Wireless sensor networks consist of sensor nodes, which include huge application in disaster management, habitat monitoring, military, and security preference and so on. Wireless sensor nodes might be small in size and have less-processing capability by means of low battery power consumption. These are listed as the important constraints for many WSN applications such as network lifetime, node mobility, adaptability, scalability, energy efficient, load balancing and availability. Clustering method utilized the sensor nodes is an efficient technique to achieve these goals. The different clustering algorithms also differ in their objectives. In this paper, a new method is to achieve the proposed technique because it supports on MAPREDUCE programming model and EM (Expected Maximization) clustering algorithm. The key performances of the proposed algorithm HCM (Hierarchical Clustering with MapReduce) manage minimizing energy consumption, and take full advantage of network lifetime. The simulated performance of the results implement in the NS-2 platform, which exhibits the longer network lifetime of the proposed HCM algorithm and also it has performed better than the well-known clustering algorithms, DHAC (Distributed Hierarchical Agglomerative Clustering), HAC (Hierarchical Agglomerative Clustering), and K-Means with MapReduce.

Wireless Sensor Network, Expectation-Maximization Clustering, Cluster-Based Data Aggregation, Energy Efficient Clustering Algorithm for Maximizing Lifetime, Hierarchical Agglomerative Clustering, Distributed Hierarchical Agglomerative Clustering, K-Means Clustering using Map-Reduce Technique

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