Vehicular networks have emerged as a promising technology for the development of traffic management systems in smart cities. They are expected to revolutionize a variety of applications such as traffic monitoring and pay-as-you-drive services. Recently, the notion of road pricing has become crucial in most big cities as it contributes in road congestion avoidance, fuel consumption saving and pollution reduction. However, as the road pricing systems need trip data to invoice citizens, it is vital to ensure geolocation privacy while keeping drivers honest. In this paper, we propose a security approach for Smart Road Pricing (SRP) systems, which prevents toll evasion violations. The proposed approach operates under a fully distributed thresholdbased control system to detect fraudulent drivers trying to cheat on their tolls. The accused drivers are reported to the toll server in order to take the appropriate countermeasures. Through the security analysis, we show the robustness of the proposed approach against a range of potential attacks. We also evaluate the proposed approach through simulations considering important metrics, namely, the storage and communication overheads. The proposed approach shows better performance results in comparison to the existing approaches. Furthermore, we evaluate the proposed approach efficiency in terms of detection precision, where it demonstrates promising results.

The emergence of modern Intelligent Transportation Systems (ITS) based on vehicular networks (VNs) has diversified the range of applications provided to drivers and passengers during their road trips. To fully benefit from these services, vehicles need however to share their geolocation data making location privacy a critical issue. Hence, to mitigate these threats and promote VNs use in developing Smart Cities, it is vital to guarantee conditional anonymity to vehicular users. In this paper, we propose an efficient pseudonym changing strategy for privacy-preserving in VNs. The proposed approach uses threshold cryptography properties to provide moving vehicles with pseudonyms, allowing them to communicate anonymously during their journey. These vehicles though can be traced back by trusted authorities in case of misbehavior actions. We evaluate the efficiency of the proposed approach by comparison with concurrent solutions, where it demonstrates the best results in both congested road traffic and dense infrastructure deployment scenarios.

Medical Body Area Network (MBAN) has emerged as a promising solution for monitoring patient activities and actions, and supports a lot of healthcare applications. A MBAN includes a set of sensor nodes deployed such, they can be located on, in, or around the patient body. They are used to monitor physiological signs, which are transmitted then to medical servers without hampering the patient activities. Security is one of the main challenging issues in MBANs since the data nature is highly sensitive. In order to ensure the reliable gathering of patient critical information, it is vital to provide authentication to prevent an attacker from impersonating legitimate sensor nodes. In this paper, we propose a patient body motion based authentication solution. The routine activities, as walking or running, are characterized through a generic model allowing to identify the patient sensor nodes. Through the security analysis, we show its robustness against the well known attacks. In addition, we develop an analytical model to measure the impact of physical and logical attacks on the proposed solution with comparison to the existing protocols. We also evaluate the proposed solution through simulations with respect of important criteria, namely the transmission overhead, response time and energy consumption. The proposed solution demonstrates the best results in performance with comparison to the existing protocols. Furthermore, we have developed a prototype of the proposed solution, where it demonstrates promising results in terms of true acceptation and false rejection.

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