Title: A quantum-inspired evolutionary clustering algorithm for the lifetime problem of wireless sensor network

Authors: Chun-Wei Tsai; Chun-Ting Kang; Kai-Cheng Hu; Ming-Chao Chiang

Addresses: Department of Computer Science and Engineering, National Chung Hsing University, Taichung 40227, Taiwan ' Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan ' Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan ' Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung 80424, Taiwan

Abstract: One of the solutions to the lifetime problem of a wireless sensor network (WSN) is to select a sensor as the cluster head (CH) to reduce the transmission cost of the other sensors in a cluster. However, the high computation load will quickly run out of its energy. The most well-known method for selecting the CHs of a WSN is the so-called low energy adaptive clustering hierarchy (LEACH), but it is far from optimal in terms of the energy consumed. On the other hand, some recent studies showed that the quantum-inspired evolutionary algorithm (QEA) can provide a better result than rule-based and metaheuristic algorithms. This paper is, therefore, aimed at applying QEA to the lifetime problem of a WSN. Simulation results show that the proposed algorithm can provide a better result than LEACH and genetic algorithm in terms of the overall energy consumed, especially for complex and large lifetime problems.

Keywords: wireless sensor networks; WSNs; quantum inspired evolutionary algorithms; QEA; low energy adaptive clustering hierarchy; LEACH; quantum-inspired evolutionary clustering algorithms; QECA; internet of things; IoT; network lifetime; cluster head selection; energy consumption; simulation; genetic algorithms.

DOI: 10.1504/IJITST.2016.083000

International Journal of Internet Technology and Secured Transactions, 2016 Vol.6 No.4, pp.259 - 290

Received: 06 Apr 2016
Accepted: 27 Oct 2016
Published online: 17 Mar 2017 *

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