Efficient sensor node connectivity and target coverage using genetic algorithm with Daubechies 4 lifting wavelet transform. (29th March 2022) - Ganesan, T.; Rajarajeswari, Pothuraju
Recently, target coverage algorithms are widely used to monitor the target point by dividing sensor nodes into a set of cover groups where each sensor cover group contains the target points. Optimal sensor node placement imposes a critical task when the number of sensors is limited. The quality of maximum target coverage and node connectivity can be improved by deploying sensors in the optimal location. In this paper, a novel genetic algorithm with 2D discrete Daubechies 4 (db4) lifting wavelet transform is proposed for identifying the optimal position of each sensor. Initially, the genetic algorithm identifies the population-based sensor location and 2D 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 further prove that the proposed model is better than the existing algorithm, a set of experiments are carried out with different scenarios by achieving maximum target points covers, node connectivity, and network lifetime with a limited number of sensor nodes.