Distributed On-Demand Clustering Algorithm for Lifetime Optimization in Wireless Sensor Networks


Wireless Sensor Networks (WSNs) play a significant role in Internet of Things (IoT) to provide cost effective solutions for various IoT applications, e.g., wildlife habitat monitoring, but are often highly resource constrained. Hence, preserving energy (or, battery power) of sensor nodes and maximizing the lifetime of WSNs is extremely important. To maximize the lifetime of WSNs, clustering is commonly considered as one of the efficient technique. In a cluster, the role of individual sensor nodes changes to minimize energy consumption, thereby prolonging network lifetime. This paper addresses the problem of lifetime maximization in WSNs by devising a novel clustering algorithm where clusters are formed dynamically. Specifically, we first analyze the network lifetime maximization problem by balancing the energy consumption among cluster heads. Based on the analysis, we provide an optimal clustering technique, in which the cluster radius is computed using alternating direction method of multiplier. Next, we propose a novel On-demand, oPTImal Clustering (OPTIC) algorithm for WSNs. Our cluster head election procedure is not periodic, but adaptive based on the dynamism of the occurrence of events. This on-demand execution of OPTIC aims to significantly reduce computation and message overheads. Experimental results demonstrate that OPTIC improves the energy balance by more than 18% and network lifetime by more than 19% compared to a non-clustering and two clustering solutions in the state-of-the-art.


Computer Science

Research Center/Lab(s)

Center for High Performance Computing Research

Second Research Center/Lab

Intelligent Systems Center

Keywords and Phrases

Energy balance; Linear programming; Network lifetime; On-demand clustering; Wireless sensor network

International Standard Serial Number (ISSN)


Document Type

Article - Journal

Document Version


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© 2020 Elsevier, All rights reserved.

Publication Date

01 Jul 2020