Traffic Congestion Reduction Using Fuzzy Optimization Scheme in Heterogeneous Vehicular Communication Network

VANETs emphasize their similarities and challenges compared to MANETs, particularly concerning dynamic node movement and routing protocols. It highlights existing research efforts, including routing protocol proposals and optimization algorithms, addressing congestion, delay reduction, and resource management. Proposed TCRFOS approach for congestion reduction in VANETs through fuzzy optimization techniques. VANETs, a type of MANETs, pose unique challenges due to the dynamic nature of vehicle movements and network conditions. Existing routing protocols adapted from MANETs struggle to address the rapid changes in node locations and wireless channels. To tackle this, the TCRFOS approach integrates fuzzy logic and resource management to accurately predict resource survival time, optimizing traffic flow and minimizing congestion. Through fuzzification, fuzzy inference, and defuzzification, TCRFOS effectively manages resources, enhancing network efficiency. The document describes the system model, explaining how the method considers aspects such as the likelihood of road collapse and the road utilization ratio to enhance the road network while reducing congestion. Additionally, the fuzzy optimization process is explained, highlighting the components involved and their roles. Results from experimentation using the proposed ERCRMs protocol demonstrate significant improvements in various metrics such as network throughput, delay, routing overhead, transmission accuracy, and energy efficiency compared to existing methods. This underscores the effectiveness of the TCRFOS approach in enhancing communication efficiency, reliability, and energy conservation in VANETs, positioning it as a promising solution for congestion mitigation in dynamic vehicular environments.