5G-Enabled MEC: A Distributed Traffic Steering for Seamless Service Migration of Internet of Vehicles

Anwar, Muhammad Rizwan; Wang, Shangguang; Akram, Umair; Raza, Salman; Mahmood, Shahid

doi:10.1109/jiot.2021.3084912

Cited by 42 publications

(21 citation statements)

References 24 publications

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“…Considering the scenario of the UAV as an auxiliary base station as shown in Figure 1 to restore post-disaster vehicle communication, under this system, there is a cellular cell, which is composed of ground base stations, from the perspective of existing technology, it can be considered as a 5G base station [3], and the collection of 5G base stations can be recorded as A={1,2,…,M}, M refers to the total number of 5G base stations. There is a swarm of drones, which on the one hand are the user equipment (UE) of the cellular cell, and on the other hand, they act as an air base station (ABS) to assist the ground base station to quickly and effectively restore the communication of vehicles after the disaster.…”

Section: System Network Modelmentioning

confidence: 99%

UAV as Auxiliary Base Station uses Deep Learning to Conduct Research on Network Resource Allocation

Ding¹

2023

Natural Language Processing, Information Retrieval and AI

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With the development of UAV technology, using UAV as a base station in the air can quickly restore vehicle communications after disasters. In order to reduce the delay and maximize the rational use of bandwidth and power, this paper applies TDMA technology to UAV communication network, and proposes a joint optimization allocation strategy of bandwidth and power. First of all, a deep learning network needs to be trained. The use of deep learning can improve the accuracy of prediction. The reward mechanism is set through the change of delay. The purpose of training is to enable the UAV to choose the optimal bandwidth allocation coefficient under the dynamic change of the environment. Then, a joint optimization strategy is proposed to set the SNR threshold to ensure the communication quality. The user's transmission rate is calculated according to the Shannon formula, Finally, the scheme with minimum delay is selected as the final bandwidth and power allocation value. In the simulation experiment, compared with the previous traditional algorithm, the network performance has been further improved in terms of reducing delay and energy consumption, and what needs to be improved may be the problem of computation.

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Section: System Network Modelmentioning

confidence: 99%

UAV as Auxiliary Base Station uses Deep Learning to Conduct Research on Network Resource Allocation

Ding¹

2023

Natural Language Processing, Information Retrieval and AI

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“…The authors in [31] establish an efficient service migration model and build a nonlinear 0-1 programming problem by designing a business migration scheme based on a particle swarm, effectively reducing the latency and energy consumption. In [32], by introducing distributed traffic guidance in large MEC systems and distinguishing between two different types of network elements, the scalability problem of a large MEC network was resolved as a partitioned MEC network, namely, edge servers and routers. Then, dynamic shortest path selection and dynamic multipath searching algorithms were proposed to achieve high QoS with ultra-low latency.…”

Section: Task Migrationmentioning

confidence: 99%

Energy-Delay Minimization of Task Migration Based on Game Theory in MEC-assisted Vehicular Networks

Wang,

Lv,

Lin

et al. 2022

Preprint

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Roadside units (RSUs), which have strong computing capability and are close to vehicle nodes, have been widely used to process delay-and computation-intensive tasks of vehicle nodes. However, due to their high mobility, vehicles may drive out of the coverage of RSUs before receiving the task processing results. In this paper, we propose a mobile edge computing-assisted vehicular network, where vehicles can offload their tasks to a nearby vehicle via a vehicle-to-vehicle (V2V) link or a nearby RSU via a vehicle-to-infrastructure link. These tasks are also migrated by a V2V link or an infrastructureto-infrastructure (I2I) link to avoid the scenario where the vehicles cannot receive the processed task from the RSUs. Considering mutual interference from the same link of offloading tasks and migrating tasks, we construct a vehicle offloading decision-based game to minimize the computation overhead. We prove that the game can always achieve Nash equilibrium and convergence by exploiting the finite improvement property. We then propose a task migration (TM) algorithm that includes three task-processing methods and two task-migration methods. Based on the TM algorithm, computation overhead minimization offloading (COMO) algorithm is presented. Extensive simulation results show that the proposed TM and COMO algorithms reduce the computation overhead and increase the success rate of task processing.

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“…Therefore, we can have two alternative strategies to decrease the value of ξ(f c , f d ) and improve the network IREE. One is to fine-tune the network capacity distribution via UAV or satellite base stations, and the other is to adjust the wireless traffic distribution via traffic migrations, such as [40] and [41].…”

Section: A Se-iree Trade-offmentioning

confidence: 99%

A Novel Energy Efficiency Metric for Next Generation Wireless Communication Networks

Chen¹,

Zhang²,

Chen³

et al. 2022

Preprint

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As a core performance metric for green communications, the conventional energy efficiency definition has successfully resolved many issues in the energy efficient wireless network design. In the past several generations of wireless communication networks, the traditional energy efficiency measure plays an important role to guide many energy saving techniques for slow varying traffic profiles. However, for the next generation wireless networks, the traditional energy efficiency fails to capture the traffic and capacity variations of wireless networks in temporal or spatial domains, which is shown to be quite popular, especially with ultra-scale multiple antennas and space-air-ground integrated network. In this paper, we present a novel energy efficiency metric named integrated relative energy efficiency (IREE), which is able to jointly measure the traffic profiles and the network capacities from the energy efficiency perspective. On top of that, the IREE based green trade-offs have been investigated and compared with the conventional energy efficient design. Moreover, we apply the IREE based green trade-offs to evaluate several candidate technologies for 6G networks, including reconfigurable intelligent surfaces and space-air-ground integrated network. Through some analytical and numerical results, we show that the proposed IREE metric is able to capture the wireless traffic and capacity mismatch property, which is significantly different from the conventional energy efficiency metric. Since the IREE oriented design or deployment strategy is able to consider the network capacity improvement and the wireless traffic matching simultaneously, it can be regarded as a useful guidance for future energy efficient network design.

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5G-Enabled MEC: A Distributed Traffic Steering for Seamless Service Migration of Internet of Vehicles

Cited by 42 publications

References 24 publications

UAV as Auxiliary Base Station uses Deep Learning to Conduct Research on Network Resource Allocation

UAV as Auxiliary Base Station uses Deep Learning to Conduct Research on Network Resource Allocation

Energy-Delay Minimization of Task Migration Based on Game Theory in MEC-assisted Vehicular Networks

A Novel Energy Efficiency Metric for Next Generation Wireless Communication Networks

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