Construction of energy minimized WSN using GA-SAMP-MWPSO and K-mean clustering algorithm with LDCF deployment strategy.

Energy is the most valuable resource in WSN. As the power storage capability of the tiny WSN is very limited, efficient mechanisms for energy minimization should be implemented to increase the overall lifetime of the network. Efficient clusterization of the area, cluster head selection leads toward the energy minimization of the whole network. Previously very few research works had been reported where the use of serve points had been considered. In this research work, the WSN has been configured depending upon many important parameters like (1) the frequency of serve points in a particular area and (2) WSN node deployment strategy in the target area. The main goal of this paper is to construct an efficient wireless sensor network. The efficiency of the network directly depends on the minimization technique of the consumed energy. So by minimizing the consumed energy an efficient wireless sensor network has been established. In this research, the specific region (target area) has been clustered using a modified K-mean algorithm to fix the position of the sink node. Deployment of WSN nodes in the target area has been done using least distance connect first (LDCF) deployment strategy. The cluster head has been selected for each cluster for each duty cycle. After configuring the WSN by using a modified k-mean algorithm and (LDCF) deployment strategy, a modified metaheuristic hybrid algorithm (GA-SAMP-MWPSO) has been used to get the optimized energy consumption for the network. Ultimately the consumed energy and the total day-life of the network have been calculated. The result has been compared with the existing literature, and it has been noted that the proposed technique yields 15.5544456 day-life of the network where the existing literature's approach gives 6.333777 day-life of the network. The proposed algorithm delivers a better outcome. [ABSTRACT FROM AUTHOR]