effective information capture. When mobile sensors are used to cover the region over time, the goal of threat-based coverage is to allocate the sensors' coverage time between the subregions in proportion to their threat levels. We show that, in contrast to prior results on mobile coverage for maximizing simple event capture, limiting mobility by strategically pausing the sensor is important for threat-based coverage of physical world monitoring. Besides being energy efficient, pausing has two desirable effects. First, it can improve the accuracy of the threat-based coverage, in particular, the accuracy increases monotonically with a pause time parameter, and a large enough parameter will ensure exact matching of the sensor's coverage profile with the region's threat profile. Second, diverse natural phenomena require a non-negligible sensing time to overcome statistical uncertainties posed by the random nature of the phenomena. Suitable pausing allows a subregion to be observed long enough for reliable results.

We consider a high density of sensors randomly placed in a geographical area for event monitoring. The monitoring regions of the sensors may have significant overlap, and a subset of the sensors can be turned off to conserve energy, thereby increasing the lifetime of the monitoring network. Prior work in this area does not consider the event dynamics. In this paper, we show that knowledge about the event dynamics can be exploited for significant energy savings, by putting the sensors on a periodic on/off schedule. We discuss energy-aware optimization of the periodic schedule for both cases of a synchronous and an asynchronous network. Under the periodic scheduling, coordinated sleep by the sensors can be applied orthogonally to minimize the redundancy of coverage and further improve the energy efficiency. We consider four points in the design space: synchronous periodic scheduling with and without coordinated sleep, and asynchronous periodic scheduling with and without coordinated sleep. We show that the asynchronous network exceeds the synchronous network in the coverage intensity, thereby increasing the effectiveness of the event capture, though it may also reduce the opportunities for coordinated sleep. When the sensor density is high, the asynchronous network with coordinated sleep can achieve extremely good event capture performance while being highly energy-efficient.

Abstract Energy constraint is the most conspicuous characteristic in wireless sensor networks (WSN). Node deployment, dynamic topology control, and data transmission in WSN all consume a large amount of energy. Therefore, proper adjustment of transmission power (TP) contributes much energy saving. In this paper, a new TP adjustment method based on Fuzzy Control Theory, called FCTP, is proposed for the dynamic topology control. The simulation results show that this method is more robust to tolerate accidental interfere, more rapidly convergent, and more energy efficient than other TP adjustment approaches, which lead to longer network lifetime. Copyright © 2008 John Wiley & Sons, Ltd.

Channel capacity and node energy represent resources and constraints in designing efficient routing schemes for Wireless Sensor Networks(WSNs). To delivery more data, a higher rate is desirable, which however consumes more energy and may demand more bandwidth. Hence, data transmission in WSNs should take into account both limited capacity and constrained energy. In this paper, we propose an utility-based nonlinear convex optimization formulation to maximize utility subject to the capacity and energy constraints. To achieve this, we introduce link interference set to represent all flows contention over a link, and it offers the capacity constraint over the specific link. For each node, we express the energy constraint with network lifetime requirement. A distributed solution with dual decomposition approach is proposed to address the optimization formulation. In addition, an Optimal Rate Routing (ORR) is developed by incorporating the optimization result to select the optimal rate route. Comparing with the previous schemes, ORR is able to achieve the highest utility, optimal rate selection during routing, and well-balanced performance.

This paper presents a theoretical model of control and communication over wireless sensor and actuator networks (WSANs). We propose two control schemes, a centralized control scheme (CC) in which decisions are made based on global information, and a distributed control scheme (DC) that enables distributed actuators to make decisions locally. Because of global information, CC can obtain optimal control at each step. However, when that information is delivered over lossy wireless channels, it could become unstable. It is demonstrated by simulations that DC could also stabilize the control system analogously with the CC, though with more steps.

Although most existing wireless systems and protocols are based on two-dimensional design, in reality, a variety of networks operate in three-dimensions. The design of protocols for 3D networks is surprisingly more difficult than the design of those for 2D networks. In this paper, we investigate how to design load balancing routing for 3D networks. Most current wireless routing protocols are based on Shortest Path Routing (SPR), where packets are delivered along the shortest route from a source to a destination. However, under uniform communication, shortest path routing suffers from uneven load distribution in the network, such as crowed center effect where the center nodes have more load than the nodes in the periphery. Aim to balance the load, we propose a novel 3D routing method, called 3D Circular Sailing Routing (CSR), which maps the 3D network onto a sphere and routes the packets based on the spherical distance on the sphere. We describe two mapping methods for CSR and then provide theoretical proofs of their competitiveness compared to SPR. For both proposed methods, we conduct simulations to study their performance in grid and random networks.

Wireless sensor and actuator networks (WSAN) are increasingly popular due to their abilities to detect and react to interesting events in the physical world. In this paper, a new indoor WSAN system in the context of pursuit evasion games (PEGs) is designed and implemented. Our goal is to take advantage of the WSAN capabilities to perform target tracking and capture. We design the system control model, and present the detailed implementation of the WSAN, in particular the distributed evader tracking, pursuer control components and the system/user interfaces. We also present algorithms used to achieve low cost, low energy consumption, and robustness of performance in the WSAN. Experiments demonstrate that the pursuer captures the evader successfully in all the runs. Our work provides practical solutions for designing and implementing a working WSAN for PEG tasks.

To preserve network connectivity is an important issue especially in wireless sensor network, where wireless links are easy to be disturbed and tiny sensors are even easy to fail accidently. Therefore, it is necessary to design a fault-tolerant network. A feasible method is to construct a k-vertex connected topology. In this paper, we consider k-connectivity of wireless network and propose a simple global algorithm (GAFTk) which preserves the network k-connectivity and reduces the maximal transmission power (TP). The average degree expectation of the topology generated by GAFTkis O(k2). Based on GAFTk, we propose a simple local algorithm (LAFTk) which preserves k-vertex connectivity while maintaining bi-directionality of the network. Simulation results show that GAFT/LAFT have better performance than other current fault-tolerant protocols.

This paper describes a basic coverage issue, and proposes a scheme named Grid Scan which is applied to calculate the basic coverage rate with arbitrary sensing radius of each node. Based on Grid Scan, a re-deployment approach is suggested to meet any k-covered rate in some region according to application requirements. The objective of our re-deployment scheme is to get equivalent coverage rate using less number of sensor nodes or to achieve higher coverage rate with the same number of sensor nodes. The results of simulation experiments support that Grid Scan based re-deployment is more effective to cover monitored area than random spread.

Many routing protocols have been proposed for wireless sensor networks in recent years. For some special applications, not only energy aware but link reliable is needed. Historical link status should be captured while making routing decisions. In this paper, we design a reliable link quality estimation based routing protocol (LQER), which integrates the approach of minimum hop field and (m, k). The performance of LQER is evaluated by simulation experiments to be more energy-aware, with lower loss rate and better scalability than MHFR (Z. Ma and Y. Sun, 2004) and MCR (F. Ye et al., 2001). Thus the whole network may obtain longer lifetime and better link quality.