Optimization of Low Power Wireless Sensor Network Lifetime Using Improved Clone Elite Monkey Algorithm

Abstract

Recent advances in efficient software algorithms, electronics and networking technologies have promoted the development of low computational complexity, low-cost, low storage, and intelligent tiny nodes. Low power wireless sensor networks (LPWSNs) are composed of some sensing units with limited communications as well as sensing capabilities. LPWSNs are approaches in a good deal of domains such as traffic control, automated assistance for the elderly monitors and so on. Recently network lifetime optimization has been receiving a lot of attention for wide applications of LPWSNs. Achieving a longer network lifetime under the restricted power source has a large calculation difficulty, which can be regard as an NP-hard problem. An improved clone elite monkey algorithm (ICEMA) to determine the network lifetime optimization in LPWSNs is given. In this paper, in order to achieve maximum network lifetime with full coverage, we first build a system model to calculate a better network lifetime. It is designed to increment the lifetime of the nodes for LPWSNs. The ICEMA has many advantages by combining the clone strategy as well as elite strategy. The simulations verify the robust and efficiency of ICEMA when compared with strategies based on evolutionary algorithm (EA), particle swarm algorithm (PSO) and artificial fish swarm algorithm (AFSA) under a LPWSNs conditions. The outcomes demonstrate that the proposed ICEMA can achieve a longer network lifetime than EA, PSO and AFSA while taking the same computational complexity.