Optimal Congestion Control Mechanism For Intelligent Routing To Improve QoS Using Temporal Deep Learning

Abstract

Congestion in Mobile Ad-hoc Networks (MANETs) leads to connection failures, node loss, and affects network setup. MANETs, lacking permanent infrastructure and central management, suffer from buffer overflow and packet loss under high traffic. Machine Learning (ML) enhances Quality of Service (QoS) in network routing. This paper introduces a congestion control system model with node-level states, control strategies, and network optimization objectives. It analyzes congestion state transitions and real-time control to minimize network delay and congestion cost, deriving optimal strategies using optimal control theory and a Congestion Control Discretization Algorithm (CCDA). Simulation results will show reduced congestion across nodes with CCDA. The impact of parameters like congestion probability and delay weight on network loss is also explored, with our model showing lower total loss than baseline models, providing effective congestion control guidance for deterministic networks.

We also propose an intelligent routing scheme for MANETs with directional antennas, using a spatio temporal deep learning algorithm to predict traffic density in a directional heat map. This aids in selecting optimal paths to avoid congestion and interference. Our optimization algorithm splits paths around congested areas, enhancing QoS. Additionally, we introduce a novel algorithm and buffer management technique to handle congestion, eliminating unnecessary packets, preventing flooding, and maintaining buffer levels by keeping only essential data. These methods improve MANET communication performance, supporting efficient queue management.