TB-EDA: A Trust-Based Event Detection Algorithm to Detect False Events in Software-Defined Vehicular Network

False event propagation is one of the main reasons of disruption of a network. So, false events need to be detected early to protect the network from collapsing. In this paper, a trust-based event detection algorithm (TB-EDA) is proposed for a software-defined vehicular network (SDVN). In the proposed algorithm, an evaluator node first compares the trust values of its neighbor vehicles with the calculated threshold trust value. If this condition satisfies, then the evaluator node checks the correctness of the received information using the similarity method. After satisfying these both conditions, then only the evaluator node accepts information received from its surroundings vehicles and acts based upon it. If any of the two conditions is not satisfied, then the evaluator node discards the message and stops spreading of such false event messages in the network. Due to the centralized structure and open flow nature, we select the SDVN-based network model. Our work is implemented using Veins hybrid simulator, where OMNeT++ acts as the network simulator and simulation of urban mobility (SUMO) as a road traffic simulator. We check the performance of our work by considering various parameters, such as detection accuracy, detection time, and energy consumption.