Intelligent Traffic Congestion Control in MANETs: A Comparative Analysis of Neuro-Fuzzy and Metaheuristic-Enhanced Routing Strategies

Abstract

Mobile Ad Hoc Networks (MANETs) are well known to suffer from congestion due to their decentralized architecture, variable topology, and limited resource availability. The conventional shortest-path routing protocols were typically found to be the least efficient for packet delivery, and caused a slower latency when a great deal of traffic is occurring due to the fact that they have basically just used hop count in deciding on routing without taking into account the current state of the network. Although Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are used to predict congestion and improve routing strategies, it is still reliant on how membership function parameters are set. We compare three different routing methods in this study: the Shortest Path First (SPF) baseline model, the conventional ANFIS, and an advanced form of ANFIS with the Firefly Algorithm (Firefly-ANFIS) designed for congestion control in MANETs. We build a simulation environment in MATLAB with 25 mobile nodes, measured with a dataset of traffic records of 1,200 instances. Assessment metrics were congestion index, packet success rate, end-to-end latency and hop count as well as traffic overhead statistics and transmission time. The findings show that Firefly-ANFIS led to better results on all metrics, including the lowest average congestion index (0.043), minimal average end-to-end latency (0.1040 s), an average hop count as low as 2.45, and the lowest traffic overhead compared to SPF baseline and standard ANFIS models. Despite a significantly improved packet success rate of 92.76% with standard ANFIS, it was also faced with increased worst-case latencies associated with unnecessary route detours in bursty conditions. Firefly techniques provided significant mitigation through optimized tuning processes for membership function parameters, allowing for more standardized routing choices to be delivered to all member routing types. The conclusions of the present study show that combining metaheuristic optimization methods with neuro-fuzzy methods for predicting congestion can provide a better balance between reliability gains and latency reduction using dynamically influenced systems typical of MANETs.