Multi-Parameter Based Load Balanced Clustering in WSN Using MADM Technique

Abstract

Clustering performs a major role in wireless sensor networks (WSNs) for data aggregation and transmission because efficient energy utilization is becoming a big challenge in WSNs recently to inscribe efficient clustering techniques. Hence, the best set Cluster Heads (CHs) selection remains as a big challenge in WSN to gather data from different sensor nodes. In this script, an approach is introduced for CHs selection by using multi criteria decision making. Seven attributes coverage of CHs, Power of CHs, Sink to CH connectivity, distance of CH to sink, distance of CH to sensor nodes, residual energy of nodes and power of nodes are included for choosing the best set of cluster heads from the available one to balance the energy consumption by using entropy technique for order of preference by similarity to ideal solution (E-TOPSIS) because the conflicting nature of these attributes is difficult to make cooperation in between these attributes. In this MADM (TOPSIS) is used to select best set of CHs by utilizing the eleven attributes for optimal clustering. Finally, the simulation results shows that the propose approach provides a longer service life than the EECS and LEACH and others in the similar environments.