Articles | Volume 5, issue 1
Regular research article
02 Mar 2016
Regular research article |  | 02 Mar 2016

Combination of clustering algorithms to maximize the lifespan of distributed wireless sensors

Derssie D. Mebratu and Charles Kim

Abstract. Increasing the lifespan of a group of distributed wireless sensors is one of the major challenges in research. This is especially important for distributed wireless sensor nodes used in harsh environments since it is not feasible to replace or recharge their batteries. Thus, the popular low-energy adaptive clustering hierarchy (LEACH) algorithm uses the “computation and communication energy model” to increase the lifespan of distributed wireless sensor nodes. As an improved method, we present here that a combination of three clustering algorithms performs better than the LEACH algorithm. The clustering algorithms included in the combination are the k-means+ + , k-means, and gap statistics algorithms. These three algorithms are used selectively in the following manner: the k-means+ +  algorithm initializes the center for the k-means algorithm, the k-means algorithm computes the optimal center of the clusters, and the gap statistics algorithm selects the optimal number of clusters in a distributed wireless sensor network. Our simulation shows that the approach of using a combination of clustering algorithms increases the lifespan of the wireless sensor nodes by 15 % compared with the LEACH algorithm. This paper reports the details of the clustering algorithms selected for use in the combination approach and, based on the simulation results, compares the performance of the combination approach with that of the LEACH algorithm.

Short summary
Many different techniques have been introduced in an effort to maximize heterogeneous wireless sensor lifespan, but these techniques have focused on having the nodes in a cluster send their data to a selected cluster head node that, in turn, reports the data to the base station. Therefore, the choice of the number of clusters and the way the cluster head node is selected are the main focuses of this research paper.