A Heuristic Distributed Scheme to Detect Falsification of Mobility Patterns in Internet of Vehicles

Autonomous vehicles in the Internet of Vehicles (IoV) have the ability to generate their mobility pattern in advance and share it with other vehicles or a central location. This information enables traffic systems to be aware of future traffic flow. However, the current classical traffic systems barely consider the spatial dependency of traffic data, and more advanced systems suffer from storage limitations. These problems will be exacerbated by the falsification of traffic data through corresponding data attacks, such as position forging attacks. This, in turn, negatively impacts the performance of traffic management systems. This paper proposes a heuristic distributed scheme (HIDE) to validate the mobility pattern of vehicles by penalizing or rewarding vehicles based on the contacts’ conformation. HIDE enables every vehicle to consider the claimed mobility pattern of every neighboring vehicle that is exchanged, and assigns a penalty or reward to each received mobility pattern and share with cloudlets via road side units (RSUs). These calculations are based on an efficient time-homogeneous semi-Markov process (THSMP) to predict the likelihood of the accuracy of mobility patterns. Cloudlets calculate a weight factor to determine if a vehicle is malicious. The validation results from THSMP reveal that a high correlation is achieved between the theoretical model and simulation. Also, the results show that the model fairly identifies the malicious vehicles and assigns a low weight of impact on them compared to normal vehicles