Indian Journal of Science and Technology
DOI: 10.17485/ijst/2016/v9i1/85787
Year: 2016, Volume: 9, Issue: 1, Pages: 1-7
Original Article
V. Saranya1*, N. Matheswari2 , R. Punidha3 and M. Soundarya1
1Department of Information Technology, Vel Tech Multitech Engineering College, Chennai - 600062, Tamil Nadu, India; [email protected], [email protected] 2Department of Information Technology, Eloka Enterprise Private Limited, Chennai - 600020, Tamil Nadu, India; [email protected] 3Department of Computer Science and Engineering, Vel Tech Multitech Engineering College, Chennai - 600062, Tamil Nadu, India; [email protected]
*Author For Correspondence
V. Saranya
Department of Information Technology, Vel Tech Multitech Engineering College, Chennai - 600062, Tamil Nadu, India; [email protected]
Background/Objectives: Wireless sensor network deployed over a geological area for monitoring environment and it communicates with connected nodes in the network for processing and gathering information. Methods/Statistical Analysis: This system proposes a framework for tracking, called Energy Efficient Tracking (EET), which defines the nodes of the spatial region around a target, called face. Instead of finding the target location in a face, we estimate movement of a target. Energy efficiency obtained due to tracking being performed by different clusters instead of using a faces. Findings: This system helps to reduce the energy consumption. Brink detection algorithm that enables the sensor network to be aware of target entering the face bit earlier, and to work in a timely fashion. An optimal selection algorithm is to select an appropriate sensor, which can result in having the best detection and low energy cost of transmitting data across the faces. This framework presented an efficient movement tracking system using clustering to overcome the communication latency among nodes. Application/Improvements: Energy efficiency is an important challenge in wireless sensor networks for target tracking applications. This framework proposed efficient tracking using clustering.
Keywords: Brink Detection, Clustering, Energy, Optimal Selection, Spatial, Tracking
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