Anjna Deen

Work place: University Insititute of Technology,RGPV,Bhopal,India

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Research Interests: Computer Networks, Computer Architecture and Organization, Neural Networks

Biography

Assistant Professor Anjna Deen: Mrs. Deen has received her Bachelor’s degree in Computer Science & Engineering from UIT (GEC)-RGPV, Bhopal India in 1993, and completed M.Tech. degree from Maulana Azad National Institute of Technology, Bhopal, India in 2007. At present, she is working as an Assistant Prof. in UIT-RGPV Bhopal since 1996 .She has published various research papers in National and International Journals. Her areas of interest include Computer Networks, Neural Networks, Wireless communication and Bioinformatics.

Author Articles
Location Based Data Aggregation with Energy Aware Scheduling at RSU for Effective Message Dissemination in VANET

By Akanksha Choudhary Rajeev Pandey Anjna Deen

DOI: https://doi.org/10.5815/ijem.2017.03.06, Pub. Date: 8 May 2017

Vehicular adhoc networks (VANETs) are relegated as a subgroup of Mobile adhoc networks (MANETs), with the incorporation of its principles. In VANET the moving nodes are vehicles which are self-administrated, not bounded and are free to move and organize themselves in the network. VANET possess the potential of improving safety on roads by broadcasting information associated with the road conditions. This results in generation of the redundant information been disseminated by vehicles. Thus bandwidth issue becomes a major concern. In this paper, Location based data aggregation technique is been proposed for aggregating congestion related data from the road areas through which vehicles travelled. It also takes into account scheduling mechanism at the road side units (RSUs) for treating individual vehicles arriving in its range on the basis of first-cum-first order. The basic idea behind this work is to effectually disseminate the aggregation information related to congestion to RSUs as well as to the vehicles in the network. The Simulation results show that the proposed technique performs well with the network load evaluation parameters.

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