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Optimized and load balanced clustering for wireless sensor networks to increase the lifetime of WSN using MADM approaches
Optimized and load balanced clustering for wireless sensor networks to increase the lifetime of WSN using MADM approaches
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Optimized and load balanced clustering for wireless sensor networks to increase the lifetime of WSN using MADM approaches
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Optimized and load balanced clustering for wireless sensor networks to increase the lifetime of WSN using MADM approaches
Optimized and load balanced clustering for wireless sensor networks to increase the lifetime of WSN using MADM approaches

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Optimized and load balanced clustering for wireless sensor networks to increase the lifetime of WSN using MADM approaches
Optimized and load balanced clustering for wireless sensor networks to increase the lifetime of WSN using MADM approaches
Journal Article

Optimized and load balanced clustering for wireless sensor networks to increase the lifetime of WSN using MADM approaches

2020
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Overview
The power utilization has verified as a major problem in wireless sensor networks (WSNs). Many researchers have provided efficient solutions for power utilization. Clustering is one of them that helps in topology control and ensures efficient power utilization. However, the clustering method should be effective that could obtain the best clusters. Comprehensive evolution of clustering protocols for the lifetime of sensor nodes is a unique approach to the total enhancement of the lifetime of WSNs. There are many conflicting factors that affect the efficiency of clustering, i.e. distance between CH and the base station, distance form node to cluster head (CH), maximum residual energy of CHs, etc. The coordination among these factors has a capability to insure the optimal power utilization by reducing power consumption and load balancing among the nodes and CHs. In this paper, we have considered total sixteen such factors and made coordination among them to select the best CHs. Multiple attribute decision-making methods are used to choose best set of CHs from the available alternatives that can fulfill the condition of coordination efficiently. The experimental results validate that the coordination among these sixteen factors put up one of the best demonstration for choosing best CHs.