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Energy Balanced Clustering and Data Gathering for Large-Scale Wireless Sensor Networks

Published: 28 September 2017 Publication History

Abstract

Clustering is an efficient technique for saving energy of wireless sensor networks (WSNs). In this paper a two-level clustering approach is presented, combining a traditional gradient-based clustering technique with an evolutionary optimization technique based on the Gravitational Search Algorithm (GSA), and targeting to improved performance in large-scale WSNs (where typical approaches usually lead to performance degradation). The proposed protocol initially creates energy-balanced multi-hop clusters, where the energy of the sensors increases progressively as getting closer to the cluster head (CH). In the second phase of the protocol an appropriate GSA-based evolutionary algorithm is executed in order to assign groups of CHs to specific 'gateways' for the final data forwarding to the base station (BS). The GSA fitness function is adequately defined taking in account both the distance from the CHs to the gateways and the BS as well as the residual energy of the gateways. Simulation results show the high performance of the proposed scheme as well as its superiority over the native GSA-based approach presented in the literature.

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        PCI '17: Proceedings of the 21st Pan-Hellenic Conference on Informatics
        September 2017
        322 pages
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        • Greek Com Soc: Greek Computer Society
        • University of Thessaly: University of Thessaly, Volos, Greece

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        New York, NY, United States

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        Published: 28 September 2017

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        Author Tags

        1. Wireless sensor networks
        2. data gathering
        3. gravitational search algorithm
        4. network lifetime
        5. node clustering

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        PCI 2017
        PCI 2017: 21st PAN-HELLENIC CONFERENCE ON INFORMATICS
        September 28 - 30, 2017
        Larissa, Greece

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