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The Scientific World Journal
Volume 2015 (2015), Article ID 613923, 9 pages
Research Article

An Energy-Efficient Cluster-Based Vehicle Detection on Road Network Using Intention Numeration Method

1Department of Information Technology, Easwari Engineering College, Chennai 600089, India
2Department of Computer Science and Engineering, Easwari Engineering College, Chennai 600089, India

Received 6 November 2014; Accepted 22 January 2015

Academic Editor: Long Cheng

Copyright © 2015 Deepa Devasenapathy and Kathiravan Kannan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The traffic in the road network is progressively increasing at a greater extent. Good knowledge of network traffic can minimize congestions using information pertaining to road network obtained with the aid of communal callers, pavement detectors, and so on. Using these methods, low featured information is generated with respect to the user in the road network. Although the existing schemes obtain urban traffic information, they fail to calculate the energy drain rate of nodes and to locate equilibrium between the overhead and quality of the routing protocol that renders a great challenge. Thus, an energy-efficient cluster-based vehicle detection in road network using the intention numeration method (CVDRN-IN) is developed. Initially, sensor nodes that detect a vehicle are grouped into separate clusters. Further, we approximate the strength of the node drain rate for a cluster using polynomial regression function. In addition, the total node energy is estimated by taking the integral over the area. Finally, enhanced data aggregation is performed to reduce the amount of data transmission using digital signature tree. The experimental performance is evaluated with Dodgers loop sensor data set from UCI repository and the performance evaluation outperforms existing work on energy consumption, clustering efficiency, and node drain rate.