Trust on wheels: towards secure and resource efficient IoV networks

The emerging yet promising paradigm of Internet-of-Vehicles (IoV) has received considerable attention in the recent years as an integral and indispensable constituent of the modern intelligent transportation systems, wherein safety-critical information is exchanged amongst the connected vehicles via a high-bandwidth, low-latent vehicle-to-everything communication system so as to ensure road safety and highly efficient traffic flows. Therefore, it is of utmost importance to ensure the authenticity and reliability of such safety messages together with the legitimacy of the vehicles disseminating the same. This necessitates instituting a state-of-the-art trust-based mechanism in order to not only identify but to further evict the malicious vehicles from an IoV network. The risk augments if a malicious vehicle (via its malign attempts) assume the role of a cluster head in a cluster, thereby jeopardizing the safety of both the occupants of the vehicles and vulnerable pedestrians. Hence, intelligent algorithms with a potential to opt for both a trusted and a resource efficient cluster head needs to be put into place to guarantee the overall security and resource efficacy of the corresponding cluster. Accordingly, in this paper, we have envisaged a scalable hybrid trust-based model which introduces a composite metric encompassing the weighted amalgamation of a vehicle’s computed trust score and its corresponding available resources in order to ensure that the stringent performance requirements of the safety-critical vehicular applications are fully met. A Hungarian algorithm-based role assignment mechanism has been subsequently envisaged for the selection of an optimal cluster head, proxy cluster head, and followers amongst the members of a vehicular cluster for maximizing its overall efficacy. Furthermore, the notion of an adaptive threshold has been proposed in order to identify and subsequently eliminate the smart malicious vehicles from an IoV network in a timely manner, i.e., as soon as they start exhibiting an adverse behavior, to guarantee that the network is not manipulated for any malicious gains. Extensive simulations have been carried out and performance analysis demonstrates the efficaciousness of our proposed scheme.