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• W3124117670 startingPage "791" @default.
• W3124117670 abstract "Nowadays due to smart environment creation there is a rapid growth in wireless sensor network (WSN) technology real time applications. The most critical resource in in WSN is battery power. One of the familiar methods which mainly concentrate in increasing the power factor in WSN is clustering. In this research work, a novel concept for clustering is introduced which is multi weight chicken swarm based genetic algorithm for energy efficient clustering (MWCSGA). It mainly consists of six sections. They are system model, chicken swarm optimization, genetic algorithm, CCSO-GA cluster head selection, multi weight clustering model, inter cluster, and intra cluster communication. In the performance evaluation the proposed model is compared with few earlier methods such as Genetic Algorithm-Based Energy-Efficient Adaptive Clustering Protocol For Wireless Sensor Networks (GA-LEACH), Low energy adaptive Clustering hierarchy approach for WSN (MW-LEACH) and Chicken Swarm Optimization based Genetic Algorithm (CSOGA). During the comparison it is proved that our proposed method performed well in terms of energy efficiency, end to end delay, packet drop, packet delivery ratio and network throughput." @default.
• W3124117670 created "2021-02-01" @default.
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• W3124117670 date "2021-01-25" @default.
• W3124117670 modified "2023-09-23" @default.
• W3124117670 title "MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network" @default.
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• W3124117670 doi "https://doi.org/10.3390/s21030791" @default.
• W3124117670 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7865231" @default.
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• W3124117670 hasPublicationYear "2021" @default.
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