Multiple Parameter Based Energy Balanced and Optimized Clustering for WSN to Enhance the Lifetime Using MADM Approaches

Efficient utilization of power has recently emerged as a critical issue in sensor networks that is addressed by efficient clustering techniques. In WSN, clustering process selects cluster heads (CHs) to control the topology and consumes the power effectively. The comprehensive evolution of CH selection process increases the lifetime of sensor nodes resulting in total enhancement of the lifetime of WSN. The efficiency of clustering is affected by many attributes like higher residual energy, distance from a normal node to CH, distance from CH to the Base Station, etc. The conflicting nature of these attributes makes it difficult to find the cooperation among these attributes for optimal clustering. In this paper, we have applied MADM approaches for optimal CH selection to enhance the lifetime of WSN by utilizing eleven attributes, these attributes have very important role in efficient power consumption during data set collection. The MADM approaches employed for ranking and choosing optimal CHs are: Technique for Order Preference by Similarity to Ideal Solution, Preference Ranking Organization METHod for Enrichment Evaluations, and Analytic Hierarchy Process. Results reveal that these eleven attributes helps the proposed approach to outperform over the other approaches such as LEACH, LEACH-C and EECS in terms of lifetime.