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AMSPM: Adaptive Model Selection and Partition Mechanism for Edge Intelligence-driven 5G Smart City with Dynamic Computing Resources ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-03-16 Xin Niu, Xuejiao Cao, Chen Yu, Hai Jin
With the help of 5G network, edge intelligence (EI) can not only provide distributed, low-latency, and high-reliable intelligent services, but also enable intelligent maintenance and management of smart city. However, the constantly changing available computing resources of end devices and edge servers cannot continuously guarantee the performance of intelligent inference. In order to guarantee the
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A Differential Evolution Offloading Strategy for Latency and Privacy Sensitive Tasks with Federated Local-edge-cloud Collaboration ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-03-12 Yishan Chen, Wei Li, Junhong Huang, Honghao Gao, Shuiguang Deng
Due to an explosive growth in mobile devices and the rapid evolution of wireless communication technologies, local-edge-cloud computing is becoming an attractive solution for providing a higher-quality service by exploiting the multi-computation power of mobile devices, edge servers and cloud. However, as the tasks are latency and privacy sensitive, highly credible task offloading becomes a crucial
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Evaluating Compressive Sensing on the Security of Computer Vision Systems ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-03-13 Yushi Cheng, Boyang Zhou, Yanjiao Chen, Yi-Chao Chen, Xiaoyu Ji, Wenyuan Xu
The rising demand for utilizing fine-grained data in deep-learning (DL) based intelligent systems presents challenges for the collection and transmission abilities of real-world devices. Deep compressive sensing, which employs deep learning algorithms to compress signals at the sensing stage and reconstruct them with high quality at the receiving stage, provides a state-of-the-art solution for the
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Holistic Energy Awareness and Robustness for Intelligent Drones ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-03-13 Ravi Raj Saxena, Joydeep Pal, Srinivasan Iyengar, Bhawana Chhaglani, Anurag Ghosh, Venkata N. Padmanabhan, Prabhakar T. Venkata
Drones represent a significant technological shift at the convergence of on-demand cyber-physical systems and edge intelligence. However, realizing their full potential necessitates managing the limited energy resources carefully. Prior work looks at factors such as battery characteristics, intelligent edge sensing considerations, planning, and robustness in isolation. But a global view of energy awareness
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Flow-Time Minimization for Timely Data Stream Processing in UAV-Aided Mobile Edge Computing ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-03-13 Zichuan Xu, Haiyang Qiao, Weifa Liang, Zhou Xu, Qiufen Xia, Pan Zhou, Omer F. Rana, Wenzheng Xu
Unmanned Aerial Vehicles (UAVs) have gained increasing attention by both academic and industrial communities, due to their flexible deployment and efficient line-of-sight communication. Recently, UAVs equipped with base stations have been envisioned as a key technology to provide 5G network services for mobile users. In this article, we provide timely services on the data streams of mobile users in
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A Resource Allocation Scheme for Edge Computing Network in Smart City Based on Attention Mechanism ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-03-11 Zhengjie Sun, Hui Yang, Chao Li, Qiuyan Yao, Yun Teng, Jie Zhang, Sheng Liu, Yunbo Li, Athanasios V. Vasilakos
In recent years, the number of devices and terminals connected to the smart city has increased significantly. Edge networks face a greater variety of connected objects and massive services. Considering that a large number of services have different QoS requirements, it has always been a huge challenge for smart city to optimally allocate limited computing resources to all services to obtain satisfactory
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Accurate Localization in LOS/NLOS Channel Coexistence Scenarios Based on Heterogeneous Knowledge Graph Inference ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-03-07 Bojun Zhang, Xiulong Liu, Xin Xie, Xinyu Tong, Yungang Jia, Tuo Shi, Wenyu Qu
Accurate localization is one of the basic requirements for smart cities and smart factories. In wireless cellular network localization, the straight-line propagation of electromagnetic waves between base stations and users is called line-of-sight (LOS) wireless propagation. In some cases, electromagnetic wave signals cannot propagate in a straight line due to obstruction by buildings or trees, and
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Greentooth: Robust and Energy Efficient Wireless Networking for Batteryless Devices ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-03-01 Simeon Babatunde, Arwa Alsubhi, Josiah Hester, Jacob Sorber
