An Algorithm to Detect Malicious Activity in Dynamic Vanet Environment

Abstract

AbstractThe technology of vehicle ad hoc networks (VANETs) evolved from that of mobile ad hoc networks (MANETs), which help to improve the performance of the transportation area. Because of its benefits in assuring traffic safety and preventing accidents, this technology is gaining a lot of traction. Furthermore, a VANET exhibits self-organization, fast topological changes, and frequent link disconnection, all of which might pose problems. A very effective technique is necessary to mitigate these concerns; hence, this work has used a firefly optimization algorithm (FOA) and a whale optimization algorithm (WOA) as a hybrid model. As a result, the developed model is known as HFWOA-VANET, and it combines the benefits of both metaheuristic methods and is used to improve VANET performance. This procedure is primarily based on the analysis of each vehicle’s Quality of Service (QoS) criteria. As a result, the vehicle’s performance may be determined, allowing for better service under the VANET platform. This study is implemented on the NS2 platform, and the results are examined to ensure that the suggested model performs as expected. Furthermore, the model’s performance is compared to that of existing technology; as a result, the proposed model can be assured to be the most effective technique in terms of performance metrics.KeywordsVANETClusterFOAWOAQOSQMM-VANETHFWOA-VANET