MLPRS: A Machine Learning-Based Proactive Re-Routing Scheme for flow classification and priority assignment

Gunavathie, M A; Umamaheswari., S

doi:10.1016/j.jer.2023.100075

Cited by 2 publications

(1 citation statement)

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“…In order to optimize connection stability, range and to increase the packet delivery rate of the IoV, the sending node uses the multi-weight decision algorithm for decision-making process [64]. The author in [65] aims to solve dynamic load balance problem in real-time topology and improve the QoS of the system using the proximity of each of the paths and priority-based categorization. The suggested approach keeps track of each link's load and, if necessary, re-routes traffic in real-time.…”

Section: Reinforcement Learning Based Routing Algorithmmentioning

confidence: 99%

Review on Machine Learning for Intelligent Routing, Key Requirement and Challenges Towards 6G

Langpoklakpam,

Murry

2023

Comput. Networks Commun.

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The constant desire for faster data rates, lower latency, improved reliability, global device integration, and pervasiveness are some of the factors driving the development of next-generation communication systems. Sixth-generation (6G) networks have received a lot of attention from the industry and academics as fifth-generation (5G) communications are being rolled out globally. With the proliferation of smart devices and the Internet of Things (IoT), 6G networks will require ultra-reliable and low-latency communication. Routing protocols have a significant role in improving the performance of a network. Traditional routing techniques will have difficulty coping with the highly complex and dynamic 6G environments. Recently, machine learning (ML), a key component of artificial intelligence, is emerging as the key to managing complex and dynamic networks efficiently. However, there are still several significant challenges that need to be addressed. In this paper, we provide an overview of current machine-learning techniques used in network routing. Lastly, we highlight open research problems that need to be addressed and prospects for future research.

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Section: Reinforcement Learning Based Routing Algorithmmentioning

confidence: 99%