Minimization of Energy Consumption for Routing in High‐Density Wireless Sensor Networks Based on Adaptive Elite Ant Colony Optimization

Abstract

High-density wireless sensor networks (HDWSNs) are usually deployed randomly, and each node of the network collects data from complex environments. Because the energy of sensor nodes is powered by batteries, it is basically impossible to replace batteries or charge in the complex surroundings. In this paper, a QoS routing energy consumption model is designed, and an improved adaptive elite ant colony optimization (AEACO) is proposed to reduce HDWSN routing energy consumption. This algorithm uses the adaptive operator and the elite operator to accelerate the convergence speed. So, as to validate the efficiency of AEACO, the AEACO is contrast with particle swarm optimization (PSO) and genetic algorithm (GA). The simulation outcomes show that the convergence speed of AEACO is sooner than PSO and GA. Moreover, the energy consumption of HDWSNs using AEACO is reduced by 30.7% compared with GA and 22.5% compared with PSO. Therefore, AEACO can successfully decrease energy consumption of the whole HDWSNs.

Path Planning of Storage and Logistics Mobile Robot Based on ACA-E Algorithm

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Node Injection Control Logic Design for Intelligent Home System Based on Wireless Sensor Network

“…Engineering construction personnel can even complete their work in a few hours. Customers can flexibly configure the system according to their personal needs to adapt to their own lifestyle; intelligent household system has a very extensive application [17,18].…”

[Retracted] Research on Wireless Sensor Network Positioning Based on Genetic Algorithm

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