Bacterial Foraging Inspired Mamdani Fuzzy Inference based AODV (BF-MFI-AODV) for VANET Route Management Optimization

In this research, we propose an integrated routing protocol termed Bacterial Foraging inspired Mamdani Fuzzy Inference based AODV (BF-MFI-AODV) for Vehicular Ad-hoc Networks (VANETs), which combines Mamdani Fuzzy Inference System (MFIS) and Bacterial Foraging Optimization (BFO) techniques. The protocol aims to address the challenges of dynamic and unpredictable network conditions in VANETs by leveraging fuzzy logic and bio-inspired optimization principles. BF-MFI-AODV enhances route discovery, maintenance, and optimization mechanisms, resulting in improved adaptability, reliability, and efficiency of communication. Through extensive simulations and real-world experiments, the performance of BF-MFI-AODV is evaluated in terms of packet delivery ratio, end-to-end delay, routing overhead, and network lifetime. Our results demonstrate the effectiveness of BF-MFI-AODV in enhancing the overall performance of VANETs compared to existing routing protocols. The proposed protocol shows promise in providing robust and efficient communication solutions for dynamic vehicular environments, thus contributing to the advancement of intelligent transportation systems.