Traffic Monitoring in Self-Organizing VANETs: A Privacy-Preserving Mechanism for Speed Collection and Analysis

Abstract

With the explosive growth of vehicles, traffic monitoring has garnered significant attention in recent years. Collecting vehicular speed is an effective way to monitor traffic conditions and help vehicles to find optimal routes. However, further progress may be impeded due to users' privacy concerns. In addition, traffic monitoring is more difficult in a self-organizing VANET, since there is no centralized entity to collect and analyze the speed information. In this article, we mainly focus on privacy-preserving traffic monitoring in self-organizing VANETs. To address the unique features and security requirements of VANETs, we incorporate the homomorphic encryption, data perturbation, and super-increasing sequence in the proposed novel solution to resolve the challenges of efficient and privacy-preserving traffic monitoring. Security analysis shows that not only can our solution preserve vehicles' identities, locations, and data privacy, but it is also effective in mitigating collusion attacks. Moreover, experimental results confirm the efficiency of our solution in terms of computation and communication costs. Last but not least, some interesting challenges along with potential solutions are discussed, aiming to attract more research in this emerging area.