A Secure Parking Navigation System for Autonomous Vehicles Communication

Abstract

Finding a parking space in a crowded environment, such as downtown or a mall, is a constant source of frustration and delays for drivers of autonomous vehicles (Aut-Vs), particularly during peak hours. There-fore, sharing real-time parking information is particularly beneficial as it helps them locate available places quickly. To surmount this obstacle, sophisticated and effective parking strategies are needed. Therefore, this work suggests that sharing data about parking spaces between Aut-Vs will enhance parking navigation systems and congestion avoidance. For example, if a number of Aut-Vs are ready to share their parking spaces with others before leaving their spots, this will promote parking navigation services’ efficiency, reduce time and energy consumption. However, despite sharing data being beneficial, this massively relies on provider Aut-Vs agreeing to sharing their parking spots data with others. However, sharing data will result in various challenges, including incentive and security as well as latency issues. For instance, Aut-Vs may not wish to share their data with zero benefit since doing so will incur them more computation and communication efforts and security issues. Therefore, this paper aims to propose a novel security incentive mechanism for improving the parking navigation system for the Aut-Vs paradigm. However, even with low latency and minimal cost, data sharing between Aut-Vs still has mobility issues. For this reason, the fog caching (FC) system has been created to boost data sharing effectiveness while maintaining high levels of mobility and flexibility. This paper has designed a secure incentive mechanism based on an FC network to proposed a secure parking navigation (SPN) system for the Aut-Vs paradigm. The proposed SPN combines auction methodology with attribute-based encryption algorithm to design a secure incentive mechanism which guarantees the security and privacy of sharing parking data. Security analysis shows that our work is able to achieve sharing parking with full guarantee of privacy protection. Performance evaluation also shows that the proposed scheme for the SPN system is highly feasible and scalable.