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Rogue nodes broadcasting false information in beacon messages may lead tocatastrophic consequences in Vehicular Ad Hoc Networks (VANETs). Previousresearchers used either cryptography, trust scores, or past vehicle data todetect rogue nodes; however, these methods suffer from high processing delay,overhead, and False-Positive Rate (FPR). We propose herein Greenshield'straffic model-based fog computing scheme called Fog-based Rogue Nodes Detection(F-RouND), which dynamically utilizes the On-Board Units (OBUs) of all vehiclesin the region for rogue node detection. We aim to reduce the data processingdelays and FPR in detecting rogue nodes at high vehicle densities. Theperformance of the F-RouND framework was evaluated via simulations. Resultsshow that the F-RouND framework ensures 45% lower processing delays, 12% loweroverhead, and 36% lower FPR at the urban scenario compared to the existingrogue node detection schemes even when the number of rogue nodes increases byup to 40% in the region.

A statistical approach for enhancing security in VANETs with efficient rogue node detection using fog computing