Communication presents a critical challenge for emerging intermittently powered batteryless sensors. Batteryless devices that operate entirely on harvested energy often experience frequent, unpredictable power outages and have trouble keeping time accurately. Consequently, effective communication using today’s low-power wireless network standards and protocols becomes difficult, particularly because
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PaSTG: A Parallel Spatio-Temporal GCN Framework for Traffic Forecasting in Smart City ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-03-01 Xianhao He, Yikun Hu, Qing Liao, Hantao Xiong, Wangdong Yang, Kenli Li
Predicting future traffic conditions from urban sensor data is crucial for smart city applications. Recent traffic forecasting methods are derived from Spatio-Temporal Graph Convolution Networks (STGCNs). Despite their remarkable achievements, these spatio-temporal models have mainly been evaluated on small-scale datasets. In light of the rapid growth of the Internet of Things and urbanization, cities
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Hypergraph-based Truth Discovery for Sparse Data in Mobile Crowdsensing ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-28 Pengfei Wang, Dian Jiao, Leyou Yang, Bin Wang, Ruiyun Yu
Mobile crowdsensing leverages the power of a vast group of participants to collect sensory data, thus presenting an economical solution for data collection. However, due to the variability among participants, the quality of sensory data varies significantly, making it crucial to extract truthful information from sensory data of differing quality. Additionally, given the fixed time and monetary costs
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A Liquidity Analysis System for Large-Scale Video Streams in the Oilfield ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-29 Qiang Ma, Hao Yuan, Zhe Hu, Xu Wang, Zheng Yang
This article introduces LinkStream, a liquidity analysis system based on multiple video streams designed and implemented for oilfield. LinkStream combines a variety of technologies to solve several problems in computing power and network latency. First, the system adopts an edge-central architecture and tailoring based on spatio-temporal correlation, which greatly reduces computing power requirements
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Who Should We Blame for Android App Crashes? An In-Depth Study at Scale and Practical Resolutions ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-29 Liangyi Gong, Hao Lin, Daibo Liu, Lanqi Yang, Hongyi Wang, Jiaxing Qiu, Zhenhua Li, Feng Qian
Android system has been widely deployed in energy-constrained IoT devices for many practical applications, such as smart phone, smart home, healthcare, fitness, and beacons. However, Android users oftentimes suffer from app crashes, which directly disrupt user experience and could lead to data loss. Till now, the community have limited understanding of their prevalence, characteristics, and root causes
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TG-SPRED: Temporal Graph for Sensorial Data PREDiction ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-28 Roufaida Laidi, Djamel Djenouri, Youcef Djenouri, Jerry Chun-Wei Lin
This study introduces an innovative method aimed at reducing energy consumption in sensor networks by predicting sensor data, thereby extending the network’s operational lifespan. Our model, TG-SPRED (Temporal Graph Sensor Prediction), predicts readings for a subset of sensors designated to enter sleep mode in each time slot, based on a non-scheduling-dependent approach. This flexibility allows for
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DTSSN: A Distributed Trustworthy Sensor Service Network Architecture for Smart City ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-28 Shengye Pang, Jiayin Luo, Xinkui Zhao, Jintao Chen, Fan Wang, Jianwei Yin
The smart city is an increasingly popular concept when it comes to urban development. In a smart city, numerous sensor services are generated by IoT sensors in a distributed manner, requiring proper management and effective interaction to guarantee the connectivity of different regions. However, the sensitive nature of sensor data raises concerns over joining public cloud centers or edge servers, despite
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Full View Maximum Coverage of Camera Sensors: Moving Object Monitoring ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-26 Hongwei Du, Jingfang Su, Zhao Zhang, Zhenhua Duan, Cong Tian, Ding-Zhu Du
The study focuses on achieving full view coverage in a camera sensor network to effectively monitor moving objects from multiple perspectives. Three key issues are addressed: camera direction selection, location selection, and moving object monitoring. There are three steps to maximize coverage of moving targets. The first step involves proposing the Maximum Group Set Coverage (MGSC) algorithm, which
