ARTNet: Ai-Based Resource Allocation and Task Offloading in a Reconfigurable Internet of Vehicular Networks

Akbar, Aamir; Jan, Roohullah; Jan, Mian Ahmad; Wang, Lei; Song, Houbing; Shah, Nadir

doi:10.1109/tnse.2020.3047454

Cited by 27 publications

(19 citation statements)

References 38 publications

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“…Once the problem is solved, the nondominated sorting genetic algorithm is applied. MDP was first proposed as a tool for making resource decisions [20]. In order to better understand fog computing, researchers are looking at SDV-F (softwaredefined vehicular-based fog computing).…”

Section: Theories and Applications Of Optimizationmentioning

confidence: 99%

A Federated Deep Learning Empowered Resource Management Method to Optimize 5G and 6G Quality of Services (QoS)

Alsulami

Serbaya

Abualsauod

et al. 2022

Wireless Communications and Mobile Computing

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The quality of service (QoS) in 5G/6G communication enormously depends upon the mobility and agility of the network architecture. An increase in the possible uses of 5G vehicular network simultaneously expands the scope of the network’s quality of service (QoS). To this end, a safety-critical real-time system has become one of the most demanding criteria for the vehicular network. Although different mathematical and computation methods have traditionally been used to optimize the allocation of resources, but the nonconvexity of optimization issues creates unique type of challenges. In recent years, machine learning (ML) has emerged as a valuable tool for dealing with computational complexity that involves large amounts of data in heterogeneous vehicular networks. By using optimization and cutting-edge machine learning techniques, this article gives readers an insight about how 5G vehicular network resources can be allocated to reinforce network communication. Furthermore, a new federated deep reinforcement learning- (FDRL-) based vehicle communication method is presented as a new insight. Finally, a UAV-aided vehicular communication system based on FDRL-based UAVs is proposed as a novel resource management technique to optimize 5G and 6G quality of services.

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Section: Theories and Applications Of Optimizationmentioning

confidence: 99%

A Federated Deep Learning Empowered Resource Management Method to Optimize 5G and 6G Quality of Services (QoS)

Alsulami

Serbaya

Abualsauod

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…A network layer approach is essential to secure the performance in inter-vehicle network. One of the examples is ARTNet (Ai-Based Resource Allocation and Task Offloading in a Reconfigurable Internet of Vehicular Networks), which maximizes resource utilization and optimizes the distribution of traffic [13]. Although it may be helpful to avoid large delay in the Internet of Vehicular (IoV) infrastructure, it can not prevent [14].…”

Section: Figure 2 Latency Requirements For Each Domain In a Vehicular...mentioning

confidence: 99%

Performance Enhancement of In-Vehicle 10BASE-T1S Ethernet Using Node Prioritization and Packet Segmentation

Min

Park

2022

IEEE Access

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Following the appearance of electrical vehicles and autonomous driving, a new in-vehicle network architecture is required that should be able to process substantial sensor data and communicate with other vehicles or infrastructure. Ethernet is considered a promising technology for replacing existing communication networks due to its stability and large bandwidth. Among various types of Ethernet, 10Base-T1S can play a significant role in connecting multiple nodes in a bus structure at each zone of the zone-based network architecture. Although its latency is reduced using the physical layer collision avoidance (PLCA) algorithm, it is not small enough to be adopted in safety and powertrain domains, which require a very small delay of less than a few hundred microseconds. Therefore, this study uses node prioritization and packet segmentation to overcome the limitations of the existing PLCA algorithm. The former changes the transmission sequence of nodes while the latter reduces the waiting time for a packet. This paper suggests the algorithms of these schemes and analyzes the performance.

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“…Then, the problem is solved by the nondominated sorting genetic algorithm. In [78], a resource decision making, based on the Markov decision process (MDP), is proposed. The approach of softwaredefined vehicular-based fog computing (SDV-F) is studied.…”

Section: Supervised Learning-assisted Techniquesmentioning

confidence: 99%

DRL-Based Intelligent Resource Allocation for Diverse QoS in 5G and toward 6G Vehicular Networks: A Comprehensive Survey

Nguyen

et al. 2021

Wireless Communications and Mobile Computing

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The vehicular network is taking great attention from both academia and industry to enable the intelligent transportation system (ITS), autonomous driving, and smart cities. The system provides extremely dynamic features due to the fast mobile characteristics. While the number of different applications in the vehicular network is growing fast, the quality of service (QoS) in the 5G vehicular network becomes diverse. One of the most stringent requirements in the vehicular network is a safety-critical real-time system. To guarantee low-latency and other diverse QoS requirements, wireless network resources should be effectively utilized and allocated among vehicles, such as computation power in cloud, fog, and edge servers; spectrum at roadside units (RSUs); and base stations (BSs). Historically, optimization problems have mostly been investigated to formulate resource allocation and are solved by mathematical computation methods. However, the optimization problems are usually nonconvex and hard to be solved. Recently, machine learning (ML) is a powerful technique to cope with the complexity in computation and has capability to cope with big data and data analysis in the heterogeneous vehicular network. In this paper, an overview of resource allocation in the 5G vehicular network is represented with the support of traditional optimization and advanced ML approaches, especially a deep reinforcement learning (DRL) method. In addition, a federated deep reinforcement learning- (FDRL-) based vehicular communication is proposed. The challenges, open issues, and future research directions for 5G and toward 6G vehicular networks, are discussed. A multiaccess edge computing assisted by network slicing and a distributed federated learning (FL) technique is analyzed. A FDRL-based UAV-assisted vehicular communication is discussed to point out the future research directions for the networks.

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ARTNet: Ai-Based Resource Allocation and Task Offloading in a Reconfigurable Internet of Vehicular Networks

Cited by 27 publications

References 38 publications

A Federated Deep Learning Empowered Resource Management Method to Optimize 5G and 6G Quality of Services (QoS)

A Federated Deep Learning Empowered Resource Management Method to Optimize 5G and 6G Quality of Services (QoS)

Performance Enhancement of In-Vehicle 10BASE-T1S Ethernet Using Node Prioritization and Packet Segmentation

DRL-Based Intelligent Resource Allocation for Diverse QoS in 5G and toward 6G Vehicular Networks: A Comprehensive Survey

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