Abstract
Fog-based VANETs (Vehicular ad hoc networks) is a new paradigm of vehicular ad hoc networks with the advantages of both vehicular cloud and fog computing. Real-time navigation schemes based on fog-based VANETs can promote the scheme performance efficiently. In this paper, we propose a secure and privacy-preserving navigation scheme by using vehicular spatial crowdsourcing based on fog-based VANETs. Fog nodes are used to generate and release the crowdsourcing tasks, and cooperatively find the optimal route according to the real-time traffic information collected by vehicles in their coverage areas. Meanwhile, the vehicle performing the crowdsourcing task can get a reasonable reward. The querying vehicle can retrieve the navigation results from each fog node successively when entering its coverage area, and follow the optimal route to the next fog node until it reaches the desired destination. Our scheme fulfills the security and privacy requirements of authentication, confidentiality and conditional privacy preservation. Some cryptographic primitives, including the Elgamal encryption algorithm, AES, randomized anonymous credentials and group signatures, are adopted to achieve this goal. Finally, we analyze the security and the efficiency of the proposed scheme.
Highlights
Traffic congestion in crowded urban areas has had a number of negative effects on society, such as wasting motorists’ time, increasing air pollution from the wasted fuel, and creating a higher chance of collisions, etc
We proposed a secure and privacy-preserving real-time navigation system based on fog-based vehicular ad hoc networks (VANETs)
We utilized the real-time traffic information to guid the vehicle to a desired destination in a distributed way: fog nodes generate the spatial crowdsourcing task to collect real-time road conditions
Summary
Traffic congestion in crowded urban areas has had a number of negative effects on society, such as wasting motorists’ time, increasing air pollution from the wasted fuel, and creating a higher chance of collisions, etc. RSUs have limited computation and storage capability, while real-time navigation systems based on crowd sourcing require complex computation and large storage This challenge has motivated researchers to investigate the new paradigm of VANETs. Recently, the vehicular cloud has been proposed for big data processing and complicated intelligent analysis on VANET environments [4,5,6]. We proposed a secure and privacy-preserving navigation scheme (SPNS) in fog-based VANETs, which use spatial crowdsourcing to collect real-time traffic information and analyze the collected data to provide real-time navigation services to drivers. The proposed scheme can ensure the conditional privacy preservation of the vehicles (or drivers), which is regarded as the basic security requirement in VANET communications [8,9,10,11].
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