Energy based analytical modelling of ANCH clustering algorithm for wireless sensor networks

Morteza Mohammadi Zanjireh, Hadi Larijani, Wasiu O. Popoola

    Research output: Contribution to journalArticle

    Abstract

    Wireless Sensor Networks (WSNs) have had remarkable advances in the past couple of decades due to their fast growth and flexibility. In order to supervise an area, hundreds or thousands of sensors can be established and collaborate with each other in the environment. The sensors’ sensed and collected data can be delivered to the base station. Energy optimisation is crucial in WSN’s efficiency. Organising sensor nodes into small clusters helps save their initial energy and thus increases their lifetime. Also, the number and distribution of Cluster Heads (CHs) are fundamental for energy saving and flexibility of clustering methods. Avoid Near Cluster Heads (ANCH) is one of the most recent energy-efficient clustering algorithms proposed for WSNs in order to extend their lifetime by uniform distributing of CHs through the network area. In this manuscript, we suggest an analytical approach to model the energy consumption of the ANCH algorithm. The results of our comprehensive research show a 95.4% to 98.6% accuracy in energy consumption estimation using the proposed analytical model under different practical situations. The suggested analytical model gives a number of indications concerning the impact of different factors on the energy depletion pattern of the ANCH clustering algorithm.
    Original languageEnglish
    Pages (from-to)173 -182
    Number of pages10
    JournalInternational Journal on Advances in Networks and Services
    Volume7
    Issue number3/4
    Publication statusPublished - 30 Dec 2014

    Keywords

    • wireless sensor networks
    • ANCH
    • analytical modelling
    • algorithm

    Fingerprint Dive into the research topics of 'Energy based analytical modelling of ANCH clustering algorithm for wireless sensor networks'. Together they form a unique fingerprint.

  • Cite this