A novel genetic algorithm with CDF5/3 filter-based lifting scheme for optimal sensor placement. (18th March 2021)

• Record Type: Journal Article
• Authors: Ganesan, T.; Rajarajeswari, Pothuraju; Nayak, Soumya Ranjan; Bhatia, Amandeep Singh
• Is Part Of: International journal of innovative computing and applications. Volume 12:Number 2/3(2021)
• Page Start: 67
• Page End: 76
• Publication Date: 2021-03-18
• Subjects: wireless sensor network -- sensor deployment -- genetic algorithm -- lifting scheme -- target coverage; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

The generic algorithm has been receiving significant attention due to the node placement problem in the field of sensor application in terms of machine learning. Sensor deployment is able to provide maximum coverage and maximum connectivity with less energy consumption to sustain the network lifetime. The maximum quality coverage problem has been solved successfully by an evolutionary algorithm while placing nodes in optimal position. In evolutionary algorithms, genetic algorithm (GA) plays an important technique for deploying the sensor in the form of population matrix. However, the existing techniques are unable to place sensor position perfectly. In this paper, a novel genetic algorithm with second generation wavelet transform (SGWT) is proposed for identifying optimal potential position for node placement. In order to improve the quality of population matrix, bi-orthogonal Cohen-Daubechies-Feauveau wavelet (CDF 5/3) has been employed. The proposed method is performed primarily to generate sensor position with different populations. Subsequently, it can extend to CDF5/3 filter-based lifting scheme to adjust the sensor position. The proposed method has been compared with random deployment, genetic algorithm and GA with CDF5/3 wavelets in terms of target to cover by the sensor. The result of the proposed method affirms better optimisation as compared to the state-of-art techniques.