Title: A routing algorithm based on simulated annealing algorithm for maximising wireless sensor networks lifetime with a sink node

Authors: Hui Wang; Kangshun Li; Witold Pedrycz

Addresses: Shenzhen Institute of Information Technology, Shen Zhen, 518029, China ' College of Mathematics and Informatics, South China Agriculture University, Guangzhou, China; Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China ' Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

Abstract: Energy saving becomes a central issue in the design of wireless sensor network routing algorithms. In the wireless sensor networks (WSNs), when intra-network communication is ensured, the lifetime of node can be extended by reducing data transmission or data volume as much as possible. However, the problem is that energy of the nodes around the sink node becomes exhausted quickly due to excessive communication overhead. To handle this problem, in this study, we propose a routing algorithm based on the sink node path optimisation. The study uses the energy consumption model as a constraint, transforms the time optimisation problem into an optimisation model, optimises the sink node path with the aid of simulated annealing (SA) algorithm, and uses data fusion to reduce the intra-network redundant data in the time domain. The proposed algorithm innovatively self-adjusts the path of sink node that is optimised by SA using new fitness function. Comprehensive simulation results show that the proposed algorithm can reduce the node energy consumption of waiting of sink node at the address of sink node, balance the network load and improve survival time of WSNs by 30% in comparison with results produced with the state-of-the art algorithms REAC-IN and DALMDT.

Keywords: routing algorithm; sink node; wireless sensor network; WSNs; simulated annealing; SA; optimal path.

DOI: 10.1504/IJBIC.2020.108596

International Journal of Bio-Inspired Computation, 2020 Vol.15 No.4, pp.264 - 275

Received: 20 Aug 2019
Accepted: 31 Jan 2020

Published online: 15 Jul 2020 *

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