On 5G-V2X Use Cases and Enabling Technologies: A Comprehensive Survey

Allouis, Alain; Dayoub, Iyad; Cherkaoui, Soumaya

doi:10.1109/access.2021.3100472

Cited by 101 publications

(80 citation statements)

References 122 publications

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“…The results for our study of the testbed ED resources behavior under the different resource provisioning schemes are illustrated in Fig. (4)(5)(6)(7)(8)(9)(10)(11). Our proposed LRR scheme shows a significant improvement in terms of CPU consumption as shown in Fig.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…remote cloud computing infrastructure may not be suitable for latency-sensitive applications such as industrial process monitoring, automated vehicles [3], [4], virtual/augmented reality, surveillance and security and human emotions detection. Therefore, the proximity to the processing infrastructure is the key when it comes to reducing the experienced latency [5], [6].…”

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confidence: 99%

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Resource Provisioning in Edge Computing for Latency-Sensitive Applications

Abouaomar

Cherkaoui

Mlika

et al. 2021

IEEE Internet Things J.

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Low-Latency IoT applications such as autonomous vehicles, augmented/virtual reality devices and security applications require high computation resources to make decisions on the fly. However, these kinds of applications cannot tolerate offloading their tasks to be processed on a cloud infrastructure due to the experienced latency. Therefore, edge computing is introduced to enable low latency by moving the tasks processing closer to the users at the edge of the network. The edge of the network is characterized by the heterogeneity of edge devices forming it; thus, it is crucial to devise novel solutions that take into account the different physical resources of each edge device. In this paper, we propose a resource representation scheme, allowing each edge device to expose its resource information to the supervisor of the edge node through the mobile edge computing application programming interfaces proposed by European Telecommunications Standards Institute. The information about the edge device resource is exposed to the supervisor of the EN each time a resource allocation is required. To this end, we leverage a Lyapunov optimization framework to dynamically allocate resources at the edge devices. To test our proposed model, we performed intensive theoretical and experimental simulations on a testbed to validate the proposed scheme and its impact on different system's parameters. The simulations have shown that our proposed approach outperforms other benchmark approaches and provides low latency and optimal resource consumption.

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Section: Simulation Resultsmentioning

confidence: 99%

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confidence: 99%

Resource Provisioning in Edge Computing for Latency-Sensitive Applications

Abouaomar

Cherkaoui

Mlika

et al. 2021

IEEE Internet Things J.

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“…The Internet of vehicles (IoV) is an emerging concept that enhances the existing capabilities of vehicular communication by integrating with the Internet of things (IoT). IoV is a key use-case in the upcoming beyond fifth generation (5G) wireless networks [1,2]. IoV creates diverse new applications with extremely diverse service requirements including ultrahigh reliable and delay-sensitive, bandwidth-hungry as well as compute-intensive applications [3].…”

Section: Imentioning

confidence: 99%

Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles

Mlika

Cherkaoui

2021

IEEE Network

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The interconnection of vehicles in the future fifth generation (5G) wireless ecosystem forms the so-called Internet of vehicles (IoV). IoV offers new kinds of applications requiring delay-sensitive, compute-intensive and bandwidth-hungry services. Mobile edge computing (MEC) and network slicing (NS) are two of the key enabler technologies in 5G networks that can be used to optimize the allocation of the network resources and guarantee the diverse requirements of IoV applications. As traditional model-based optimization techniques generally end up with NP-hard and strongly non-convex and non-linear mathematical programming formulations, in this paper, we introduce a model-free approach based on deep reinforcement learning (DRL) to solve the resource allocation problem in MECenabled IoV network based on network slicing. Furthermore, the solution uses non-orthogonal multiple access (NOMA) to enable a better exploitation of the scarce channel resources.The considered problem addresses jointly the channel and power allocation, the slice selection and the vehicles selection (vehicles grouping). We model the problem as a single-agent Markov decision process. Then, we solve it using DRL using the wellknown DQL algorithm. We show that our approach is robust and effective under different network conditions compared to benchmark solutions.

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“…V2X communication enables information sharing through vehicleinfrastructure (V2I), vehicle-to-pedestrian, vehicle-to-vehicle (V2V), or vehicle-tocellular network (V2N) communication. V2X communication covers a range of use cases [7] that can broadly be divided into safety services, non-safety services and infotainment services. Safety services are the main use case of V2X communication, and aim to improve road safety by enabling road users and/or infrastructure to exchange information in a timely manner to avoid accidents.…”

Section: Introductionmentioning

confidence: 99%

Network slicing for vehicular communications: a multi-agent deep reinforcement learning approach

Mlika,

Cherkaoui

2022

Preprint

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This paper studies the multi-agent resource allocation problem in vehicular networks using non-orthogonal multiple access (NOMA) and network slicing. Vehicles want to broadcast multiple packets with heterogeneous quality-of-service (QoS) requirements, such as safety-related packets (e.g., accident reports) that require very low latency communication, while raw sensor data sharing (e.g., high-definition map sharing) requires high-speed communication. To ensure heterogeneous service requirements for different packets, we propose a network slicing architecture. We focus on a non-cellular network scenario where vehicles communicate by the broadcast approach via the direct device-to-device interface (i.e., sidelink communication). In such a vehicular network, resource allocation among vehicles is very difficult, mainly due to (i) the rapid variation of wireless channels among highly mobile vehicles and (ii) the lack of a central coordination point. Thus, the possibility of acquiring instantaneous channel state information to perform centralized resource allocation is precluded. The resource allocation problem considered is therefore very complex. It includes not only the usual spectrum and power allocation, but also coverage selection (which target vehicles to broadcast to) and packet selection (which network slice to use). This problem must be solved jointly since selected packets can be overlaid using NOMA and therefore spectrum and power must be carefully allocated for better vehicle coverage. To do so, we first provide a mathematical programming formulation and a thorough NP-hardness analysis of the problem. Then, we model it as a multi-agent Markov decision process. Finally, to solve it efficiently, we use a deep reinforcement learning (DRL) approach and specifically propose a deep Q learning

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On 5G-V2X Use Cases and Enabling Technologies: A Comprehensive Survey

Cited by 101 publications

References 122 publications

Resource Provisioning in Edge Computing for Latency-Sensitive Applications

Resource Provisioning in Edge Computing for Latency-Sensitive Applications

Network Slicing with MEC and Deep Reinforcement Learning for the Internet of Vehicles

Network slicing for vehicular communications: a multi-agent deep reinforcement learning approach

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