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- W3133840799 abstract "Recent trends in Wireless Sensor Network (WSN) have been adopted as an emerging networking paradigm called Software-Defined Wireless Sensor Network (SD-WSN) to dynamically and efficiently managing the network functionality. Generally, smart sensor devices suffer from low battery and could not be replaced after a deployment. Therefore, This work is focusing on the energy-efficient clustering routing problem in SD-WSN. In SD-WSN, Control Server (CS) assigns the tasks to selected Control Nodes (CNs) dynamically. Thus, CNs selection is formulated as an optimization problem in SD-WSNs. To solve this, a nature-inspired approach i.e. Gray-Wolf Optimization (GWO) is applied to balance the normal nodes' energy. Thus, proposed approach is referred to as the Energy-balanced Grey-wolf optimizer (EBGWO). Further, a fitness function is designed that considers several parameters e.g., CS to CNs distance, intracluster distance, node residual energy, and cluster size. Thus, the EBGWO perform balanced, energy-efficient clustering and prolong the network lifespan. The comparative simulation results show that the EBGWO approach outperforms the NWPSO concerning network lifespan, residual energy, network throughput, and convergence rate." @default.
- W3133840799 created "2021-03-15" @default.
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- W3133840799 date "2021-01-20" @default.
- W3133840799 modified "2023-09-27" @default.
- W3133840799 title "A Meta-heuristic-based Green-routing Algorithm in Software-Defined Wireless Sensor Network" @default.
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- W3133840799 doi "https://doi.org/10.1109/icict50816.2021.9358478" @default.
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