Location information verification using transferable belief model for geographic routing in vehicular ad hoc networks
Location verification has witnessed significant attention in vehicular communication due to the growth in number of location based intelligent transport system (ITS) applications. The traditional cryptography based techniques have been suggested to secure and verify location of vehicles. The traditi...
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| Main Authors: | , , , , , |
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| Format: | Article |
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Institution of Engineering and Technology
2017
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/76266/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85017510692&doi=10.1049%2fiet-its.2015.0228&partnerID=40&md5=eafcb4a347597434a8b0ce180db14673 |
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| Summary: | Location verification has witnessed significant attention in vehicular communication due to the growth in number of location based intelligent transport system (ITS) applications. The traditional cryptography based techniques have been suggested to secure and verify location of vehicles. The traditional techniques increase protocol complexity and computational overhead due to the ad hoc nature of vehicular network environments. In this context, this study proposes two layered location information verification cum security (LIVES) technique based on transferable belief model (TBM). In layer 1, tiles based verification is performed using the concepts of virtual tiles on roads and received signal strength. In layer 2, TBM based verification is performed. Specifically, the belief of the presence of a vehicle on each tiles, and the belief of the presence of a vehicle as neighbour of other neighbouring vehicles are combined as collective belief to attest the location claim of a neighbour vehicle. The performance of LIVES is evaluated with road-based and map-based network environments. The single, mixed and multiple adversary vehicles are considered in both the network environments. The comparative performance evaluations attest the benefits of LIVES as compared with the verification and inference of position using anonymous beaconing and without using LIVES. © 2016. The Institution of Engineering and Technology. |
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