Enhancing Congestion Control and QoS Scheduling Using Novel Rate Aware-Neuro-Fuzzy Algorithm in MANET

Abstract

Mobile Ad Hoc Networks (MANET) provides a vibrant atmosphere wherein data may be substituted deprived of the necessity of human authority or a centralized server, as long as nodes work together for routing. As long as security throughout the multipath routing protocol and data transfer over many routes in a MANET is a difficult problem, this work offers a message security technique. This study presents the congestion control and QoS scheduling mechanism. The goal of this study is to examine standardized MAC protocols on MANET, to measure performance under various node densities and MAC protocols. Initially, this work presents the Centralized Congestion Detection method to detect congestion with baseline parameters. Accordingly, the congestion is avoided using Novel Rate Aware-Neuro-Fuzzy based Congestion Controlling strategy. This method effectively controls the congestion in the Network. This mechanism has been proposed which defines three levels of congestion based on which the data rate, throughput, overhead and delay. However, after controlling the congestion, the optimal routes are given to the packets by proposing an Ambient Intelligence-based Ant colony optimization quality-aware energy routing protocol (AIACOAR). This method finds the most efficient route to a destination and decreases the time and energy required. Accordingly, for securing the network against malicious attacks, an Elliptic Curve Cryptography (ECC) encryption mechanism is presented. Consequently, the multihop scheduler performs QoS-based scheduling in MANET. Schedulers in MANET take into account various QoS parameters such as end-to-end packet delay, packet delivery ratio, flow priority, etc. The proposed method is implemented using Matlab software, and the evaluation metrics are PDR, jitter, congestion detection time, delay, route selection time, and throughput. The performance of the proposed method is compared to the existing AIFSORP and LF-SSO techniques. While compared to these methods, the proposed method’s performance is improved in terms of PDR, delay, throughput, etc. The PDR value of the proposed method reaches approximately 99%, and it produces a very low delay. This produces reliable route discovery, optimized congestion control, and better QoS scheduling, therefore, these improve the system performance. In future, a recent bio-inspired technique is presented to even more minimize energy consumption and further improve the system's performance.