Coverage and Connectivity Aware Neural Network Based Energy Efficient Routing in Wireless Sensor Networks

TL;DR

This paper introduces a neural network-based routing scheme for wireless sensor networks that optimizes energy use by considering coverage and connectivity, thereby extending network lifetime.

Contribution

It presents a novel neural network approach with adaptive learning for cluster head selection and energy-efficient routing based on coverage and connectivity constraints.

Findings

Improved network lifetime compared to existing schemes

Higher packet delivery fraction in simulations

More efficient residual energy utilization

Abstract

There are many challenges when designing and deploying wireless sensor networks (WSNs). One of the key challenges is how to make full use of the limited energy to prolong the lifetime of the network, because energy is a valuable resource in WSNs. The status of energy consumption should be continuously monitored after network deployment. In this paper, we propose coverage and connectivity aware neural network based energy efficient routing in WSN with the objective of maximizing the network lifetime. In the proposed scheme, the problem is formulated as linear programming (LP) with coverage and connectivity aware constraints. Cluster head selection is proposed using adaptive learning in neural networks followed by coverage and connectivity aware routing with data transmission. The proposed scheme is compared with existing schemes with respect to the parameters such as number of alive nodes, packet delivery fraction, and node residual energy. The simulation results show that the proposed scheme can be used in wide area of applications in WSNs.