The widely acceptable problem in wireless sensor networks (WSNs) is to develop a practical scheme for data aggregation in the massive range of sensor nodes that are randomly distributed over a network region. The esse...
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The widely acceptable problem in wireless sensor networks (WSNs) is to develop a practical scheme for data aggregation in the massive range of sensor nodes that are randomly distributed over a network region. The essential operation of cluster heads (CHs) in such a network is to transmit the aggregated data to the sink node through multi-hop communication, thus the energy to be used in a better way during the period of aggregation and transmission. Therefore, this study presents a scheme based on grid clustering and fuzzyreinforcement-learning to maximise network lifetime as well as energy-efficient data aggregation for distributed WSN. Initially, grid clustering is employed for cluster formation and CH selection. Further, a fuzzy rule system-based reinforcement learning algorithm is used to select the data aggregator node based on the parameters, such as distance, neighbourhood overlap, and algebraic connectivity. Finally, the dynamic relocation of the mobile sink is performed within a grid-based clustered network region using a fruit fly optimisation algorithm. The experimental outcomes revealed that the proposed data aggregation scheme provides superior performance in terms of energy consumption and network lifetime compared to earlier systems.
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