Load Balanced Clustering in WSN using MADM Approaches

Over the last several decades, wireless sensor networks have grabbed a lot of attention because of their wide range of applications in scientific communities as well as industrial aspects. In WSN, sensor nodes are created with very limited resources, imposing energy constraints. Therefore, it is important to design a less energyconsuming, ascendible and power-efficient approach by selecting the optimal cluster heads (CHs) to enhance the life of these networks. Clustered sensor network is a method to optimize the power consumption in the network, which greatly affects the performance of the networks. In this article, we looked at clustering and routing issues by employing intelligent optimization techniques by considering the maximum attribute of the wireless sensor networks (WSN), which are conflicting in nature. The efficiency of WSN mainly depends upon the conflicting attributes, like residual energy, CH to base station distance, normal node to CH distance, etc. In this paper, multiattribute decision making (MADM) technique is considered for choosing the optimal CHs, so that energy consumption of the nodes is minimized and lifetime of the network can be maximized. The proposed approach is compared with other approaches like LEACH, HEED, etc. Results verified that the proposed algorithm is outmatched in comparison to existing algorithms.

Keywords: Clustering, EECS, HEED, LEACH, LEACH-C, Multi-attribute decision making, Multi-objective decision making.