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Behave Differently when Clustering: A Semi-asynchronous Federated Learning Approach for IoT ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-23 Boyu Fan, Xiang Su, Sasu Tarkoma, Pan Hui
The Internet of Things (IoT) has revolutionized the connectivity of diverse sensing devices, generating an enormous volume of data. However, applying machine learning algorithms to sensing devices presents substantial challenges due to resource constraints and privacy concerns. Federated learning (FL) emerges as a promising solution allowing for training models in a distributed manner while preserving
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BEANet: An Energy-efficient BLE Solution for High-capacity Equipment Area Network ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-23 Yifan Xu, Fan Dang, Kebin Liu, Zhui Zhu, Xinlei Chen, Xu Wang, Xin Miao, Haitian Zhao
The digital transformation of factories has greatly increased the number of peripherals that need to connect to a network for sensing or control, resulting in a growing demand for a new network category known as the Equipment Area Network (EAN). The EAN is characterized by its cable-free, high-capacity, low-latency, and low-power features. To meet these expectations, we present BEANet, a novel solution
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Ultrasound Communication Using the Nonlinearity Effect of Microphone Circuits in Smart Devices ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-23 Guoming Zhang, Xiaoyu Ji, Xinyan Zhou, Donglian Qi, Wenyuan Xu
Acoustic communication has become a research focus without requiring extra hardware and facilitates numerous near-field applications such as mobile payment. To communicate, existing researchers use either an audible frequency band or an inaudible one. The former gains a high throughput but endures being audible, which can be annoying to users. The latter, although inaudible, falls short in throughput
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FLoRa+: Energy-efficient, Reliable, Beamforming-assisted, and Secure Over-the-air Firmware Update in LoRa Networks ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-23 Zehua Sun, Tao Ni, Huanqi Yang, Kai Liu, Yu Zhang, Tao Gu, Weitao Xu
The widespread deployment of unattended LoRa networks poses a growing need to perform Firmware Updates Over-The-Air (FUOTA). However, the FUOTA specifications dedicated by LoRa Alliance fall short of several deficiencies with respect to energy efficiency, transmission reliability, multicast fairness, and security. This article proposes FLoRa+, energy-efficient, reliable, beamforming-assisted, and secure
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SecEG: A Secure and Efficient Strategy against DDoS Attacks in Mobile Edge Computing ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-23 Haiyang Huang, Tianhui Meng, Jianxiong Guo, Xuekai Wei, Weijia Jia
Application-layer distributed denial-of-service (DDoS) attacks incapacitate systems by using up their resources, causing service interruptions, financial losses, and more. Consequently, advanced deep-learning techniques are used to detect and mitigate these attacks in cloud infrastructures. However, in mobile edge computing (MEC), it becomes economically impractical to equip each node with defensive
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Room-Scale Location Trace Tracking via Continuous Acoustic Waves ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-20 Jie Lian, Xu Yuan, Jiadong Lou, Li Chen, Hao Wang, Nianfeng Tzeng
The increasing prevalence of smart devices spurs the development of emerging indoor localization technologies for supporting diverse personalized applications at home. Given marked drawbacks of popular chirp signal-based approaches, we aim to develop a novel device-free localization system via the continuous wave of the inaudible frequency. To achieve this goal, solutions are developed for fine-grained
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Multi-sensor Data-driven Route Prediction in Instant Delivery with a 3-Conversion Network ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-16 Zhiyuan Zhou, Xiaolei Zhou, Baoshen Guo, Shuai Wang, Tian He
Route prediction in instant delivery is still challenging due to the unique characteristics compared with conventional delivery services, such as strict deadlines, overlapped delivery time of multiple orders, and diverse individual preferences on delivery routes. Recently, development in the mobile Internet of Things (IoT) offers the opportunity to collect multi-sensor data with rich real-time information
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Robust Classification and 6D Pose Estimation by Sensor Dual Fusion of Image and Point Cloud Data ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-16 Yaming Xu, Yan Wang, Boliang Li
It is an important aspect to fully leverage complementary sensors of images and point clouds for objects classification and six-dimensional (6D) pose estimation tasks. Prior works extract objects category from a single sensor such as RGB camera or LiDAR, limiting their robustness in the event that a key sensor is severely blocked or fails. In this work, we present a robust objects classification and
