Innovative Fuzzy Logic Methods for Congestion Control in Vehicular Ad-Hoc Networks

Abstract

This research investigates the use of state-of-the-art fuzzy logic-based techniques for congestion control in vehicular ad hoc networks (VANETs), as VANETs are dynamic networks with high mobility and frequent topology changes. In time, it can therefore be difficult to maintain optimum performance using specific signal management strategies. The recommended algorithm uses real-time data and expert knowledge to dynamically change the transmission rate in response to changing network conditions. The main goal is to increase the number of s along with the fact that the method promises to be flexible and scalable in various VANET settings. The fuzzy logic method shows good results through several detailed simulations and performance comparisons with machine learning-based traditional ones. Hybrid An interesting algorithm is recommended technique achieves speeds of 20.1 Mbps, surpassing the current techniques with impressive growth above. Moreover, Packet loss is reduced to 4.7%, promoting reliable communication. And latency is reduced to 160 milliseconds, guaranteeing faster data transfers. The scalability of the algorithm (score 9) makes it ideal for expanding network demands in smart transportation systems. While resilient (score 9), future research directions include integrating additional environmental factors such as weather and road conditions. To further improve decision-making that guarantees smooth performance across a wide range of traffic conditions. By incorporating adaptive learning techniques into the fuzzy logic framework, the system may be able to develop and get better over time. Additionally, investigating hierarchical control structures might provide multi-layered network settings with more effective management. Validating the simulation results will require real-world installations and resolving privacy and security issues will be essential to guaranteeing dependable and secure operation in real-world situations. The outcomes demonstrate how well the suggested strategy works to achieve increased throughput, reduced latency, and less packet loss while maintaining the flexibility and scalability needed for upcoming vehicular networks.