Mobility pattern based misbehavior detection to avoid collision in vehicular adhoc networks

Mohammad Abdur Razzaque, Fuad A. Ghaleb, Anazida Zainal

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Citations (Scopus)

Abstract

Vehicular Ad-hoc Network (VANET) can improve road safety through collision avoidance. False or bogus information is a real threat in VANET’s safety applications. Vehicles or drivers may react to false information and cause serious problems. In VANETs drivers’ behavioral tendencies can be reflected in the mobility patterns of the vehicles. Monitoring mobility patterns of the vehicles within their transmission range helps them in earlier detection of the correctness of the received message. This paper presents a misbehavior detection scheme (MDS) and corresponding framework based on the mobility patterns analysis of the vehicles in the vicinity of concerned vehicles. Initial simulation results demonstrate the potential of the proposed MDS and framework in message’s correctness detection, hence its corresponding applications in collision avoidance.

Original languageEnglish
Title of host publicationUbiquitous Computing and Ambient Intelligence
Subtitle of host publicationPersonalisation and User Adapted Services - 8th International Conference, UCAmI 2014, Proceedings
EditorsSungyoung Lee, Ramón Hervás, José Bravo, Chris Nugent
PublisherSpringer Verlag
Pages300-303
Number of pages4
ISBN (Electronic)9783319131016
Publication statusPublished - 1 Jan 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8867
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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Razzaque, M. A., Ghaleb, F. A., & Zainal, A. (2014). Mobility pattern based misbehavior detection to avoid collision in vehicular adhoc networks. In S. Lee, R. Hervás, J. Bravo, & C. Nugent (Eds.), Ubiquitous Computing and Ambient Intelligence: Personalisation and User Adapted Services - 8th International Conference, UCAmI 2014, Proceedings (pp. 300-303). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8867). Springer Verlag.