An Improved Energy-Aware Clustering Method for the Regional Queries in the Internet of Things

TL;DR

This paper proposes an enhanced energy-efficient hierarchical clustering method for IoT query processing, aiming to improve accuracy, reduce energy consumption, and prevent network disconnection by optimizing clustering and eliminating data duplication.

Contribution

It introduces a novel clustering index tree optimized for energy efficiency and query accuracy in IoT, addressing data heterogeneity and redundancy issues.

Findings

Reduced energy consumption through data duplication elimination

Improved query accuracy in heterogeneous clusters

Enhanced network stability by preventing disconnections

Abstract

We will offer a method to improve energy efficient consumption for processing queries on the Internet of Things. We focused on an energy efficient hierarchical clustering index tree such that we can facilitate time-correlated region queries in the I.O.T (Internet of Things). We try to improve clustering and make a change on its proposed index tree. We try to do this by optimizing the query processing. We improve clustering to increase the accuracy of the Internet of Things and prevent the network from disconnecting. In the article that we have chosen, there is a heterogeneous cluster which means there exists a large data difference in the two ends of a cluster. Also, it often happens that the same information is sent to the base station by two overlapping clusters; therefore, we save energy by eliminating duplicated data.