Privacy‐preserving cooperative localization in vehicular edge computing infrastructure. (13th June 2020)

• Record Type: Journal Article
• Authors: Chandra Shit, Rathin; Sharma, Suraj; Watters, Paul; Yelamarthi, Kumar; Pradhan, Biswajeet; Davison, Richard; Morgan, Graham; Puthal, Deepak
• Other Names: Wu Chase guestEditor.; Yildirim Tulay guestEditor.; Ivanovic Mirjana guestEditor.; Bellatreche Ladjel guestEditor.; Wyrzykowski Roman guestEditor.; Ciorba Florina M. guestEditor.
• Is Part Of: Concurrency and computation. Volume 34:Number 14(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-13
• Subjects: cooperative localization -- distributed localization -- intelligent transportation system -- privacy‐preserving localization -- vehicular edge computing; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.5827
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Advancement of computing and communication techniques transforms the traditional transport system into the intelligent transportation system (ITS). The development of distributed computing in a vehicular network platform also called Vehicular Edge Computing (VEC) promise to address most of the challenges faced by the ITS. Localization is important in these vehicular networks because of its key contribution in autonomous driving, smart traffic monitoring, and collision avoidance services. For localization, current GPS and hybrid methods are in‐efficient because of GPS outage in urban infrastructure and dynamic nature of the vehicular networks. The cooperative localization approaches, on the other hand, use dedicated short range communication to broadcast messages and estimate location. However, these messages are un‐encrypted and periodic which gives a privacy risk for vehicles. This article presents a privacy‐preserving cooperative localization in vehicular network based upon dynamic pseudonym changing strategy. First, the localization delay is addressed with the implementation of dynamic vehicular edge assignment for computational task management. In the next step, the localization is estimated from the neighbor and road side unit ranging measurement followed by a real‐time prediction of the vehicle. The performance of the proposed algorithms is analyzed in terms of localization accuracy and privacy preservation strength. Furthermore, the proposed method is simulated in a real city scenario followed by localization accuracy and privacy analysis. Finally, the localization accuracy and privacy strength of the proposed approach are compared with the state‐of‐the‐art methods.