Research Article
WSN Coverage Optimization Based on Two-Stage PSO
@INPROCEEDINGS{10.1007/978-3-030-67537-0_2, author={Wei Qi and Huiqun Yu and Guisheng Fan and Liang Chen and Xinxiu Wen}, title={WSN Coverage Optimization Based on Two-Stage PSO}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 16th EAI International Conference, CollaborateCom 2020, Shanghai, China, October 16--18, 2020, Proceedings, Part I}, proceedings_a={COLLABORATECOM}, year={2021}, month={1}, keywords={Hybrid WSN Multi-objective optimization Two-stage mechanism Area coverage Energy consumption balance}, doi={10.1007/978-3-030-67537-0_2} }- Wei Qi
Huiqun Yu
Guisheng Fan
Liang Chen
Xinxiu Wen
Year: 2021
WSN Coverage Optimization Based on Two-Stage PSO
COLLABORATECOM
Springer
DOI: 10.1007/978-3-030-67537-0_2
Abstract
Wireless Sensor Networks (WSN) coverage perception is an important basis for communication between the cyber world and the physical world in Cyber-Physical Systems (CPS). To address the coverage redundancy, hole caused by initial random deployment and the energy constraint in redeployment, this paper proposes a multi-objective two-stage particle swarm optimization algorithm (MTPSO) based on coverage rate and moving distance deviation to improve coverage efficiency. This algorithm establishes a multi-objective optimization model for above problems, and determines the candidate deployment scheme by reducing its local convergence probability through improved inertia weight, and then introduces virtual force mechanism to adjust the relative position between nodes. This paper mainly analyzes the influence of different initial deployment category and mobile nodes proportion on multi-objective optimization performance, and gives the corresponding algorithm implement. Simulation experiments show that compared with MVFA, SPSO and OPSO algorithms, MTPSO algorithm has a better redeployment coverage performance, which fully demonstrates its effectiveness.
