A qoi based energy efficient clustering for dense wireless sensor network

TL;DR

This paper proposes an energy-efficient clustering method for dense wireless sensor networks that reduces data redundancy and optimizes QoI, energy use, and congestion control, especially in high-density scenarios.

Contribution

It introduces a novel clustering approach that avoids redundant data transmission in dense networks until the network becomes sparse, improving overall efficiency.

Findings

Reduces data redundancy in dense sensor networks.

Improves energy efficiency and QoI in high-density deployments.

Enhances congestion control by optimizing TDMA usage.

Abstract

In a wireless sensor network Quality of Information (QoI), Energy Efficiency, Redundant data avoidance, congestion control are the important metrics that affect the performance of wireless sensor network. As many approaches were proposed to increase the performance of a wireless sensor network among them clustering is one of the efficient approaches in sensor network. Many clustering algorithms concentrate mainly on power Optimization like FSCH, LEACH, and EELBCRP. There is necessity of the above metrics in wireless sensor network where nodes are densely deployed in a given network area. As the nodes are deployed densely there is maximum possibility of nodes appear in the sensing region of other nodes. So there exists an option that nodes have to send the information that is already reached the base station by its own cluster members or by members of other clusters. This mechanism will affect the QoI, Energy factor and congestion control of the wireless sensor networks. Even though clustering uses TDMA (Time Division Multiple Access) for avoiding congestion control for intra clustering data transmission, but it may fail in some critical situation. This paper proposed a energy efficient clustering which avoid data redundancy in a dense sensor network until the network becomes sparse and hence uses the TDMA efficiently during high density of the nodes.