Evaluation of active position detection in Vehicular Ad Hoc Networks

  • Kiran Penna
  • , Venkatesh Yalavarthi
  • , Huirong Fu
  • , Ye Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Vehicular Ad Hoc Network (VANET) is a promising technology in which vehicle-to-vehicle and vehicle-to-roadside infrastructure wireless communications can be achieved. This is important to obtain road safety for vehicles and drivers and collision avoidance. A falsified position by malicious users is one of the important issues in VANETs. Vehicle position identification is one of the important aspects in establishing authentication and security between inter vehicular communication exchange. Deepa et al presented two approaches for verifying sender's position in a multi-hop network. Their first proposed algorithm relies on signal propagation time for verifying the position. Their second proposed algorithm verifies the position information with the help of base stations located in the coverage area of the vehicular network The main contribution of our work is validating their approach by running an ns2 simulation with dynamic number of nodes in various mobility scenarios such as urban, rural, Manhattan. We have also generated different scenarios with variable velocity ranges and simulated the VANET. We have also considered the effect of delay, jitter in our simulation and observed that the proposed approach is robust and a feasible solution to the problem of Active Position detection.
Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Place of Publicationusa
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2234-2239
Number of pages6
ISBN (Electronic)9781479914845
DOIs
StatePublished - Sep 3 2014
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: Jul 6 2014Jul 11 2014

Conference

Conference2014 International Joint Conference on Neural Networks, IJCNN 2014
Country/TerritoryChina
CityBeijing
Period07/6/1407/11/14

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