Q<sup>2</sup>-Routing : A Qos-aware Q-Routing algorithm for Wireless Ad Hoc Networks

Hendriks, Thomas; Camelo, Miguel; Latré, Steven

doi:10.1109/wimob.2018.8589161

Cited by 24 publications

(14 citation statements)

References 15 publications

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“…ML algorithms should learn the correlation between traffic inputs and link conditions to predict or determine a path for the incoming traffic. Recent studies that attempt to improve routing decisions in a network are mostly NN based, such as in [22], [25], [101]- [103], followed by works that adopt RL, such as in [35], [102], [104]. Those recent studies that exploit other ML algorithms are further elaborated in this section.…”

Section: ML For Improving Routing Decisions In Communication Netmentioning

confidence: 99%

“…MARL routing algorithms can achieve better optimization yet incur a high communication overhead, slowly converge under dynamic networks, and lack QoS support. [104] proposed an enhanced version of Q-routing, namely, the Q2routing algorithm, which merges the existing wireless routing techniques and further enhances them by using the MARL domain for an ad-hoc wireless network. Q2-routing is a hybrid routing algorithm where the nodes make routing decisions by choosing the neighbor associated with the optimal Q-value for a given destination as the next hop.…”

Section: B Rl-based Routing Protocolmentioning

confidence: 99%

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Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

et al. 2021

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Communication networks are expanding rapidly and becoming increasingly complex. As a consequence, the conventional rule-based algorithms or protocols may no longer perform at their best efficiencies in these networks. Machine learning (ML) has recently been applied to solve complex problems in many fields, including finance, health care, and business. ML algorithms can offer computational models that can solve complex communication network problems and consequently improve performance. This paper reviews the recent trends in the application of ML models in communication networks for prediction, intrusion detection, route and path assignment, Quality of Service improvement, and resource management. A review of the recent literature reveals extensive opportunities for researchers to exploit the advantages of ML in solving complex performance issues in a network, especially with the advancement of softwaredefined networks and 5G.

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Section: ML For Improving Routing Decisions In Communication Netmentioning

confidence: 99%

Section: B Rl-based Routing Protocolmentioning

confidence: 99%

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

et al. 2021

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“…Q2-R (Qos-aware Q-Routing)- [95] proposed an adaptation to Q-routing to optimize the overhead and the quality of discovered paths, while providing soft QoS in WANETs. Q2-R follows three steps: Bootstrapping, Learning, and Data routing.…”

Section: R-crs (Natg) (Rl-based Cooperative Relay Selection) -mentioning

confidence: 99%

Reinforcement Learning Based Routing in Networks: Review and Classification of Approaches

Mammeri¹

2019

IEEE Access

151

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Reinforcement learning (RL), which is a class of machine learning, provides a framework by which a system can learn from its previous interactions with its environment to efficiently select its actions in the future. RL has been used in a number of application fields, including game playing, robotics and control, networks, and telecommunications, for building autonomous systems that improve themselves with experience. It is commonly accepted that RL is suitable for solving optimization problems related to distributed systems in general and to routing in networks in particular. RL also has reasonable overheadin terms of control packets, memory and computation-compared to other optimization techniques used to solve the same problems. Since the mid-1990s, over 60 protocols have been proposed, with major or minor contributions in the field of optimal route selection to convey packets in different types of communication networks under various user QoS requirements. This paper provides a comprehensive review of the literature on the topic. The review is structured in a way that shows how network characteristics and requirements were gradually considered over time. Classification criteria are proposed to present and qualitatively compare existing RL-based routing protocols.INDEX TERMS Reinforcement learning, communication networks, routing protocols, path optimization, quality of service.

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“…Accordingly, the likelihood of successful deliveries of data packets is predicted. Thomas et al [23] introduced Q 2 − Routing, which is a hybrid routing protocol that integrates on-demand route discovery technique with proactive updates of the available routes. Their routing scheme depends on Multi-Agent Reinforcement Learning (MARL).…”

Section: Background and Related Workmentioning

confidence: 99%

FQ-AGO: Fuzzy Logic Q-Learning Based Asymmetric Link Aware and Geographic Opportunistic Routing Scheme for MANETs

2020

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The proliferation of mobile and IoT devices, coupled with the advances in the wireless communication capabilities of these devices, have urged the need for novel communication paradigms for such heterogeneous hybrid networks. Researchers have proposed opportunistic routing as a means to leverage the potentials offered by such heterogeneous networks. While several proposals for multiple opportunistic routing protocols exist, only a few have explored fuzzy logic to evaluate wireless links status in the network to construct stable and faster paths towards the destinations. We propose FQ-AGO, a novel Fuzzy Logic Q-learning Based Asymmetric Link Aware and Geographic Opportunistic Routing scheme that leverages the presence of long-range transmission links to assign forwarding candidates towards a given destination. The proposed routing scheme utilizes fuzzy logic to evaluate whether a wireless link is useful or not by capturing multiple network metrics, the available bandwidth, link quality, node transmission power, and distance progress. Based on the fuzzy logic evaluation, the proposed routing scheme employs a Q-learning algorithm to select the best candidate set toward the destination. We implemented FQ-AGO on the ns-3 simulator and compared the performance of the proposed routing scheme with three other relevant protocols: AODV, DSDV, and GOR. For precise analysis, we considered various network metrics to compare the performance of the routing protocols. The simulation result validates our analysis and demonstrates remarkable performance improvements in terms of total network throughput, packet delivery ration, and end-to-end delay. FQ-AGO achieves up to 15%, 50%, and 45% higher throughput compared to DSDV, AODV, and GOR, respectively. Meanwhile, FQ-AGO reduces by 50% the end-to-end latency and the average number of hop-count.

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Q²-Routing : A Qos-aware Q-Routing algorithm for Wireless Ad Hoc Networks

Cited by 24 publications

References 15 publications

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

Reinforcement Learning Based Routing in Networks: Review and Classification of Approaches

FQ-AGO: Fuzzy Logic Q-Learning Based Asymmetric Link Aware and Geographic Opportunistic Routing Scheme for MANETs

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Q2-Routing : A Qos-aware Q-Routing algorithm for Wireless Ad Hoc Networks

Cited by 24 publications

References 15 publications

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

Applications of Machine Learning in Networking: A Survey of Current Issues and Future Challenges

Reinforcement Learning Based Routing in Networks: Review and Classification of Approaches

FQ-AGO: Fuzzy Logic Q-Learning Based Asymmetric Link Aware and Geographic Opportunistic Routing Scheme for MANETs

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Q²-Routing : A Qos-aware Q-Routing algorithm for Wireless Ad Hoc Networks