Design Of An Iterative Method For Congestion Control In Wireless Networks Integrating Bacterial Foraging Optimizer, Ensemble Classification, And Q-Learning

Abstract

The ever-growing need for seamless data transmission in wireless networks indicates a significant requirement for efficient congestion control mechanisms. Conventional congestion control approaches suffer major drawbacks due to low responsiveness, primarily owing to the inherently dynamic and unpredictable environment of wireless networks. Static parameters characterize these traditional methods, making them unable to adapt to real-time network dynamics, and hence their performances turn out to be suboptimal. In this regard, presented are non-traditional approaches from innovation in the Bacterial Foraging Optimizer (BFO) model that synergizes the ensemble classification method with aid from Multilayer Perceptron (MLP) and Logistic Regression (LR), and Q-Learning for path optimization. The BFO, influenced by the foraging behavior of Escherichia coli bacteria, dynamically determines distinct paths within a network, effectively bypassing congested routes. The bioinspired algorithm, by mechanisms of chemotaxis, reproduction, and elimination-dispersal, efficaciously scours through the search space and effectively finds good network paths, surpassing static routing approaches. Meanwhile, the ensemble classification strategy comprising MLP and LR predicts network congestion by considering a range of features, such as path length, traffic load, and historical congestion data samples. This integrated approach strengthens congestion prediction accuracy as a result of integrating the strengths of individual classifiers and mitigating their respective weaknesses. On top of that, the implementation of Q-Learning for real-time path optimization is another major innovation, where an optimal path is selected based on continuously feeding back from the network. This strategy will ensure that the suggested model shall remain responsive to variations in the network, which is a dynamic environment. With the synergy of all the involved methods, holistic approaches toward the management of congestion have been expressed, considering the multi-faceted challenges from detection to cure. This model not only demonstrates superior adaptability and scalability, pertinent for large-scale wireless networks, but also boasts computational efficiency conducive to real-time applications. This implementation shall bring out great improvements in network performance indices like packet delivery ratio, end-to-end delay, and throughput and thus provide an opportunity to surpass conventional static congestion control mechanisms. The impact of this paper ranges from academic contributions to practical implications in the area of wireless communication. In that case, this research will provide a strong framework for reliable and efficient operation of wireless networks, provided that demands from modern digital communication systems persist. In this way, this paradigm shift of congestion control strategies reflects a landmark in the evolution of management of wireless networks.