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The Efficient Localization and Data Gathering in Mobile wireless sensor networks (MWSNs) is considered as an evolving technology for many system applications like military surveillance, health monitoring and reporting to ambulance team, tracking the animal migration patterns, etc.,. The wireless sensor nodes with the ability to report the location information and sensory data. Therefore, there have been a numerous significances on localization and Energy Efficient data gathering in MWSNs in the past few years. In this paper, our proposed system Energy Efficient Data Gathering Pattern (EEDGP) is performs in two phases. In the first phase the recognition of pattern is done by K-Means technique. The K-Mean clustering is simple and understandable algorithm based on the attributes and it is used to generate the specific number of disjoint clusters. Also the maximum distance which travels by the node and velocity of the node is estimated by Hop count Journal of ICT, Vol. 5 2, 129–148. doi: 10.13052/jicts2245-800X.521 This is an Open Access publication. c © 2018 the Author(s). All rights reserved. 130 S. Sivasakthiselvan and V. Nagarajan measurement Markov Decision Process (MDR) is proposed to check the inaccuracy of localization. In the second phase global relocalization is carried out based on the result of the local relocalization and it performs global time synchronization for data gathering from the Mobile Agent (MA) in the network. We compared our proposed algorithm with other approaches in Network lifetime, Energy consumption, Packet delivery ratio, and Time Complexity. The simulation results proved the effectiveness of the proposed algorithm over similar methods.

Energy Efficient Data Gatheringby Using Optimum Pattern Recognitionwith Relocalization in Mobile WirelessSensor Networks