Robust machine learning-enabled routing for highly mobile vehicular networks with PARRoT in ns-3

Schüler, Cedrik; Patchou, Manuel; Sliwa, Benjamin; Wietfeld, Christian

doi:10.1145/3460797.3460810

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…The suggested algorithm is trained to select the best traditional routing method to apply to the traffic flows in an SDN environment and decide which QoS-based traffic class offers the optimum balance between throughput, packet loss, and rate of rejection. For the purpose of offloading in 5G cellular IoT, the author in [55] proposed reinforcement learning-based V2V routing, which is used to balance each vehicle's energy usage and conserve energy. However, strict energy consumption limits are not imposed on vehicle models.…”

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%

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

Langpoklakpam,

Murry

2023

Comput. Networks Commun.

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“…The network and MAC layers are responsible for identifying the route for which the routing protocols are used. As per the routing algorithms' strategy, the routes are identified, over which the packets transverse towards the destination [35]. Routing Protocols explore the routes for packet transmission and deliver packets to a source's desired destination.…”

Section: Routing Algorithms In Wireless Mesh Networkmentioning

confidence: 99%

Adaptive Routing in Wireless Mesh Networks Using Hybrid Reinforcement Learning Algorithm

Mahajan

Kotecha

2022

IEEE Access

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Wireless mesh networks are popular due to their adaptability, easy-setup, flexibility, cost, and transmission time-reductions. The routing algorithm plays a vital role in transferring the data between the nodes. The network's performance is significantly impacted by the route opted by the algorithm. The router takes the decision to send the packet to the next router as per the policy of that algorithm. So even though that decision does not favor the right path selection, the router tends to follow its policy. This can be avoided by having intelligent routers that can make routing decisions on the fly.This paper presents the QL-Feed Forward routing algorithm(QFFR), a new generation of routing algorithms that combines reinforcement learning based on the Q-learning algorithm with a Feed Forward neural network. This algorithm(QFFR) can learn from the network environment and make routing decisions based on the algorithm's learnings. The AI agent's ability to select the fastest path, which enhances the efficiency of the routing operation, is demonstrated by the working of the suggested QFFR algorithm. This paper also evaluates the performance of traditional algorithms, namely, Ad-hoc On-Demand Distance-Vector, Optimized-Link-State-routing, Destination-Sequenced Distance-Vector and Distance Source routing. The evaluation parameters include throughput, packet delivery ratio, and delay. The parameters are the outcomes of the time the information takes to reach from source to destination. This analysis highlights the improvement in the routing decision ability of a router. As per analysis, Ad hoc On-Demand Distance Vector Algorithm outperforms with throughput 723.13 Kbps, delay 343.73 ns. Q-learning agent identifies the route and reaches the destination in average of 3.7s in non-grid architecture. The Q-learning agent takes 0.49sec with a grid size ten by ten and 0.53sec in three by four grid size. The suggested QFFR takes 7.62s score-over time with stable, consistent performance. INDEX TERMSDeep Learning , Reinforcement Learning, Q-learning , Markov Decision Process , routing algorithms, wireless mesh networks I. INTRODUCTION D URING this era of digital development, networks play a critical role. Figure 1 shows Wireless Mesh Networks with a mesh architecture. Wireless mesh networks' key advantages are their versatility and customizing capabilities. Any future modifications would be simple to accommodate, resulting in lower expenses and upkeep of the network. Wireless mesh networking is a recent development that emerged from a decade of Ad-hoc networking development [1]. A wireless mesh network (WMN) is a decentralized network system built on existing wireless technology, namely the 802.11 standards, that works on an Ad-hoc communication mode [2] [3]. The wireless mesh network is ideal for nextgeneration communication with its flexibility and extensive coverage. Multi-radio mesh routers and single-radio mesh clients form Wireless Mesh Networks(WMN) [4]. As a result, such networks are developed by connecting wireless access...

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Learning an adaptive forwarding strategy for mobile wireless networks: resource usage vs. latency

Manfredi,

Wolfe,

Zhang

et al. 2024

Mach Learn

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Robust machine learning-enabled routing for highly mobile vehicular networks with PARRoT in ns-3

Cited by 5 publications

References 15 publications

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

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

Adaptive Routing in Wireless Mesh Networks Using Hybrid Reinforcement Learning Algorithm

Learning an adaptive forwarding strategy for mobile wireless networks: resource usage vs. latency

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