Title: A channel allocation method based on dual-population differential evolution in wireless sensor networks

Authors: Yu Cao; Wei Wei; Huiting Pei

Addresses: School of Mathematics and Information Technology, Jiangsu Second Normal University, Nanjing, 210013, China ' School of Computing, Nanjing University of Science and Technology ZIJIN College, Nanjing, 210000, China ' School of Mathematics and Information Technology, Jiangsu Second Normal University, Nanjing, 210013, China

Abstract: To solve the problem of low-efficiency channel allocation due to the high density of nodes in traditional wireless sensor networks (WSNs), a channel allocation method based on dual-population differential evolution is proposed. In our paper, the crossover and mutation operations of the differential evolution algorithm are analysed; multichannel mutation crossover optimisation is proposed by combining a differential evolution algorithm with a dual-population search method. Anti-interference optimisation is carried out for different network levels with a certain capacity, considering the time slot parameter interference degree, link capacity, and multichannel cross-layer analysis. According to the multichannel mutation cross-optimisation method, the constraint conditions are determined, the channel is divided, and a linear membership function is constructed to perform channel allocation. The experimental results show that when the mutation parameter and crossover probability of two-population differential evolution are 0.1 and 0.6, respectively, the channel allocation efficiency of the proposed method is the highest, and the shortest channel allocation time is 3.4 s.

Keywords: differential evolution algorithm; double population; cross-layer algorithm; interference degree; network channel allocation; WSNs; wireless sensor networks; anti-interference optimisation.

DOI: 10.1504/IJSNET.2021.115443

International Journal of Sensor Networks, 2021 Vol.36 No.1, pp.50 - 58

Received: 22 Jan 2021
Accepted: 22 Jan 2021

Published online: 25 May 2021 *

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