LAUSR

Abstract Wireless Sensor Networks include a substantial number of nodes with limited battery power that is used for gathering and sending the data to the Base station. Here most energy is consumed in data transfer. Hence a foremost problem is the maximization of network lifetime through minimization of energy consumption in the nodes. To resolve this issue, the clustering method is involved to achieve energy-efficient data transmission. A cluster head is elected which uses an energy distribution mechanism to conserve the remaining power, thereby extending network lifetime. Various present methodologies are employed but every algorithm had notable constraints individually. In this paper, a hybrid Sparrow Search Algorithm with Differential Evolution algorithm is intended to solve the energy efficiency issue by cluster head selection in Wireless Sensor Networks. The proposed algorithm uses the high-level search efficiency of the Sparrow Search Algorithm and the lively potential of Differential Evolution that enhances the lifetime of nodes. The performance of this hybrid model seems to be exploiting the count of alive nodes and dead nodes, throughput, and residual energy. The proposed Improved Sparrow search algorithm using the Differential evolution model for choosing the best possible cluster head shows a development in residual power and throughput than other compared algorithms.