Performance Evaluation of Various Clustering Techniques for Gathering Big Data in Distributed Wireless Sensor Network

In the digital communication era, big data plays an important role in wireless technology. One of the highly scheduled key contributors of big data is wireless sensor network. The overall data generated across the large sensors in the distributed wireless sensor networks can produce a significant big data. The efficient method for data gathering is a major concern in wireless sensor network. Some important data gathering techniques are, Fixed Clustering(FC), Mobile sink based Energy Efficiency Clustering (MEC), Expectation Maximization algorithm(EMC), Modified Expectation Maximization algorithm technique (MEM). In this paper, all above methods are analyzed and compared to show that the modified expectation maximization gives the optimum result more accurately than the rest of other techniques. A novel technique namely Fuzzy MEM which includes advantages of MEM also proposed in this paper.