Joint Communication and Computation Resource Scheduling of a UAV-Assisted Mobile Edge Computing System for Platooning Vehicles

Liu, Yang; Zhou, Jianshan; Sheng, Zhengguo; Duan, Xuting; Qu, Guixian; Leung, Victor C. M.

doi:10.1109/tits.2021.3082539

Cited by 69 publications

(18 citation statements)

References 54 publications

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“…Mobile edge computing with the implement of unmanned aerial vehicle networks has been widely investigated from the perspective of resource scheduling and UAV trajectory design [6], [13]- [17]. For example, [14], [15] have studied the scenario that UAVs equipped with MEC servers provide edge computing services to fixed users.…”

Section: B Related Work and Motivationmentioning

confidence: 99%

“…Differently, in [16], Zhang et al have considered mobile users in UAV-assisted MEC systems, which are defined as pedestrians with a predetermined trajectory and low mobility. In [13], Liu et al have further considered one-dimensional connected vehicle platoons, and jointly scheduled transmission power, communication and computation time allocation scheme to maximize computing rate. A modified SQP algorithm is applied to efficiently solve the non-convex problem.…”

Section: B Related Work and Motivationmentioning

confidence: 99%

“…Existing literature [27], [34], [35] focuses on optimizing the problem with low user mobility. Nevertheless, the original considerations seem impractical when it comes to vehicle users, and another literature assumes the vehicles move along a straight line [13]. Our research considers vehicle turning scenarios with platooning vehicles, which is widespread in the applications of intelligent vehicle control technology and ADAS.…”

Section: A Mobility Modelmentioning

confidence: 99%

“…It is widely assumed that in different time slots [4], [13], the vehicles movement can be simplified to a uniform acceleration movement. Hence, its motion equation can be expressed as follows:…”

Section: A Mobility Modelmentioning

confidence: 99%

See 3 more Smart Citations

Weighted Energy-Efficiency Maximization for a UAV-Assisted Multiplatoon Mobile-Edge Computing System

Duan

Zhou

et al. 2022

IEEE Internet Things J.

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Section: B Related Work and Motivationmentioning

confidence: 99%

Section: B Related Work and Motivationmentioning

confidence: 99%

Section: A Mobility Modelmentioning

confidence: 99%

Section: A Mobility Modelmentioning

confidence: 99%

See 2 more Smart Citations

Weighted Energy-Efficiency Maximization for a UAV-Assisted Multiplatoon Mobile-Edge Computing System

Duan

Zhou

et al. 2022

IEEE Internet Things J.

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“…It is envisioned that the VCPS can improve road traffic safety and efficiency and also bring favorable and environmentally-friendly driving experience. Specifically, the vehicle platooning control of connected and autonomous vehicles (CAVs) has attracted increasing attention in recent years [7]- [10]. In general, the goal of vehicle platooning control is to maintain a desired space headway or a desired time headway between any two successive platooning vehicles meanwhile stabilizing the whole platoon moving at a desired velocity with guaranteed safety.…”

Section: Introductionmentioning

confidence: 99%

Robust Min-Max Model Predictive Vehicle Platooning With Causal Disturbance Feedback

Zhou

Sheng

Duan

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Platoon-based vehicular cyber-physical systems have gained increasing attention due to their potentials in improving traffic efficiency, capacity, and saving energy. However, external uncertain disturbances arising from mismatched model errors, sensor noises, communication delays and unknown environments can impose a great challenge on the constrained control of vehicle platooning. In this paper, we propose a closed-loop min-max model predictive control (MPC) with causal disturbance feedback for vehicle platooning. Specifically, we first develop a compact form of a centralized vehicle platooning model subject to external disturbances, which also incorporates the lower-level vehicle dynamics. We then formulate the uncertain optimal control of the vehicle platoon as a worst-case constrained optimization problem and derive its robust counterpart by semidefinite relaxation. Thus, we design a causal disturbance feedback structure with the robust counterpart, which leads to a closed-loop min-max MPC platoon control solution. Even though the min-max MPC follows a centralized paradigm, its robust counterpart can keep the convexity and enable the efficient and practical implementation of current convex optimization techniques. We also derive a linear matrix inequality (LMI) Manuscript

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Non‐orthogonal multiple access‐based task processing and energy optimization in vehicular edge computing networks

Shi,

Li,

Zhao

et al. 2024

Concurrency and Computation

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SummaryVehicular edge computing (VEC) is envisioned as a promising approach to process explosive vehicle tasks, where vehicles can choose to upload tasks to nearby edge nodes for processing. However, since the communication between vehicles and edge nodes is via wireless network, which means the channel condition is complex. Moreover, in reality, the arrival time of each vehicle task is stochastic, so efficient communication methods should be designed for VEC. As one of the key communication technologies in 5G, non‐orthogonal multiple access (NOMA) can effectively increase the number of simultaneous transmission tasks and enhance transmission performance. In this article, we design a NOMA‐based task allocation scheme to improve the VEC system. We first establish the mathematical model and divide the allocation of tasks into two processes: the transmission process and the computation process. In the transmission process, we adopt the NOMA technique to upload the tasks in batches. In the computation process, we use a high response‐ratio strategy to determine the computation order. Then we define the optimization objective as maximizing task completion rate and minimizing task energy consumption, which is an integer nonlinear problem with lots of integer variables and cannot be solved directly. Through further analysis, we design a heuristics algorithm which we name as the AECO (average energy consumption optimization) algorithm. By using the AECO, we obtain the optimal allocation strategy by constantly adjusting the optimal variables. Simulation results demonstrate that our algorithm has a significant number of advantages.

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Joint Communication and Computation Resource Scheduling of a UAV-Assisted Mobile Edge Computing System for Platooning Vehicles

Abstract: Victor C M (2021) Joint communication and computation resource scheduling of a UAV-assisted mobile edge computing system for platooning vehicles. IEEE Transactions on Intelligent Transportation Systems. pp. 1-16.

Cited by 69 publications

References 54 publications

Weighted Energy-Efficiency Maximization for a UAV-Assisted Multiplatoon Mobile-Edge Computing System

Weighted Energy-Efficiency Maximization for a UAV-Assisted Multiplatoon Mobile-Edge Computing System

Robust Min-Max Model Predictive Vehicle Platooning With Causal Disturbance Feedback

Non‐orthogonal multiple access‐based task processing and energy optimization in vehicular edge computing networks

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