Article
Computer Science, Information Systems
Pengju Si, Shuaishuai Wang, Lei Shu, Rui Ning, Zhumu Fu
Summary: The paper introduces a deterministic deployment algorithm for finding the minimum number of sensors required to effectively construct target-barrier coverage in a surveillance region, with extensive experiments showing significant results.
IEEE SYSTEMS JOURNAL
(2021)
Article
Engineering, Electrical & Electronic
Zeren Li, Lulu Zhang, Yunze Cai, Hideya Ochiai
Summary: This paper proposes an information-quality-based sensor selection method with estimation feedback for maneuvering target tracking in uncertain wireless sensor networks (WSNs). The GPB1-UIF algorithm with estimation feedback is presented and an information-quality metric framework is proposed for sensor selection. The proposed approach outperforms previous methods in simulation results.
IEEE SENSORS JOURNAL
(2022)
Article
Computer Science, Information Systems
Ye Yuan, Wei Yi, Wan Choi
Summary: In this article, a cost-aware dynamic sensor scheduling (CADSS) framework is proposed for wireless sensor networks (WSNs) with multiple tasks. CADSS provides a comprehensive task utility evaluation methodology for self-organized WSNs by minimizing system cost and maintaining desired task qualities. The effectiveness of CADSS is verified by applying it to a multitarget tracking (MTT) application.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Zenghong Huang, Damian Marelli, Yong Xu, Minyue Fu
Summary: The proposed distributed tracking method is based on maximum likelihood Kalman filter (MLKF) for linear dynamics and non-linear measurements acquired by multiple sensors. By utilizing a fully distributed optimization method, the method computes the ML estimate and obtains the required Hessian matrix as a byproduct of the optimization procedure. Numerical simulation results show that the distributed MLKF (DMLKF) outperforms other available distributed tracking methods in terms of tracking accuracy and asymptotically approximates the optimal Bayesian tracking solution as the number of sensors and inter-node information fusion iterations increase.
IEEE SENSORS JOURNAL
(2021)
Article
Automation & Control Systems
Jiwei Zhang, Md Zakirul Alam Bhuiyan, Xu Yang, Amit Kumar Singh, D. Frank Hsu, Entao Luo
Summary: This article proposes a framework called DRLTrack for mobile target tracking in edge-assisted IoT platform using collaborative deep reinforcement learning. DRLTrack aims to achieve high quality tracking and resource-efficient network performance. By employing a large number of IoT devices, DRLTrack is able to maintain an area around the target and demonstrate reliable performance even under cyberattacks.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Automation & Control Systems
Fen Liu, Chengpeng Jiang, Wendong Xiao
Summary: A novel multistep prediction-based adaptive dynamic programming approach is proposed for collaborative target tracking in energy harvesting wireless sensor networks, showing superior performance compared to other sensor scheduling methods under limited sensor energy harvesting capabilities.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Information Systems
Anastassia Gharib, Mohamed Ibnkahla
Summary: This article proposes a node embedding with security resource allocation (NESRA) clustering algorithm for mobile ICWSNs, allocating security resources to sensor nodes in three steps to achieve efficient ICWSN operation, data security, and timely data access to mobile users.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Zizheng Dou, Zheng Yao, Mingquan Lu
Summary: This article presents an asynchronous collaborative localization system (ACLS) for large-capacity sensor networks (LCSNs). ACLS uses a hierarchical architecture and specific protocols to achieve high-rate and high-precision ranging and localization without preinstalled infrastructures.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Juan Feng, Hongwei Zhao
Summary: The paper proposes a dynamic chain-based collaboration (DCBC) approach for efficient target tracking and data gathering in WSNs, which forms a dynamic tracking chain around the target and adapts to target location changes without reconstructing the structure. The nodes in the tracking chain send their own data in turn, improving energy utilization and data transmission efficiency. Experimental results show that DCBC reduces and balances network energy consumption compared to existing approaches, prolonging network lifetime.
IEEE SENSORS JOURNAL
(2021)
Article
Engineering, Electrical & Electronic
Long Cheng, Linghe Kong, Yu Gu, Jianwei Niu, Ting Zhu, Cong Liu, Shahid Mumtaz, Tian He
Summary: In this work, a collision-free convergecast protocol named iCore is proposed for low-duty-cycle WSNs to minimize data collisions and improve channel utilization. iCore utilizes dynamic forwarding technique and optimization algorithms to reduce end-to-end latency and maintain high delivery ratio and energy efficiency in various convergecast scenarios.
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Liaoyuan Huan, Baoping Tang, Chunhua Zhao
Summary: To address the problem of limited storage and computing resources in wireless sensor networks (WSNs), a global composite compression method for deep neural networks (DNN) is proposed. The method removes redundant parameters and kernels through coarse and fine-grained composite pruning, and further reduces model storage and improves inference speed through quantification of output features and weight parameters. Experimental results show that the proposed method achieves a compression rate of approximately 20x, maintains high diagnostic accuracy, reduces power consumption, and improves system time, indicating advanced performance in DNN model compression, node power consumption, and data transmission delay.