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Retrieving Similar Trajectories from Cellular Data of Multiple Carriers at City Scale ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-16 Zhihao Shen, Wan Du, Xi Zhao, Jianhua Zou
Retrieving similar trajectories aims to search for the trajectories that are close to a query trajectory in spatio-temporal domain from a large trajectory dataset. This is critical for a variety of applications, like transportation planning and mobility analysis. Unlike previous studies that perform similar trajectory retrieval on fine-grained GPS data or single cellular carrier, we investigate the
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UETOPSIS: A Data-Driven Intelligence Approach to Security Decisions for Edge Computing in Smart Cities ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-14 Lijun Xiao, Dezhi Han, Kuan-Ching Li, Muhammad Khurram Khan
Despite considerable technological advances for smart cities, they still face problems such as instability of cloud server connection, insecurity during data transmission, and slight deficiencies in TCP/IP network architecture. To address such issues, we propose a data-driven intelligence approach to security decisions under Named Data Networking (NDN) architecture for edge computing, taking into consideration
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Detection and Identification of Non-cooperative UAV Using a COTS mmWave Radar ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-16 Yuan He, Jia Zhang, Rui Xi, Xin Na, Yimiao Sun, Beibei Li
Small Unmanned Aerial Vehicles (UAVs) are becoming potential threats to security-sensitive areas and personal privacy. A UAV can shoot photos at height, but how to detect such an uninvited intruder is an open problem. This article presents mmHawkeye, a passive approach for non-cooperative UAV detection and identification with a commercial off-the-shelf millimeter wave (mmWave) radar. mmHawkeye does
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Intelligent Networking for Energy Harvesting Powered IoT Systems ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-16 Wen Zhang, Chen Pan, Tao Liu, Jeff (Jun) Zhang, Mehdi Sookhak, Mimi Xie
As the next-generation battery substitute for IoT system, energy harvesting (EH) technology revolutionizes the IoT industry with environmental friendliness, ubiquitous accessibility, and sustainability, which enables various self-sustaining IoT applications. However, due to the weak and intermittent nature of EH power, the performance of EH-powered IoT systems as well as its collaborative routing mechanism
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Addressing Heterogeneity in Federated Learning with Client Selection via Submodular Optimization ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-16 Jinghui Zhang, Jiawei Wang, Yaning Li, Fa Xin, Fang Dong, Junzhou Luo, Zhihua Wu
Federated learning (FL) has been proposed as a privacy-preserving distributed learning paradigm, which differs from traditional distributed learning in two main aspects: the systems heterogeneity, meaning that clients participating in training have significant differences in systems performance including CPU frequency, dataset size, and transmission power, and the statistical heterogeneity, indicating
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Optimize the Age of Useful Information in Edge-assisted Energy-harvesting Sensor Networks ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-16 Tuo Shi, Zhipeng Cai, Jianzhong Li, Hong Gao
The energy-harvesting sensor network is a new network architecture to further prolong the lifetime of sensor networks and enhance the quality of IoT services. Due to the inherent problems of energy-harvesting sensor networks, it is really hard to collect fresh and useful sensory data. To solve the above problems, we investigate the data collection scheme in edge-assisted energy-harvesting sensor networks
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Exploiting Fine-grained Dimming with Improved LiFi Throughput ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-13 Xiao Zhang, James Mariani, Li Xiao, Matt W. Mutka
Optical wireless communication (OWC) shows great potential due to its broad spectrum and the exceptional intensity switching speed of LEDs. Under poor conditions, most OWC systems switch from complex and more error prone high-order modulation schemes to more robust On-Off Keying (OOK) modulation defined in the IEEE OWC standard. This paper presents LiFOD, a high-speed indoor OOK-based OWC system with
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An Experimental Study on BLE 5 Mesh Applied to Public Transportation ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-02-12 Anderson Biegelmeyer, Alexandre dos Santos Roque, Edison Pignaton de Freitas
