A time-aware trust management heuristic for the Internet of Vehicles

The emerging yet promising notion of trust amalgamated with the paradigm of the Internet of Vehicles (IoV) has been of significant interest for researchers in both industry and academia. Trust management models are being widely employed in diverse domains seeking to mitigate multifarious insider attacks where dishonest entities counterfeit or alter the messages from legitimate users, subsequently, endangering the lives of drivers, passengers, and vulnerable pedestrians. While vehicles form opinions towards the fellow network participants, numerous influencing attributes in reference to the interactions among the vehicles are aggregated to develop a final belief on a target vehicle. The significance of these trust impacting attributes is often depicted by manually assigning a weight to each parameter. The aim of this research is to address the challenges of quantification of the influencing trust attributes and the weights associated with these parameters, subsequently, quantifying the aggregated trust. Furthermore, the time-based behavior of the aggregated trust along with the contributing parameters is also analyzed in this paper to study the behavior of each vehicle and to identify suitable trust-based patterns for safety-critical and non-safety vehicular applications. Performance analysis demonstrates that the trust values computed via the envisaged trust model are lower than those calculated via the benchmark scheme.