Achieving optimal data collection efficiency with dynamic levy flight-enabled PSO in mobile sink-based WSNs

V. P. Sreekantha Kumar () and N. Kumaratharan ()
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V. P. Sreekantha Kumar: Anna University Chennai
N. Kumaratharan: Sri Venkateswara College of Engineering

Telecommunication Systems: Modelling, Analysis, Design and Management, 2024, vol. 87, issue 4, No 4, 939-957

Abstract: Abstract The data aggregation with the aid of mobile sink in wireless sensor networks (WSNs) is a promising solution to the recent hot-spot or sink-hole issues induced by multi-hop routing employing the static sink. Despite everything, most of the baseline models concentrate on energy-efficient data aggregation issues but struggle to maintain a tradeoff between energy energy-efficient and load-balanced data collection. In this research, we propose a novel Dynamic Levy Flight-enabled PSO (Dynamic LFPSO) optimization algorithm for addressing the load-balanced data aggregation problem with mobile sinks in WSNs. The Dynamic LFPSO algorithm incorporates a structured tree path for efficient data collection, where mobile sinks traverse the network following an optimized path. The algorithm leverages the benefits of the PSO algorithm combined with Levy Flight and dynamic inertia weight to achieve energy-efficient and load-balanced data collection while minimizing data collection delay. The comprehensive simulations are conducted using an NS-3 network simulator which demonstrates that the Dynamic LFPSO algorithm achieves a lower data collection delay of 55.4 ms, a higher network lifetime of 461 rounds, an improved Packet delivery ratio of 97.2%, and a better throughput of 50 kbps. Overall, the Dynamic LFPSO algorithm leads to better usage of network resources and prolonged network lifetime and also offers a practical solution to the challenges in WSNs, providing a foundation for further research and advancements in the field.

Keywords: WSNs; Load balancing; Data collection; Mobile sinks; Particle swarm optimization; Levy flight; Dynamic inertia weight (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11235-024-01198-3

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