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Employing Grey Wolf Optimizer for Energy Sink Holes Avoidance in WSNs

  • Research Article-Computer Engineering and Computer Science
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Abstract

Many efficient data gathering approaches have been proposed utilizing a mobile sink (MS). MS significantly alleviates the energy holes that result from multi-hop data dissemination near the stationary sink in wireless sensor networks (WSNs). However, most of those approaches design a predetermined MS trajectory that may encounter changes in sensor nodes status during the MS roaming. Thus, this paper proposed two MS methods called fuzzy A-star sink mobility (FASM) and grey wolf mobility (GWM). Both methods aim to alleviate the energy holes and data latency by considering the residual energy, sensor density, source sensors angle, and traffic load as guiding parameters for the next potential position. FASM uses a grid model with a fuzzy inference system, while GWM uses the grey wolf optimizer to explore the optimal MS position precisely. Both methods utilize fuzzy A-star routing protocol to run all sensors even if they were far from the MS to reduce the buffers overflow and provide a balanced energy consumption during data routing. The effectiveness of the proposed schemes for prolonging the WSNs lifetime is confirmed through strict simulations as they have been compared with two efficient existing protocols which are WRP and DBRkM.

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Authors and Affiliations

  1. Department of Computer Science, College of Computer Science and Information Technology, University of Basrah, Basrah, 61007, Iraq

    Mohammed Dheyaa Algubili & Imad S. Alshawi

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  1. Mohammed Dheyaa Algubili
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  2. Imad S. Alshawi
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Correspondence to Mohammed Dheyaa Algubili.

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Algubili, M.D., Alshawi, I.S. Employing Grey Wolf Optimizer for Energy Sink Holes Avoidance in WSNs. Arab J Sci Eng 48, 2297–2311 (2023). https://doi.org/10.1007/s13369-022-07259-6

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  • DOI: https://doi.org/10.1007/s13369-022-07259-6

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