Simulated annealing-based reprogramming scheme of wireless sensor nodes

Duan, Zhangling; Wei, Xing; Han, Jiuqiang; Lü, Yang; Shi, Lei

doi:10.1007/s11276-019-02156-7

Cited by 1 publication

(1 citation statement)

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“…If the fitness function has a local extreme value, once it falls into the local optimum in the process of optimization, it cannot escape. e idea of simulated annealing can improve or eliminate this problem [31,32]. erefore, a grey wolf optimization algorithm optimized by simulated annealing (SA-GWO) is proposed in this paper.…”

Section: Grey Wolf Algorithm Optimized By Simulatedmentioning

confidence: 99%

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

Zhang

Cao

Yue

et al. 2021

Computational Intelligence and Neuroscience

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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.

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Section: Grey Wolf Algorithm Optimized By Simulatedmentioning

confidence: 99%