Optimal-Delay-Guaranteed Energy Efficient Cooperative Offloading in VEC Networks

Zhu, Yao; Yang, Tianyu; Hu, Yulin; Gao, Wanting; Schmeink, Anke

doi:10.1109/globecom42002.2020.9348017

Cited by 8 publications

(5 citation statements)

References 16 publications

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“…Recently, computation offloading has received more and more attention as one of the most promising solutions to this issue, and various offloading strategies have been proposed ( Mao, Zhang & Letaief, 2016 ; Zhang et al, 2017a , 2019a ; Guo, Li & Guan, 2019 ; Li et al, 2019b , 2020b , 2020a ; Messous et al, 2019 ; Meng et al, 2019 ; Mitsis, Tsiropoulou & Papavassiliou, 2020 ; Zhu et al, 2020a , 2020b ; Alioua et al, 2020 ; Tang & Wong, 2022 ; Wang et al, 2021 ; Chen & Liu, 2021 ). The differences between various computation offloading methods are shown in Table 1 .…”

Section: Related Workmentioning

confidence: 99%

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Multi-user multi-objective computation offloading for medical image diagnosis

Liu

Tian

Zhao

et al. 2023

PeerJ Computer Science

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Computation offloading has effectively solved the problem of terminal devices computing resources limitation in hospitals by shifting the medical image diagnosis task to the edge servers for execution. Appropriate offloading strategies for diagnostic tasks are essential. However, the risk awareness of each user and the multiple expenses associated with processing tasks have been ignored in prior works. In this article, a multi-user multi-objective computation offloading for medical image diagnosis is proposed. First, the prospect theoretic utility function of each user is designed considering the delay, energy consumption, payment, and risk awareness. Second, the computation offloading problem including the above factors is defined as a distributed optimization problem, which with the goal of maximizing the utility of each user. The distributed optimization problem is then transformed into a non-cooperative game among the users. The exact potential game proves that the non-cooperative game has Nash equilibrium points. A low-complexity computation offloading algorithm based on best response dynamics finally is proposed. Detailed numerical experiments demonstrate the impact of different parameters and convergence in the algorithm on the utility function. The result shows that, compare with four benchmarks and four heuristic algorithms, the proposed algorithm in this article ensures a faster convergence speed and achieves only a 1.14% decrease in the utility value as the number of users increases.

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Section: Related Workmentioning

confidence: 99%

“… Zhang et al (2017a) , Guo, Li & Guan (2019) , Li et al (2019b) , Zhu et al (2020b) are offloading strategies to balance delay and energy consumption. For instance, Zhang et al (2017a) introduced predation behavior, swarm behavior and following behavior into the artificial fish swarm algorithm, which saves 30% energy consumption.…”

Section: Related Workmentioning

confidence: 99%

Multi-user multi-objective computation offloading for medical image diagnosis

Liu

Tian

Zhao

et al. 2023

PeerJ Computer Science

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“…Meanwhile, with the deployment of more roadside edge servers to provide communication/computation resources, the construction cost and energy consumption will further increase and need to be considered. Energy-efficient computation offloading strategies will be required for optimal resource allocation and offloading decision for saving energy in vehicular edge computing scenarios [24], [98], [99], [101], [102], [103], [104], [106].…”

Section: Mec For Vehicular Usersmentioning

confidence: 99%

Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking

Wang¹,

Zhu²,

Hossain³

2021

Preprint

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As one of the most promising applications in future Internet of Things, Internet of Vehicles (IoV) has been acknowledged as a fundamental technology for developing the Intelligent Transportation Systems in smart cities. With the emergence of the sixth generation (6G) communications technologies, massive network infrastructures will be densely deployed and the number of network nodes will increase exponentially, leading to extremely high energy consumption. There has been an upsurge of interest to develop the green IoV towards sustainable vehicular communication and networking in the 6G era. However, as a special mobile ad-hoc network, the energy cost in an IoV system involves the communication and computation energy in addition to the fuel consumption and the electricity cost of moving vehicles. Moreover, the energy harvesting technology, which is likely to be adopted widely in 6G systems, will complicate the optimization of energy efficiency in the entire system. Current studies focus only on part of the energy issues in IoV systems without a comprehensive discussion of the state-of-the-art energy-efficient approaches and the influence of the development of 6G networks on green IoV. In this paper, we present the main considerations for green IoV from five different scenarios, including the communication, computation, traffic, Electric Vehicles (EVs), and energy harvesting management. The literatures relevant to each of the scenarios are compared from the perspective of energy optimization (e.g., with respect to resource allocation, workload scheduling, routing design, traffic control, charging management, energy harvesting and sharing, etc.) and the related factors affecting energy efficiency (e.g., resource limitation, channel state, network topology, traffic condition, etc.). In addition, we introduce the potential challenges and the emerging technologies in 6G for developing green IoV systems. Finally, we discuss the research trends in designing energy-efficient IoV systems.

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“…For example, by optimizing the offloading scheduling and communication and computation resource allocation during the offloading process, [23]- [26] maximize the system utility, [27], [28] minimize the total latency, and [29] minimizes the total energy consumption. Most of the works, including [23]- [29], offload a task to only one roadside unit (RSU) (i.e., edge computing server). However, in a practical vehicular network, an RSU's coverage diameter is around 300m, and a vehicle moves at 20km/h-100km/h.…”

Section: Introductionmentioning

confidence: 99%

“…The optimal offloading design of edge computing-enabled vehicular networks has recently received increasing attention [23]- [30]. For example, by optimizing the offloading scheduling and communication and computation resource allocation during the offloading process, [23]- [26] maximize the system utility, [27], [28] minimize the total latency, and [29] minimizes the total energy consumption. Most of the works, including [23]- [29], offload a task to only one roadside unit (RSU) (i.e., edge computing server).…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Computation Task Splitting for Edge Computing-Enabled Vehicular Networks

Cho

Cui

Lee

2020

2020 IEEE International Conference on Communications Workshops (ICC Workshops)

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Edge computing technology has great potential to improve various computation-intensive applications in vehicular networks by providing sufficient computation resources for vehicles. However, it is still a challenge to fully unleash the potential of edge computing in edge computing-enabled vehicular networks. In this paper, we develop the energy-efficient cooperative offloading scheme for edge computing-enabled vehicular networks, which splits the task into multiple subtasks and offloads them to different roadside units (RSUs) located ahead along the route of the vehicle. We first establish novel cooperative offloading models for the offline and online scenarios in edge computing-enabled vehicular networks. In each offloading scenario, we formulate the total energy minimization with respect to the task splitting ratio, computation resource, and communication resource. In the offline scenario, we equivalently transform the original problem to a convex problem and obtain optimal solutions for multi-vehicle case and single-vehicle case, respectively. Furthermore, we show that the method proposed for the offline scenario can also be applied to solve the optimization problem in the online scenario. Finally, through numerical results, by analyzing the impact of network parameters on the total energy consumption, we verify that our proposed solution consumes lower energy than baseline schemes.

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Optimal-Delay-Guaranteed Energy Efficient Cooperative Offloading in VEC Networks

Cited by 8 publications

References 16 publications

Multi-user multi-objective computation offloading for medical image diagnosis

Multi-user multi-objective computation offloading for medical image diagnosis

Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking

Energy-Efficient Computation Task Splitting for Edge Computing-Enabled Vehicular Networks

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