Risk-based decision methods for vehicular networks

Vehicular networks play a key role in building intelligent transport systems for smart cities. For the purpose of achieving traffic efficiency, road safety, and traveler comfort, vehicles communicate and collaborate with each other as well as with the fixed infrastructure. In practice, not all vehicles are trustworthy. A faulty or malicious vehicle may forward or share inaccurate or bogus information, which may cause adverse things, such as, road accidents and traffic congestion. Therefore, it is very important to evaluate risk before a vehicle takes any decision. Various risk-based decision systems have already been proposed in the literature. The fuzzy risk-based decision model of vehicular networks is one of them. In this paper, we have proposed various extensions in the fuzzy risk-based decision model to achieve higher robustness, reliability, and completeness. We have presented the theoretical and simulation-based analysis and evaluation of the proposed scheme in a comprehensive manner. In addition, we have analytically cross verified the theoretical and simulation-based results. Qualitative comparison of the proposed scheme has also been presented in this work.