Energy Effective Data Gathering In Wsn: A Hybrid Approach Using K-Means And AFSO
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Abstract
Data gathering using a static sink causes the nodes nearby sink to run out of energy very soon and isolate the network. The invention of Mobile Sinks has reduced energy consumption, balanced the network by even energy consumption, and also solve hotspot issues by keeping the network connected. But all these are dependent on the mobile sink path. In this work, a dual mobile base station data gathering, and mobile charging path is formed by the artificial fish swarm optimization algorithm. The path is formed considering fitness function. The data gathering points known as summit stations are formed by using the K-means algorithm by calculating the weight function establishing complete coverage of all the nodes. The proposed method EEDG is vindicated with existing algorithms in terms of packet delivery ratio, delay, lifetime, and goodput. Results Show increased packet delivery ratio and reduced delay when compared with existing algorithms. EEDG prolongs the lifetime by 52% more than K-means and 78% more than GEACH.
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