Nowadays In-Vehicle Wireless Sensor Networks (IVWSN) are taking place in car manufacturers because it saves time in the assembling process, saves costs in harness and after-sales, and represents less weight on vehicles helping in diminishing fuel consumption. There is no definition for wireless solution technology for IVWSN, because each one has its own characteristics, and probably this is one of
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PAM-FOG Net: A Lightweight Weed Detection Model Deployed on Smart Weeding Robots ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-22 Jiahua Bao, Siyao Cheng, Jie Liu
Visual target detection based on deep learning with high computing power devices has been successful, but the performance in intelligent agriculture with edge devices has not been prominent. Specifically, the existing model architecture and optimization methods are not well-suited to low-power edge devices, the agricultural tasks such as weed detection require high accuracy, short inference latency
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AQMon: A Fine-grained Air Quality Monitoring System Based on UAV Images for Smart Cities ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-19 Shuangqing Xia, Tianzhang Xing, Chase Q. Wu, Guoqing Liu, Jiadi Yang, Kang Li
Air quality monitoring is important to the green development of smart cities. Several technical challenges exist for intelligent, high-precision monitoring, such as computing overhead, area division, and monitoring granularity. In this article, we propose a fine-grained air quality monitoring system based on visual inspection analysis embedded in unmanned aerial vehicle (UAV), referred to as AQMon
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Spray: A Spectrum-efficient and Agile Concurrent Backscatter System ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-19 Shanyue Wang, Yubo Yan, Yujie Chen, Panlong Yang, Xiang-Yang Li
Recent works have achieved considerable success in improving the concurrency of backscatter network. However, they do not optimize the balance between throughput and spectrum occupancy, both of which serve as pivotal parameters in concurrent transmissions. Moreover, these works also introduce complex components on tag thereby increasing both power consumption and deployment costs. In this article,
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Tensor-Based Viterbi Algorithms for Collaborative Cloud-Edge Cyber-Physical-Social Activity Prediction ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-17 Shunli Zhang, Laurence T. Yang, Yue Zhang, Zhixing Lu, Zongmin Cui
With the rapid development and application of smart city, Cyber-Physical-Social Systems (CPSS) as its superset is becoming increasingly important, and attracts extensive attentions. For satisfying the smart requirements of CPSS design, a cloud-edge collaborative CPSS framework is first proposed in this paper. Then Coupled-Hidden-Markov-Model (CHMM) and tensor algebra are used to improve existing activity
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Distributed Learning Mechanisms for Anomaly Detection in Privacy-Aware Energy Grid Management Systems ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-17 Jia-Hao Syu, Jerry Chun-Wei Lin, Gautam Srivastava
Smart grids have become an emerging topic due to net-zero emissions and the rapid development of artificial intelligence (AI) technology focused on achieving targeted energy distribution and maintaining operating reserves. In order to prevent cyber-physical attacks, issues related to the security and privacy of grid systems are receiving much attention from researchers. In this paper, privacy-aware
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Adaptive Offloading of Transformer Inference for Weak Edge Devices with Masked Autoencoders ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-13 Tao Liu, Peng Li, Yu Gu, Peng Liu, Hao Wang
Transformer is a popular machine learning model used by many intelligent applications in smart cities. However, it has high computational complexity and it would be hard to deploy it in weak-edge devices. This paper presents a novel two-round offloading scheme, called A-MOT, for efficient transformer inference. A-MOT only samples a small part of image data and sends it to edge servers, with negligible
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An Anonymous and Supervisory Cross-chain Privacy Protection Protocol for Zero-trust IoT Application ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-09 Yinghong Yang, Fenhua Bai, Zhuo Yu, Tao Shen, Yingli Liu, Bei Gong
Internet of things (IoT) development tends to reduce the reliance on centralized servers. The zero-trust distributed system combined with blockchain technology has become a hot topic in IoT research. However, distribution data storage services and different blockchain protocols make network interoperability and cross-platform more complex. Relay chain is a promising cross-chain technology that solves
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Editorial: Special Issue on Cyber-Physical Security and Zero Trust ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-10 Fangyu Li, Wenzhan Song, Xiaohua Xu
No abstract available.
