MAC Contention Protocol Based on Reinforcement Learning for IoV Communication Environments

Pei, Zhonghui; Chen, Wei; Du, Luyao; Zheng, Hongjiang

doi:10.1109/icccs52626.2021.9449308

Cited by 6 publications

(5 citation statements)

References 15 publications

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“…The effectiveness of the proposed approach is evaluated with the existing techniques, like NA‐SMT, 1 deep learning, 31 and reinforcement learning 32 …”

Section: Resultsmentioning

confidence: 99%

“…It obtained lower execution cost, but it faced computational complexity issues. Pei et al 32 designed a reinforcement learning‐based method using medium access control (MAC) layer with a window adaptive adjustment policy. It obtained higher performance with the measures of end‐to‐end delay, and network throughput, but faced communication overhead.…”

Section: Literature Surveymentioning

confidence: 99%

“…The integration of IoV communication with fog computing enables data from different environments and service messages pertaining to the area, where the vehicle is located to process the message efficiently 32 . The I2V and V2V communication are designed to send service messages more reliably.…”

Section: Introductionmentioning

confidence: 99%

“…30,31 The integration of IoV communication with fog computing enables data from different environments and service messages pertaining to the area, where the vehicle is located to process the message efficiently. 32 The I2V and V2V communication are designed to send service messages more reliably. The I2V communication can take place through the Uu interface by sharing various resources with cellular users, and the information can be broadcasted at a higher rate by consuming a limited number of cellular resources.…”

mentioning

confidence: 99%

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Fractional mayfly optimization algorithm‐based Infrastructure‐to‐Vehicle and Vehicle‐to‐Vehicle scheduling for service message transmission in IoV‐fog

Lohat

Jain

Kumar

2023

Int J Communication

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Summary For the rising number of vehicles, and the advancement of communication and computation technologies, the perception of Internet of Vehicles (IoV) is introduced. It is utilized to capture vehicle‐based information that includes road description, road congestion, vehicle speed, and location. However, this information is important, and it showed more benefits in different ways, like route selection and message dissemination. IoV is the self‐structured network composed with vehicles lies in the road and the road side units (RSUs). It offers Infrastructure‐to‐Vehicle (I2V), as well as Vehicle‐to‐Vehicle (V2V) data transmission mechanism for transmitting service messages. To reliably broadcast the service information to the intended recipient in the IoV network still faced issues. Hence, an efficient service message transmission protocol is developed using the proposed fractional mayfly optimization algorithm (FMA) for selecting the relay vehicle and cooperative vehicle for transmitting service messages to the destination vehicle from RSU through the process of I2V and V2V scheduling. The RSU selects the relay vehicle for every service message using the proposed algorithm and allocates the cooperative vehicle by RSU for scheduling V2V transmission. The simulation results showed that the proposed scheduling method obtains the best channel quality indicator (CQI), delay, distance, packet delivery ratio (PDR), and throughput value of 0.92 for 150 vehicles and 0.005891, 6.060731, 83.45%, and 171.50 Mbps for 100 vehicles.

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“…The effectiveness of the proposed approach is evaluated with the existing techniques, like NA‐SMT, 1 deep learning, 31 and reinforcement learning 32 …”

Section: Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Fractional mayfly optimization algorithm‐based Infrastructure‐to‐Vehicle and Vehicle‐to‐Vehicle scheduling for service message transmission in IoV‐fog

Lohat

Jain

Kumar

2023

Int J Communication

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“…One of the key challenges for such systems is to efficiently coordinate multiple protocol layers and allocate limited resources in a partially observable environment while satisfying QoS constraints. Wang et al [19] proposed a strategy that combines multi-hop forwarding via vehicles with dynamic spectrum access by first using a depth-first algorithm to limit the number of relay vehicles, and then using DRL to dynamically select the next hop node to reduce the conflict probability and channel idle probability. Choe et al [20] proposed an adaptive MAC layer algorithm using a deep Q network, which operates in a fully distributed manner and uses contention information collected from neighboring vehicles with a fully informative state representation to improve the performance of V2V secure packet broadcasting with simulations considering various levels of traffic congestion are evaluated.…”

Section: Drl-based Routing Protocols For Ugv Clustermentioning

confidence: 99%

Review of unmanned cluster routing protocols based on deep reinforcement learning

Peng

Yuan

Liu

et al. 2023

International Conference on Signal Processing and Communication Technology (SPCT 2022)

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Most unmanned clusters are typical mobile self-organizing networks (MANETs) with high dynamics and energy constraints. Deep reinforcement learning (DRL) is an emerging hotspot in the field of machine learning because it can make decisions without relying on models and is very suitable for dynamic unmanned cluster communication systems with time-varying network conditions. DRL -based unmanned cluster routing protocols are of great research value and significance. This paper introduces the fundamental characteristics of unmanned clusters and dynamic routing protocols; briefly describes the fundamental elements of DRL algorithms and their applicability in dynamic routing protocols; reviews the applications of DRL algorithms in unmanned clusters routing protocols; and summarizes the challenges and future development directions of unmanned clusters routing protocols based on DRL.

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Prediction-based Congestion Avoidance MAC Protocol for IoV (PCA-MAC)

Zerrouki

Moussaoui

Boualouache

2022

2022 First International Conference on Computer Communications and Intelligent Systems (I3CIS)

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MAC Contention Protocol Based on Reinforcement Learning for IoV Communication Environments

Cited by 6 publications

References 15 publications

Fractional mayfly optimization algorithm‐based Infrastructure‐to‐Vehicle and Vehicle‐to‐Vehicle scheduling for service message transmission in IoV‐fog

Fractional mayfly optimization algorithm‐based Infrastructure‐to‐Vehicle and Vehicle‐to‐Vehicle scheduling for service message transmission in IoV‐fog

Review of unmanned cluster routing protocols based on deep reinforcement learning

Prediction-based Congestion Avoidance MAC Protocol for IoV (PCA-MAC)

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