Title: A novel genetic algorithm with 2D CDF 9/7 lifting discrete wavelet transform for total target coverage in WSNs deployment

Authors: T. Ganesan; Pothuraju Rajarajeswari

Addresses: Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India ' Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India

Abstract: In recent days, environmental monitoring has been achieved by wireless sensor networks. The node placement problem is playing a significant role in positioning and infrastructure for gathering information from engineering and environment fields. When the number of sensors is limited to cover the maximum area or total target coverage (TTC) imposes a real challenge in sensor placement in a different field because of complicated weather condition, the quality of maximum coverage is achieved by deploying sensors in an optimum position such that it covers the entire field. In this paper, a novel genetic algorithm with a 2D lifting-based discrete wavelet transform is proposed for finding the optimal location for each sensor with connectivity. The enhanced genetic algorithm generates the population matrix to identify each sensor position whereas, the quality of maximum coverage or monitoring and connectivity of every sensor is achieved by a 2D lifting scheme based on bi-orthogonal Cohen-Daubechies-Feauveau CDF 9/7 wavelet transform for adjusting sensor position optimally. The theoretical analysis and mathematical model have been carried out to the simulation results and are compared with the existing algorithm in terms of maximum coverage, connectivity, the total number of sensors and optimal position.

Keywords: wireless sensor network; WSN; sensor deployment; lifting scheme; genetic algorithm; wavelet transform; total target coverage; TTC.

DOI: 10.1504/IJCNDS.2021.115571

International Journal of Communication Networks and Distributed Systems, 2021 Vol.26 No.4, pp.464 - 483

Received: 17 Jun 2020
Accepted: 31 Jul 2020
Published online: 10 Jun 2021 *

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