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Reliability–Security Tradeoff Analysis in mmWave Ad Hoc–based CPS ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-09 Ying Ju, Mingjie Yang, Chinmay Chakraborty, Lei Liu, Qingqi Pei, Ming Xiao, Keping Yu
Cyber-physical systems (CPS) offer integrated resolutions for various applications by combining computer and physical components and enabling individual machines to work together for much more excellent benefits. The ad hoc–based CPS provides a promising architecture due to its decentralized nature and destructive-resistance. A growing number of information leakage events in CPSs and the following
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FedSuper: A Byzantine-Robust Federated Learning Under Supervision ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-10 Ping Zhao, Jin Jiang, Guanglin Zhang
Federated Learning (FL) is a machine learning setting where multiple worker devices collaboratively train a model under the orchestration of a central server, while keeping the training data local. However, owing to the lack of supervision on worker devices, FL is vulnerable to Byzantine attacks where the worker devices controlled by an adversary arbitrarily generate poisoned local models and send
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Train Once, Locate Anytime for Anyone: Adversarial Learning-based Wireless Localization ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-10 Danyang Li, Jingao Xu, Zheng Yang, Chengpei Tang
Among numerous indoor localization systems, WiFi fingerprint-based localization has been one of the most attractive solutions, which is known to be free of extra infrastructure and specialized hardware. To push forward this approach for wide deployment, three crucial goals on high deployment ubiquity, high localization accuracy, and low maintenance cost are desirable. However, due to severe challenges
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i-Sample: Augment Domain Adversarial Adaptation Models for WiFi-based HAR ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-10 Zhipeng Zhou, Feng Wang, Wei Gong
Recently, using deep learning to achieve WiFi-based human activity recognition (HAR) has drawn significant attention. While capable of achieving accurate identification in a single domain (i.e., training and testing in the same consistent WiFi environment), it would become extremely tough when WiFi environments change significantly. As such, domain adversarial neural networks-based approaches have
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An Eavesdropping System Based on Magnetic Side-Channel Signals Leaked by Speakers ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-10 Qianru Liao, Yongzhi Huang, Yandao Huang, Kaishun Wu
The use of speakers in electronic devices has become widespread, but the security risks associated with micro-speakers, such as earphones, are often overlooked. Many assume that soundproof barriers can prevent sound leakage and protect privacy. This article presents the prototype MagEar, an eavesdropping system that exploits magnetic side-channel signals leaked by a micro-speaker to restore intelligible
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RF-TESI: Radio Frequency Fingerprint-based Smartphone Identification under Temperature Variation ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-10 Xiaolin Gu, Wenjia Wu, Aibo Song, Ming Yang, Zhen Ling, Junzhou Luo
Radio frequency fingerprint identification (RFFI) is a promising technique for smartphone identification. However, we find that the temperature of the RF front end in smartphones can significantly impact the RF features, including the carrier frequency offset (CFO) and statistical RF features. The unstable RF features caused by temperature changes can negatively affect the performance of state-of-the-art
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Real-time Cyber-Physical Security Solution Leveraging an Integrated Learning-Based Approach ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-09 Di Wang, Fangyu Li, Kaibo Liu, Xi Zhang
Cyber-Physical Systems (CPS) has emerged as a paradigm that connects cyber and physical worlds, which provides unprecedented opportunities to realize intelligent applications such as smart home, smart cities, and smart manufacturing. However, CPS faces a great number of information security challenges (e.g., attacks) due to the integration of CPS as well as the human behaviors and interactions. Therefore
