Target coverage algorithms have considerable attention for monitoring the target point by dividing sensor nodes into cover groups, with each sensor cover group containing the target points. When the number of sensors is restricted, optimal sensor node placement becomes a key task. By placing sensors in the ideal position, the quality of maximum target coverage and node connectivity can be increased. In this paper, a novel genetic algorithm based on the 2-D discrete Daubechies 4 (db4) lifting wavelet transform is proposed for determining the optimal sensor position. Initially, the genetic algorithm identifies the population-based sensor location and 2-D discrete db4 lifting adjusts the sensor location into an optimal position where each sensor can cover a maximum number of targets that are connected to another sensor. To demonstrate that the suggested model outperforms the existing method, A series of experiments are carried out using various situations to achieve maximum target point coverage, node interconnectivity, and network lifetime with a limited number of sensor nodes.

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[36] Zhang Y., Gong Y., Gu T., Li Y., and Zhang J., “Flexible Genetic Algorithm: A Simple and Generic Approach to Node Placement Problems,” Applied Soft Computing, vol. 52, pp. 1-14, 2016. Ganesan Thangavel is currently working as an Assistant Professor in the Department of CSE at Koneru Lakshmaiah Education Foundation (KLEF), India, where he is currently pursuing his Ph.D. He has completed his BTech and MTech from Anna University, India. He published several research papers in reputed journals. His research interest includes wireless sensor network, genetic algorithm, and machine learning. Pothuraju Rajarajeswari received her Doctorate in Computer Science and Engineering and Ph.D. in CSE in 2012 from Acharya Nagarjuna University. Currently, she is working as a Professor in the Department of CSE in Koneru Lakshmaiah Education Foundation (KLEF). She has professional memberships in FIETE, MISTE, MIACSIT in the International Association of Computer Science and Information Technology, and MIAENG in the International Association of Engineers. She has 19 years of experience in teaching computer subjects for BTech, MCA, and MTech Postgraduate students. Her research interests are bioinformatics, data mining, artificial intelligence, and data science.