Minimization of Energy Consumption for Routing in High-Density Wireless Sensor Networks Based on Adaptive Clone Elite Genetic Algorithm

Abstract

High-density wireless sensor networks (HDWSNs) perceive environmental information through sensor nodes, connects data to the network, and is widely used in environmental detection, intelligent control, and military fields. Since sensor nodes are usually randomly distributed in the environment and have limited energy, ensuring the energy supply of these nodes is still a difficult problem to solve at present. Therefore, how to effectively reduce network energy consumption, improve algorithm efficiency, and extend network life time is the main problem to be solved by QoS routing in HDWSNs. This paper proposes an adaptive clone elite genetic algorithm (ACEGA) to reduce the energy consumption of HDWSNs routing. The algorithm uses clone operators and elite operators to speed up the convergence speed, and uses adaptive operators to enhance the global search capability of the algorithm. To verify the effectiveness of ACEGA, we compare ACEGA with particle swarm optimization (PSO) and simulated annealing (GA). Simulation results demonstrate that the execution performance of the ACEGA outperforms SA and PSO. In addition, the system energy consumption of HDWSNs using ACEGA is also lower than that of SA and PSO. Therefore, the algorithm we proposed effectively reduces the routing consumption of the entire HDWSNs system.