A new rank-order clustering algorithm for prolonging the lifetime of wireless sensor networks

TL;DR

This paper introduces ARO-WSN, a novel clustering algorithm combining hierarchical and distance-based methods, significantly extending wireless sensor network lifetime by improving energy efficiency.

Contribution

The paper presents ARO-WSN, a new clustering algorithm adapted from image processing techniques, optimized for energy efficiency and network longevity in WSNs.

Findings

ARO-WSN outperforms LEACH, LEACH-C, and K-means in energy consumption.

ARO-WSN extends network lifetime compared to classical algorithms.

The algorithm runs in O(n) time, suitable for large WSNs.

Abstract

Energy efficient resource management is critical for prolonging the lifetime of wireless sensor networks (WSN). Clustering of sensor nodes with the aim of distributing the traffic loads in the network is a proven approach for balanced energy consumption in WSN. The main body of literature in this topic can be classified as hierarchical and distance-based clustering techniques in which multi-hop, multi-level forwarding and distance-based criteria, respectively, are utilized for categorization of sensor nodes. In this study, we propose the Approximate Rank-Order Wireless Sensor Networks (ARO-WSN) clustering algorithm as a combined hierarchical and distance-based clustering approach. ARO-WSN algorithm which has been extensively used in the field of image processing, runs in the order of O(n) for a large data set, therefore it can be applied on WSN. The results shows that ARO-WSN outperforms the classical LEACH, LEACH-C and K-means clustering algorithms in the terms of energy consumption and network lifetime.