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Detect Insider Attacks in Industrial Cyber-physical Systems Using Multi-physical Features-based Fingerprinting ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-09 Zhen Hong, Lingling Lu, Dehua Zheng, Jiahui Suo, Peng Sun, Raheem Beyah, Zhenyu Wen
ICPS software and hardware suffer from low update frequency, making it easier for insiders to bypass external defenses and launch concealed destructive attacks. To address these concerns, we design a device fingerprinting method based on multi-physical features, augmenting current intrusion detection techniques in the ICPS environment. In this article, we use the sorting system as an example, demonstrating
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A Robust Learning Framework for Smart Grids in Defense Against False-Data Injection Attacks ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-09 Zhuoyi Miao, Jun Yu
With the rapid development of the application of smart grids in different sectors, security management has become a major concern due to cyber attack risks. Correctly and accurately estimating the real status of a smart grid under false-data injection attacks (FDIAs) is currently an emerging concern. In response to that concern, this work proposes a distributed robust learning framework for the inference
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Secure Data Sharing over Vehicular Networks Based on Multi-sharding Blockchain ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-09 Junqin Huang, Linghe Kong, Jingwei Wang, Guihai Chen, Jianhua Gao, Gang Huang, Muhammad Khurram Khan
Internet of Vehicles (IoV) has become an indispensable technology to bridge vehicles, persons, and infrastructures and is promising to make our cities smarter and more connected. It enables vehicles to exchange vehicular data (e.g., GPS, sensors, and brakes) with different entities nearby. However, sharing these vehicular data over the air raises concerns about identity privacy leakage. Besides, the
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CPS Attack Detection under Limited Local Information in Cyber Security: An Ensemble Multi-Node Multi-Class Classification Approach ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-09 Junyi Liu, Yifu Tang, Haimeng Zhao, Xieheng Wang, Fangyu Li, Jingyi Zhang
Cybersecurity breaches are common anomalies for distributed cyber-physical systems (CPS). However, the cyber security breach classification is still a difficult problem, even using cutting-edge artificial intelligence (AI) approaches. In this article, we study a multi-class classification problem in cyber security for attack detection. A challenging multi-node data-censoring case is considered. In
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VSSB-Raft: A Secure and Efficient Zero Trust Consensus Algorithm for Blockchain ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-09 Siben Tian, Fenhua Bai, Tao Shen, Chi Zhang, Bei Gong
To solve the problems of vote forgery and malicious election of candidate nodes in the Raft consensus algorithm, we combine zero trust with the Raft consensus algorithm and propose a secure and efficient consensus algorithm -Verifiable Secret Sharing Byzantine Fault Tolerance Raft Consensus Algorithm (VSSB-Raft). The VSSB-Raft consensus algorithm realizes zero trust through the supervisor node and
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DPIVE: A Regionalized Location Obfuscation Scheme with Personalized Privacy Levels ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-09 Shun Zhang, Pengfei Lan, Benfei Duan, Zhili Chen, Hong Zhong, Neal N. Xiong
The popularity of cyber-physical systems is fueling the rapid growth of location-based services. This poses the risk of location privacy disclosure. Effective privacy preservation is foremost for various mobile applications. Recently, geo-indistinguishability and expected inference error are proposed for limiting location leakages. In this article, we argue that personalization means regionalization
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ECRLoRa: LoRa Packet Recovery under Low SNR via Edge–Cloud Collaboration ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-09 Luoyu Mei, Zhimeng Yin, Shuai Wang, Xiaolei Zhou, Taiwei Ling, Tian He
Low-Power Wide-Area Networks (LPWANs), extensively utilized for connecting billions of IoT devices, encounter wireless interference challenges in unlicensed frequency bands. Cutting-edge research suggests employing Received Signal Strength Indication (RSSI) sequences for error detection to mitigate interference-related issues. Nevertheless, the effectiveness of this method significantly declines under
