Energy-proportional routing for lifetime extension of clustering-based wireless sensor networks

Chao Lieh Chen, Kuan Rong Lee, Yau Hwang Kuo

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Purpose: The purpose of this paper is to propose an energy-proportional routing (EPR) algorithm, which effectively extends the lifetimes of sensor networks. Design/methodology/approach: The algorithm makes no specific assumption on network topology and hence is suitable for improving sensor networks with clustering. To optimally utilize energy, light-load units - nodes or clusters that conserve energy are ideal candidates as intermediate units for forwarding data from others. To balance the load, first, the proposed algorithm predicts energy consumption of each node in each round. Then the algorithm controls the energy consumption of each unit as close as possible to the threshold representing the energy utilization mean value among clusters. Finally the algorithm checks satisfaction of the energy constraints in terms of distances and predicted data amounts. The proposed algorithm performs routing by determining whether a cluster head or a node should either undertake forwarding tasks or transmit data to intermediate hops. In this way, energy dissipation is evenly distributed to all units and the lifetime of the whole wireless sensor network is ultimately extended. Findings: The algorithm applies hierarchically to different levels of network topology. In addition to experiments, the mathematical proofs of lifetime extension by the proposed routing algorithm are given in accordance with three widely accepted criteria - total energy dissipation, the number of live nodes in each round and the throughput (data amount per round). Originality/value: A new routing algorithm is proposed.

Original languageEnglish
Pages (from-to)304-321
Number of pages18
JournalInternational Journal of Pervasive Computing and Communications
Volume3
Issue number3
DOIs
Publication statusPublished - 2008 Feb

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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