Learning for Multiple-Relay Selection in a Vehicular Delay Tolerant Network

Dong, Yuzhen; Zhang, Fuquan; Joe, Inwhee; Lin, Haifeng; Jiao, Wanguo; Zhang, Yan

doi:10.1109/access.2020.3026373

Cited by 13 publications

(6 citation statements)

References 30 publications

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“…TQL evaluates all links by Q-function and selects the link with the maximum Q-function each time to transmit the signals and thus achieves reliable communications. Due to the excellent reliability performance of the proposed scheme based on TQL, the authors in [ 24 , 25 ] extend this work to vehicular networks, D2D communications and achieve reliable communications in vehicular networks and D2D communications. Although these TQL-based schemes can achieve reliable communications, too much storage space is needed to store the Q-table and a high time cost to look up the Q-function in the Q-table as all Q-functions have to be stored in the Q-table.…”

Section: Related Workmentioning

confidence: 99%

Deep Q-Learning-Based Buffer-Aided Relay Selection for Reliable and Secure Communications in Two-Hop Wireless Relay Networks

Zhang

Liao

et al. 2023

Sensors

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This paper investigates the problem of buffer-aided relay selection to achieve reliable and secure communications in a two-hop amplify-and-forward (AF) network with an eavesdropper. Due to the fading of wireless signals and the broadcast nature of wireless channels, transmitted signals over the network may be undecodable at the receiver end or have been eavesdropped by eavesdroppers. Most available buffer-aided relay selection schemes consider either reliability or security issues in wireless communications; rarely is work conducted on both reliability and security issues. This paper proposes a buffer-aided relay selection scheme based on deep Q-learning (DQL) that considers both reliability and security. By conducting Monte Carlo simulations, we then verify the reliability and security performances of the proposed scheme in terms of the connection outage probability (COP) and secrecy outage probability (SOP), respectively. The simulation results show that two-hop wireless relay network can achieve reliable and secure communications by using our proposed scheme. We also performed comparison experiments between our proposed scheme and two benchmark schemes. The comparison results indicate that our proposed scheme outperforms the max-ratio scheme in terms of the SOP.

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Section: Related Workmentioning

confidence: 99%

Deep Q-Learning-Based Buffer-Aided Relay Selection for Reliable and Secure Communications in Two-Hop Wireless Relay Networks

Zhang

Liao

et al. 2023

Sensors

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“…Wu et al [29] propose a Bayesian network based approach to predict the movement pattern of vehicles in DTN scenarios. Dong et al [30] discuss the incentive problem in VDTN routing, and use the Q-learning algorithm to calculate the credit value of each candidate node. Both [29] and [30] do not consider the link quality between two nodes in the forwarding decision.…”

Section: A Routing Protocols In Vdtnsmentioning

confidence: 99%

“…Dong et al [30] discuss the incentive problem in VDTN routing, and use the Q-learning algorithm to calculate the credit value of each candidate node. Both [29] and [30] do not consider the link quality between two nodes in the forwarding decision. Xia et al [31] study the use of fog computing in delay-tolerant communications for Internet of vehicles.…”

Section: A Routing Protocols In Vdtnsmentioning

confidence: 99%

A Routing Protocol for UAV-Assisted Vehicular Delay Tolerant Networks

Yoshinaga

et al. 2021

IEEE Open J. Comput. Soc.

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Vehicular delay tolerant networks (VDTNs) enable information sharing among mobile nodes in scenarios where cellular base stations are unavailable and the connections between the mobile nodes are intermittent. While unmanned aerial vehicles (UAVs) have shown to improve the performance of VDTNs, existing routing protocols, such as PRoPHET, consider the encounter probability between mobile nodes only, which does not fully address the characteristics of UAVs in route selection. In this paper, we propose a routing protocol, which considers both the encounter probability and the persistent connection time between mobile nodes for each encounter, in UAV-assisted VDTNs. By introducing the persistent connection time in route selection, the proposed protocol is able to evaluate the stability of a communication link more accurately in the UAV-assisted VDTN environment. The proposed protocol is evaluated using realistic simulations by comparing it with existing baselines. The simulation results verify that the proposed protocol can improve the reliability of message forwarding while reducing network overhead and end-to-end delay.

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“…Typically, this type of node takes advantage of and utilizes the services of several other nodes to serve their interests. The selfish node, for instance, is the one that drops packages without sending them at least once [ 11 , 12 ]. This type of node often wastes a lot of network resources and, therefore, can impact the efficiency of well-behaved nodes.…”

Section: Introductionmentioning

confidence: 99%

EMS: Efficient Monitoring System to Detect Non-Cooperative Nodes in IoT-Based Vehicular Delay Tolerant Networks (VDTNs)

Rehman¹,

Zubair²,

Qasim

et al. 2022

Sensors

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Since several Internet of Things (IoT) applications have been widely deployed on unstable wireless networks, such as the Delay Tolerant Network (DTN), data communication efficiency in DTN remains a challenge for IoT applications. Vehicular Delay Tolerant Network (VDTN) has become one of DTN’s potential applications, in which the network experiences connectivity interruption due to the lack of an end-to-end relay route. VDTNs focus on node cooperation to achieve this goal. As a result, it is essential to ensure that almost all network nodes adopt the protocol to preserve network performance. This is a challenging task since nodes may diverge from the basic protocol to optimize their effectiveness. This article presents an Efficient Monitoring System (EMS) to detect and respond to just selfish nodes to minimize their entire network and data communication efficacy. The scheme is based on a network-wide cooperative sharing of node reputation. It is also necessary to increase overall network efficiency by tracking selfish nodes. The NS-2 simulator is used to run this experimental setup. Simulation results indicate that the proposed scheme performs better in terms of probability of package delivery, package delivery delay, energy consumption, and amount of packet drops. For 80% selfish nodes in the network, the packet delivery of EMS is 37% and 31% better than SOS and IPS. Similarly, the average delivery delay of EMS is 22% and 18% lower than SOS and IPS when 80% selfish nodes are incorporated in the network.

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Learning for Multiple-Relay Selection in a Vehicular Delay Tolerant Network

Cited by 13 publications

References 30 publications

Deep Q-Learning-Based Buffer-Aided Relay Selection for Reliable and Secure Communications in Two-Hop Wireless Relay Networks

Deep Q-Learning-Based Buffer-Aided Relay Selection for Reliable and Secure Communications in Two-Hop Wireless Relay Networks

A Routing Protocol for UAV-Assisted Vehicular Delay Tolerant Networks

EMS: Efficient Monitoring System to Detect Non-Cooperative Nodes in IoT-Based Vehicular Delay Tolerant Networks (VDTNs)

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