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Efficient Bike-sharing Repositioning with Cooperative Multi-Agent Deep Reinforcement Learning ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2024-01-03 Yao Jing, Bin Guo, Yan Liu, Daqing Zhang, Djamal Zeghlache, Zhiwen Yu
As an emerging mobility-on-demand service, bike-sharing system (BSS) has spread all over the world by providing a flexible, cost-efficient, and environment-friendly transportation mode for citizens. Demand-supply unbalance is one of the main challenges in BSS because of the inefficiency of the existing bike repositioning strategy, which reallocates bikes according to a pre-defined periodic schedule
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ViST: A Ubiquitous Model with Multimodal Fusion for Crop Growth Prediction ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2023-12-07 Junsheng Li, Ling Wang, Jie Liu, Jinshan Tang
Crop growth prediction can help agricultural workers to make accurate and reasonable decisions on farming activities. Existing crop growth prediction models focus on one crop and train a single model for each crop. In this article, we develop a ubiquitous growth prediction model for multiple crops, aiming at training a single model for multiple crops. A ubiquitous vision and sensor transformer (ViST)
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Taming Irregular Cardiac Signals for Biometric Identification ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2023-12-07 Weizheng Wang, Qing Wang, Marco Zuniga
Cardiac patterns are being used to provide hard-to-forge biometric signatures in identification applications. However, this performance is obtained under controlled scenarios where cardiac signals maintain a relatively uniform pattern, facilitating the identification process. In this work, we analyze cardiac signals collected in more realistic (uncontrolled) scenarios and show that their high signal
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Multi-User Mobile Augmented Reality with ID-Aware Visual Interaction ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2023-12-07 Xinjun Cai, Zheng Yang, Liang Dong, Qiang Ma, Xin Miao, Zhuo Liu
Most existing multi-user Augmented Reality (AR) systems only support multiple co-located users to view a common set of virtual objects but lack the ability to enable each user to directly interact with other users appearing in his/her view. Such multi-user AR systems should be able to detect the human keypoints and estimate device poses (for identifying different users) in the meantime. However, due
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AdaMEC: Towards a Context-adaptive and Dynamically Combinable DNN Deployment Framework for Mobile Edge Computing ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2023-12-07 Bowen Pang, Sicong Liu, Hongli Wang, Bin Guo, Yuzhan Wang, Hao Wang, Zhenli Sheng, Zhongyi Wang, Zhiwen Yu
With the rapid development of deep learning, recent research on intelligent and interactive mobile applications (e.g., health monitoring, speech recognition) has attracted extensive attention. And these applications necessitate the mobile edge computing scheme, i.e., offloading partial computation from mobile devices to edge devices for inference acceleration and transmission load reduction. The current
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A Model Personalization-based Federated Learning Approach for Heterogeneous Participants with Variability in the Dataset ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2023-12-07 Rahul Mishra, Hari Prabhat Gupta
Federated learning is an emerging paradigm that provides privacy-preserving collaboration among multiple participants for model training without sharing private data. The participants with heterogeneous devices and networking resources decelerate the training and aggregation. The dataset of the participant also possesses a high level of variability, which means the characteristics of the dataset change
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Communication-Topology-preserving Motion Planning: Enabling Static Routing in UAV Networks ACM Trans. Sens. Netw. (IF 4.1) Pub Date : 2023-12-07 Ziyao Huang, Weiwei Wu, Chenchen Fu, Xiang Liu, Feng Shan, Jianping Wang, Xueyong Xu
Unmanned Aerial Vehicle (UAV) swarm offers extended coverage and is a vital solution for many applications. A key issue in UAV swarm control is to cover all targets while maintaining connectivity among UAVs, referred to as a multi-target coverage problem. With existing dynamic routing protocols, the flying ad hoc network suffers outdated and incorrect route information due to frequent topology changes