WSN Coverage Optimization Based on Two-Stage PSO

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.

A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling

“…Tree (MST) routing protocol was applied to reduce the network's energy consumption, thereby extending its lifespan. Qi et al in [108] studied the WSN redeployment problem to increase the network coverage and reduce the moving distance of mobile sensors. The proposed approach is based on the PSO algorithm and adapts a new nonlinear decreasing inertia weight as an improvement to avoid falling in local optima.…”