Clustering Based Lifetime Maximizing Aggregation Tree for Wireless Sensor Networks

TL;DR

This paper introduces CLMAT, a clustering-based aggregation tree that enhances energy efficiency and prolongs the lifetime of wireless sensor networks by optimizing energy consumption and reducing data redundancy.

Contribution

The paper proposes a novel CLMAT approach that constructs energy-efficient aggregation trees by selecting nodes with maximum available energy as aggregators.

Findings

CLMAT reduces overall energy consumption.

It extends the network lifetime significantly.

The approach minimizes data transmission costs.

Abstract

Energy efficiency is the most important issue in all facets of wireless sensor networks (WSNs) operations because of the limited and non-replenish able energy supply. Data aggregation mechanism is one of the possible solutions to prolong the life time of sensor nodes and on the other hand it also helps in eliminating the data redundancy and improving the accuracy of information gathering, is essential for WSNs. In this paper we propose a Clustering based lifetime maximizing aggregation tree (CLMAT) in which we create aggregation tree which aim to reduce energy consumption, minimizing the distance traversed and minimizing the cost in terms of energy consumption. In CLMAT the node having maximum available energy is used as parent node/ aggregator node. We concluded with the best possible aggregation tree minimizing energy utilization, minimizing cost and hence maximizing network lifetime.