Academic Editor
Author
Contributions by role
Author 1
Reviewer 13
Editor 31
Membership
Jinchao Chen
School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
Summary
Dr. Jinchao Chen is an Associate Professor in School of Computer Science at Northwestern Polytechnical University, Xi’an, China. He has received his Ph.D. degree in Computer Science from the same institution in 2016. He focuses on the multi-processor scheduling, embedded and real-time systems, simulation and verification, decision-making and intelligent control of unmanned aerial vehicles, human-computer interaction systems. He has over 50 papers and 4 ESI highly-cited papers published in international conferences and journals (e.g., IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Industrial Electronics, IEEE Transactions on Cybernetics, IEEE Real-Time Systems Symposium). He is the Editor-in-Chief of ASP Transactions on Computers and ASP Transactions on Computers, and the Academic Editor of International Journal of Aerospace Engineering. He is a TCP member of many conferences and regular reviewer of IEEE Transactions on Industrial Informatics, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Transportation Electrification, IEEE Transactions on Vehicular Technology, and Concurrency and Computation Practice and Experience. He is a member of IEEE and CCF.
Edited Journals
IECE Contributions

Free Access | Research Article | Feature Paper | 17 April 2023 | Cited: 11
Adaptive Binary Particle Swarm Optimization for WSN Node Optimal Deployment Algorithm
IECE Transactions on Internet of Things | Volume 1, Issue 1: 1-8, 2023 | DOI:10.62762/TIOT.2023.564457
Abstract
In order to optimize the deployment of wireless sensor network nodes, and avoid network energy consumption increase due to node redundancy and uneven coverage, the multi-objective mathematical optimization problem of area coverage is transformed into a function problem. Aiming at network coverage rate, node dormancy rate and network coverage uniformity, the idea of genetic algorithm mutation is introduced based on the discrete binary particle swarm optimization and the global optimal speed is mutated to avoid the algorithm falling into the local optimal solution. In order to further improve the optimization ability of the algorithm, the adaptive learning factor and inertia weight are introdu... More >

Graphical Abstract
Adaptive Binary Particle Swarm Optimization for WSN Node Optimal Deployment Algorithm
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