Poisson's probability‐based Q‐Routing techniques for message forwarding in opportunistic networks

Sharma, Deepak Kumar; Kukreja, Deepika; Aggarwal, Pranav; Kaur, Manpreet; Sachan, Ayushee

doi:10.1002/dac.3593

Cited by 12 publications

(4 citation statements)

References 22 publications

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“…Recently, more advanced routing algorithms are proposed to extend the baseline Q-Routing into more complex scenarios with enhanced performance. Three successful algorithms include Poisson's probability-based Q-Routing (PBQ-Routing) [26], Delayed Q-Routing (DQ-routing) [27] and Qos-aware Q-Routing (Q 2 -Routing) [28]. PBQ-Routing uses forwarding probability and Poisson's probability for decision making and controlling transmission energy for intermittently connected networks.…”

Section: A Prior Workmentioning

confidence: 99%

Fully-Echoed Q-Routing With Simulated Annealing Inference for Flying Adhoc Networks

Rovira-Sugranes

Afghah

et al. 2021

IEEE Trans. Netw. Sci. Eng.

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Current networking protocols deem inefficient in accommodating the two key challenges of Unmanned Aerial Vehicle (UAV) networks, namely the network connectivity loss and energy limitations. One approach to solve these issues is using learning-based routing protocols to make close-tooptimal local decisions by the network nodes, and Q-routing is a bold example of such protocols. However, the performance of the current implementations of Q-routing algorithms is not yet satisfactory, mainly due to the lack of adaptability to continued topology changes. In this paper, we propose a fullecho Q-routing algorithm with a self-adaptive learning rate that utilizes Simulated Annealing (SA) optimization to control the exploration rate of the algorithm through the temperature decline rate, which in turn is regulated by the experienced variation rate of the Q-values. Our results show that our method adapts to the network dynamicity without the need for manual re-initialization at transition points (abrupt network topology changes). Our method exhibits a reduction in the energy consumption ranging from 7% up to 82%, as well as a 2.6 fold gain in successful packet delivery rate, compared to the state of the art Q-routing protocols 1 .

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Section: A Prior Workmentioning

confidence: 99%

Fully-Echoed Q-Routing With Simulated Annealing Inference for Flying Adhoc Networks

Rovira-Sugranes

Afghah

et al. 2021

IEEE Trans. Netw. Sci. Eng.

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“…• Adaptive Q-Routing with Random Echo and Route Memory (AQRERM) [197]: An extension of the previous work is AQRERM, which improves the performance of the baseline method in terms of the overshoot and settling time of the learning process, as well as the learning stability. • Poisson's probability-based Q-Routing (PBQ-Routing) [198]: This approach uses forwarding probability and Poisson's probability for decision making and controlling transmission energy for intermittently connected networks. Results show that the delivery probability is almost twice bigger than for Q-Routing, and the overhead ratio is reduced to half.…”

Section: B Ai-enabled Routing Protocolsmentioning

confidence: 99%

A Review of AI-enabled Routing Protocols for UAV Networks: Trends, Challenges, and Future Outlook

Rovira-Sugranes¹,

Razi²,

Afghah³

et al. 2021

Preprint

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Unmanned Aerial Vehicles (UAVs), as a recently emerging technology, enabled a new breed of unprecedented applications in different domains. This technology's ongoing trend is departing from large remotely-controlled drones to networks of small autonomous drones to collectively complete intricate tasks time and cost-effectively. An important challenge is developing efficient sensing, communication, and control algorithms that can accommodate the requirements of highly dynamic UAV networks with heterogeneous mobility levels. Recently, the use of Artificial Intelligence (AI) in learning-based networking has gained momentum to harness the learning power of cognizant nodes to make more intelligent networking decisions. An important example of this trend is developing learning-powered routing protocols, where machine learning methods are used to model and predict topology evolution, channel status, traffic mobility, and environmental factors for enhanced routing.This paper reviews AI-enabled routing protocols designed primarily for aerial networks, with an emphasis on accommodating highly-dynamic network topology. To this end, we review the basics of UAV technology, different approaches to swarm formation, and commonly-used mobility models, along with their impact on networking paradigms. We proceed with reviewing conventional and AI-enabled routing protocols, including topology-predictive and self-adaptive learning-based routing algorithms. We also discuss tools, simulation environments, remote experimentation platforms, and public datasets that can be used for developing and testing AI-enabled networking protocols for UAV networks. We conclude by presenting future trends, and the remaining challenges in AI-based UAV Networking, for different aspects of routing, connectivity, topology control, security and privacy, energy efficiency, and spectrum sharing.

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“…As they evaluated Q-routing on their home simulator, they eluded the problem of the noise of measure of the latency. Even on implementation of Q-routing [3]- [5] on network simulator, the problem wasn't mentioned but real if we take a look to quality of service results such as packet delivery ratio and average delivery time.…”

Section: Introductionmentioning

confidence: 99%

Latency filtering for Q-routing on wireless networks

Bitaillou

Parrein

Andrieux

et al. 2021

2021 International Wireless Communications and Mobile Computing (IWCMC)

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Q-routing is inspired by Q-learning, a reinforcement learning algorithm. Originally, it uses latency as routing metric. But, latency can be difficult to estimate especially in a noisy wireless environment. In this paper, we propose to filter the latency measure with a moving average, in order to improve the quality of service metrics such as packet delivery ratio and average delay. We compare our modification to the original Q-routing and use OLSRv2 as reference routing protocol. We observe an improvement of the average delivery time on a wireless grid of 3 % compared to the original Q-routing. On our mobility scenario, the number of routes changes is at least twice lower (from 6 to 3 route changes between the two approaches in this scenario). The gain on QoS metrics depends mainly on the speed of the nodes. These improvements are obtained without making Q-routing more complex as a moving average is added. We provide all the materials to conduct reproducible research on our public git repository.

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Poisson's probability‐based Q‐Routing techniques for message forwarding in opportunistic networks

Cited by 12 publications

References 22 publications

Fully-Echoed Q-Routing With Simulated Annealing Inference for Flying Adhoc Networks

Fully-Echoed Q-Routing With Simulated Annealing Inference for Flying Adhoc Networks

A Review of AI-enabled Routing Protocols for UAV Networks: Trends, Challenges, and Future Outlook

Latency filtering for Q-routing on wireless networks

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