A QoS Perception Routing Protocol for MANETs Based on Machine Learning
Keywords:
AODV, Attack, Machine learning (ML), ML-AODV, MANETs, mobile node, SVM, Routing protocol, DSDVAbstract
Machine learning (ML) approaches facilitate the acquisition of knowledge by a system and promote its capacity to adapt to the environment, relying on a multitude of logical and statistical processes. The primary objective of ML is to identify intricate patterns and derive decisions from the obtained outcomes. A range of ML methods have been used for the purpose of enhancing the security of mobile ad-hoc networks (MANETs). The recent progress in wireless communication has prompted researchers to focus their efforts on the development of MANETs. These networks include nodes communicating with one other in order to provide real-time entertainment services as required. Nevertheless, the establishment of safe routing in MANETs remains a formidable challenge, mostly attributed to the wireless connection and decentralized design of these networks. The Ad-hoc On-demand Distance Vector (AODV) routing protocol is extensively used in MANETs due to its broad range of applications, commendable performance, and scalability. However, the AODV routing protocol is considered to be a non-optimal solution since it offers simply an alternative route rather than an optimized one. This research presents a suggested ML based AODV Routing Protocol (ML-AODV) for the purpose of mitigating flooding and blackhole attacks in MANETs. The assessment of the suggested methodology is conducted using the NS-2 simulator and compared to established routing frameworks. This work aims to conduct a comparative analysis and investigation of the AODV, DSDV, and ML-AODV protocols. The analysis will primarily focus on evaluating the performance of these protocols using different metrics, including throughput (TP), packet delivery ratio (PDR), average end-to-end latency (E2EL), packet loss rate (PLR), and energy consumption (EC). The results indicate that the performance of ML-AODV outperforms that of DSDV and AODV. The ML-AODV algorithm demonstrates enhanced performance and reliability compared to previous techniques, while significantly reducing latency, routing overhead (RO), and PLR.
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