IEEE SENSORS JOURNAL
(2023)
Article
Computer Science, Information Systems
Arun Kumar Sangaiah, Ali Shokouhi Rostami, Ali Asghar Rahmani Hosseinabadi, Morteza Babazadeh Shareh, Amir Javadpour, Shirin Hatami Bargh, Mohammad Mehedi Hassan
Summary: Monitoring the concentration of workers and increasing productivity in large factories is crucial. Workforce can be monitored using wireless sensor networks, with optimal energy consumption being essential. Various routing protocols are designed to ensure efficient energy consumption while tracking targets. The proposed energy-efficient routing algorithm GRTT utilizes topological information of sensor nodes for target tracking and coverage applications, showing better results in energy and power consumption compared to other methods.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Engineering, Electrical & Electronic
Qiaoyun Zhang, Chih-Yung Chang, Zaixiu Dong, Diptendu Sinha Roy
Summary: This paper proposes a target coverage mechanism called TCSAR in wireless rechargeable sensor networks. It applies the Probabilistic Sensing Model (PSM) and adjustable sensing radius of sensors to maximize surveillance quality while ensuring network lifetime. The mechanism evaluates the surveillance contribution of each sensor and schedules the one with the highest contribution to the bottleneck points of interest. It further improves surveillance quality by adjusting the sensing radius of sensors from space to time dimension.
IEEE SENSORS JOURNAL
(2022)
Review
Engineering, Electrical & Electronic
Lismer Andres Caceres Najarro, Iickho Song, Kiseon Kim
Summary: This article discusses the importance of localization in wireless sensor networks and the different techniques available with varying accuracies, complexities, and applicabilities. It also addresses the fundamental limitations that restrict localization accuracy and presents recent solutions and remaining challenges.
IEEE SENSORS JOURNAL
(2022)
Article
Computer Science, Information Systems
Bingkun Yao, Hong Gao, Yang Zhang, Jinbao Wang, Jianzhong Li
Summary: This study proposes a more practical energy model and formally defines the problem of Maximum Age of Information (AoI) minimization for target monitoring in battery-free wireless sensor networks. A two-stage algorithm is proposed to solve this problem, and extensive simulations and real-world experiments verify the high performance of the algorithm and energy model.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Article
Computer Science, Information Systems
Xuanping Li, Xue Wang, Yixiang Dai
JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY
(2018)
Article
Computer Science, Information Systems
Peng Dai, Xue Wang, Weihang Zhang
MULTIMEDIA TOOLS AND APPLICATIONS
(2018)
Article
Computer Science, Information Systems
Peng Dai, Xue Wang, Weihang Zhang, Pengbo Zhang, Wei You
MULTIMEDIA TOOLS AND APPLICATIONS
(2018)
Article
Computer Science, Information Systems
Yixiang Dai, Xue Wang, Pengbo Zhang, Weihang Zhang, Junfeng Chen
MULTIMEDIA TOOLS AND APPLICATIONS
(2018)
Article
Computer Science, Information Systems
Lin Zhao, Xue Wang
Article
Engineering, Biomedical
Pengbo Zhang, Xue Wang, Weihang Zhang, Junfeng Chen
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2019)
Article
Computer Science, Information Systems
Peng Dai, Xue Wang, Weihang Zhang, Junfeng Chen
IEEE TRANSACTIONS ON MULTIMEDIA
(2019)
Article
Engineering, Biomedical
Pengbo Zhang, Xue Wang, Junfeng Chen, Wei You, Weihang Zhang
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2019)
Article
Engineering, Electrical & Electronic
Yanchi Liu, Xue Wang, Wei You
IEEE TRANSACTIONS ON SMART GRID
(2019)
Article
Computer Science, Information Systems
Wei You, Xue Wang, Weihang Zhang, Zhenfeng Qiang
Summary: This paper proposes a multi-level kinematic constraints method to construct multiple skeleton features, which can effectively utilize valid information and enhance the performance of skeleton-based action recognition methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Junfeng Chen, Xue Wang, Xiaotian Zhang, Weihang Zhang
Summary: Non-intrusive load monitoring (NILM) is a promising technology that can monitor appliance operating state and energy consumption without sub-meters. This paper proposes a method using temporal and spectral load signatures for appliance recognition in NILM. Deep learning techniques and affinity propagation clustering strategy are used to extract features and mitigate the negative impact of multi-state loads. Experimental results show that the proposed method outperforms existing methods in recognition accuracy.
IEEE TRANSACTIONS ON SMART GRID
(2022)
Article
Automation & Control Systems
Haiming Yao, Wenyong Yu, Xue Wang
Summary: Recent advances in industrial inspection of textured surfaces have made efficient and flexible manufacturing systems possible. This paper proposes an unsupervised feature memory rearrangement network (FMR-Net) that accurately detects various textural defects simultaneously. The network utilizes background reconstruction and artificial synthetic defects to recognize anomalies, achieving state-of-the-art inspection accuracy and showing great potential for use in smart industries.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Information Systems
Wei You, Xue Wang
Summary: A novel self-supervised learning method is proposed in this study, which introduces the view enhanced jigsaw puzzle (VEJP) pretext task and the view pooling encoder (VPE) to improve feature learning. Experimental results show that moderately difficult pretext tasks can effectively enhance feature learning.
Article
Computer Science, Artificial Intelligence
Weihang Zhang, Xue Wang, Junfeng Chen, Wei You
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2020)
Article
Computer Science, Artificial Intelligence
Weihang Zhang, Xue Wang, Wei You, Junfeng Chen, Peng Dai, Pengbo Zhang
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2020)
