Certain investigations on improving lifetime of wireless sensor networks through hybrid optimization techniques

Abstract

Wireless Sensor Networks (WSNs) consist of a number of nodes that transfer data to the sink nodes through the Cluster Heads (CHs). Each node acts as a sensor and the data are transferred using a particular cluster termed as CH. The data are transferred to the sink node only when the sink node is present within the transmission range, but if the sink is present at a longer distance from the source, then routing process takes place with the neighbouring nodes or clusters that are directly connected to the sink or with the selection of any one of CHs to transmit the data. This process is carried out until the data are received by the sink node. Data clustering in WSN is a major research area that ensures efficient communication to satisfy energy constraints. The existing routing protocols concentrate on data collection from different environments using WSNs. They must forward the data to control station with minimal delay and enhanced lifetime of the network. In the present research, a hybrid optimization algorithm is proposed to handle the CH selection efficiently in order to ensure energyaware routing and effective communication in WSNs. The optimization algorithm, called Dolphin Echolocation-based Crow Search Algorithm (DECSA) is the combination of Crow Search Algorithm (CSA) and Dolphin Echolocation (DE) algorithm. It provides hybrid optimization of energy conservation for the selection of CHs based on the multi constraints effectively and with high convergence rate. The energy-aware efficient routing is implemented in WSN using the proposed algorithm. The performance metrics of the clustering algorithm are compared through simulation obtained in WSN environment using 50, 75, and 100 nodes newline