Multi-Objective Optimization of Clustering Techniques in WSN using Harmony Search Algorithm

Abstract

Now a days we all know that wireless sensor network (WSN) is used in many application like commercial, military, and environmental. Clustering is one of the popular method. Energy consumption optimization is a major focus while planning and the designing the operation of wireless sensor network. The lifetime of a network can be extended by clustering technique which includes data aggregation and balancing the energy consumption among the sensor nodes. The algorithms which are generally ussed are heuristic and and they aim to generate minimum number of cluster in a WSN and also to keep minimum distance between them.So here we are using some protocol like LEACH(low energy adaptive clustering hierarchy), GCA(genetic clustering algorithm), Fuzzy-C mean, Particle Swarm Optimization (PSO), ERP(Evolutionary Routing Protocol), EAERP(Energy Aware Evolutionary Routing Protocol), HSA(Harmony Search Algorithm) to know that which protocol is best for energy consumption, which is minimize the transmission distance etc. Heuristic methods suggest probable solutions from which best objective function is selected as maximum or minimum valued function. Depending on the optimization function design the choice may be to have minimum value or maximum value. There can be sub-objectives also, which may be con icting in nature. The multi-objective function can not have unique solutions So there is a possibility of multiple probable solutions. The same has been explore in this thesis.