A Novel Coverage Optimization Strategy Based on Grey Wolf Algorithm Optimized by Simulated Annealing for Wireless Sensor Networks

Abstract

The coverage optimization problem of wireless sensor network has become one of the hot topics in the current field. Through the research on the problem of coverage optimization, the coverage of the network can be improved, the distribution redundancy of the sensor nodes can be reduced, the energy consumption can be reduced, and the network life cycle can be prolonged, thereby ensuring the stability of the entire network. In this paper, a novel grey wolf algorithm optimized by simulated annealing is proposed according to the problem that the sensor nodes have high aggregation degree and low coverage rate when they are deployed randomly. Firstly, the mathematical model of the coverage optimization of wireless sensor networks is established. Secondly, in the process of grey wolf optimization algorithm, the simulated annealing algorithm is embedded into the grey wolf after the siege behavior ends and before the grey wolf is updated to enhance the global optimization ability of the grey wolf algorithm and at the same time improve the convergence rate of the grey wolf algorithm. Simulation experiments show that the improved grey wolf algorithm optimized by simulated annealing is applied to the coverage optimization of wireless sensor networks. It has better effect than particle swarm optimization algorithm and standard grey wolf optimization algorithm, has faster optimization speed, improves the coverage of the network, reduces the energy consumption of the nodes, and prolongs the network life cycle.

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

“…Zhang et al [184] proposed a coverage optimization method that adopts the grey wolf technique for wireless sensor networks. After establishing the coverage optimization and ending the siege behavior, the simulated annealing procedure is included in the grey wolf.…”

Coverage optimization in wireless sensor networks with a modified arithmetic optimization algorithm

“…WSN is an NP-hard problem that has been solved by many scholars using swarm intelligence optimization algorithms. Y Zhang et al implemented a modified Gray Wolf algorithm using simulated annealing, which was proposed in based on the problem of high aggregation and low coverage when sensor nodes are randomly deployed 5 . An optimal node coverage solution for unbalanced WSNs distribution during random deployment based on a hybrid-strategy-improved butterfly optimization algorithm is proposed in Reference 6 .…”

Coverage Optimization of Wireless Sensor Networks Using Combinations of PSO and Chaos Optimization

“…Deepa et al [10] proposed to use levy flight to enhance WOA for guaranteeing the network coverage. Other intelligent optimization algorithms, including the bee algorithm [11,12], the weed algorithm [13], the wolf pack algorithm [14], the glowworm swarm optimization [15,16], the social spider optimization (SSO) algorithm [17], the multi-objective immune co-evolutionary algorithm (MOICEA) [18], the simulated annealing (SA) [19], the ant colony optimization (ACO) [20], the combined optimization using chaotic flower pollination and cuckoo algorithms [21], the biogeography-based optimization [22], the grey wolf optimizer [23,24] and the termite flies optimization (TFO) algorithm [25]. Other works also include linear programming optimization [26], the barrier coverage algorithm [27], and the coverage and connectivity problem solving of large sensor networks [28].…”