With the rising popularity of Cloud computing and Mobile computing, different field, such as individuals, enterprises and research centers have started outsourcing their IT and computational requirement to on-demand cloud services. Many research works have been done on the load balanced issue for wireless sensor network or WSN in Mobile Cloud Computing. For supplying fast, reliable, secure data, collected by WSN to users through cloud is an important issue in Mobile Cloud Computing. Main limitation of WSN is it has limited energy resources, thus maximizing the lifetime of the sensor node; it is required to distribute the energy dissipated throughout the wireless sensor network, which is a critical problem. There are several existing clustering based algorithm in case of load distribution such as, K-means clustering, random clustering etc. In order to minimize maintenance and maximize overall system performance distribution of load to all sensor nodes not only cluster head node is preferable job. Here we present MapReduce based K-means clustering for load distribution equally to all sensor nodes for avoiding overload condition and optimized energy consumption in WSN. MapReduced based clustering method is desirable due to its scalability and fault tolerant property. In our approach, it reads data from and writes the output back to the sensor nodes once. We also show that our proposed algorithm gives better performance in comparison with other existing load distribution approach for WSN by optimizing overload condition from cluster head node [CH] in case of wireless sensor network.

Mobile Cloud Computing, Wireless Sensor Network, load distribution, Clustering, MapReduce, K-means

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