Optimized scheme for cluster head selection and EAMPR routing protocol design in WSN

Abstract

Wireless sensor networks (WSN) is a popularly emerging technology with several opportunities to sustain in various field that require multipurpose sensor nodes, less energy and non-expensive system. But in the WSN, the radio transmission needs high amount of energy and this creates the critical problem. Hence consumption of energy has to be decreased to extend the network durability. Even though there are so many techniques existing for clustering approach of WSN, they have limitations like increased energy consumption, less delivery rate of data, redundancy and unbalanced network load. Hence, these problems are solved by introducing the energy efficient deep learning techniques for clustering and finding the optimal route. Initially the initialization process of system model is performed with the implementation of energy model. In WSN, energy consumption should be reduced to enhance the QoS and balance the network traffic. Hence clustering method is used to group up the sensor nodes and the optimal cluster head is selected with the proposed technique of hybrid cuckoo search and particle swarm optimization (CSO-PSO). As the CH is chosen, the optimal path of routing data should be found in addition with the procedure of optimization and it is done through the proposed model of Optimization based routing protocol that incorporates the Energy Aware Multi Point Routing (EAMPR) protocol along with the Improved Tuna Search Optimization (ITSO) algorithm. Finally, by the use of ITSO-EAMPR technique the energy consumption will get reduced with the decrease in relative mobility and high stability of nodes would be achieved. The simulations are proceeded and the outcomes are validated. The result obtained is compared with the traditional methods to show the effectiveness of proposed technique. As per the results obtained the proposed ITSO-EAMPR attains maximized PDR and Throughput, higher energy efficiency with extension in lifetime of WSN along with decrease in BER, end-to-end latency as compared to the existing techniques.