Joint deployment and trajectory optimization in UAV-assisted vehicular edge computing networks

Wu, Zhiwei; Yang, Zilin; Yang, Chao; Lin, Jixu; Liu, Yi; Chen, Xin

doi:10.23919/jcn.2021.000026

Cited by 51 publications

(10 citation statements)

References 37 publications

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“…Seid et al [38] proposed a deep deterministic policy gradient (DDPG)-based collaborative task offloading and resource allocation framework for minimizing the task execution delay and energy consumption. Wu et al [39] designed a pre-dispatch UAV-assisted vehicular edge computing network to cope with the demand of vehicles in multiple traffic jams and proposed a DRL-based energy efficiency autonomous deployment strategy, to obtain the optimal hovering position of UAV at each assigned mission area. Liu et al [40] proposed a two-phase DRL-based task offloading algorithm.…”

Section: Machine Learning-based Approachesmentioning

confidence: 99%

“…Then, the data transmission rate r UU n,m from U n to U m using a sub-channel can be expressed as [39]:…”

Section: Figurementioning

confidence: 99%

“…Many efforts have been made for efficiently scheduling tasks in the multi‐UAV‐empowered edge computing scenario [21–41]. However, little attention has been paid to joint SF placement and collaborative task scheduling.…”

Section: Introductionmentioning

confidence: 99%

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Joint UAV deployment, SF placement, and collaborative task scheduling in heterogeneous multi‐UAV‐empowered edge intelligence

et al. 2023

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To support artificial intelligence (AI)‐involved tasks offloaded from the mobile devices (MDs), it is necessary to equip the Unmanned Aerial Vehicle (UAV) with custom‐made co‐processor (CP) for handling AI workloads in multi‐UAV‐empowered Edge Intelligence. Existing CPU‐oriented task scheduling algorithm cannot apply to the CPU+CP heterogeneous architecture. In this backdrop, this paper first formulates the joint service function placement, collaborative task scheduling, UAV deployment, and MD position determination problem as a Mixed Integer Non‐Linear Programming problem. Then, an alternating optimization‐based algorithm is put forward to derive a sub‐optimal solution of the problem utilizing Differential Evolution and Greedy‐based Hungarian algorithms. A series of experiments are conducted to evaluate the performance of the proposal. Results show that authors' proposal can achieve an overall revenue that is roughly 50% higher than those of existing methods.

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Section: Machine Learning-based Approachesmentioning

confidence: 99%

“…Then, the data transmission rate r UU n,m from U n to U m using a sub-channel can be expressed as [39]:…”

Section: Figurementioning

confidence: 99%

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Joint UAV deployment, SF placement, and collaborative task scheduling in heterogeneous multi‐UAV‐empowered edge intelligence

et al. 2023

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“…By virtue of the advantages of dynamic mobility, flexibility, and on-demand deployment, unmanned aerial vehicles (UAVs) have been deemed as a promising technique in post-disaster area communication recovery [7][8][9]. In particular, the existence of line-of-sight (LoS) links between UAV and ground users has aroused a fastgrowing interest in utilizing UAVs as aerial wireless platforms [10][11][12][13], while the limited power supply in disaster areas restricts the users' survival time and equipment performance, which also puts forward higher requirements for UAV-aided post-disaster services.…”

Section: Introductionmentioning

confidence: 99%

Robust Resource Allocation and Trajectory Planning of UAV-Aided Mobile Edge Computing in Post-Disaster Areas

2022

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When natural disasters strike, users in the disaster area may be isolated and unable to transmit disaster information to the outside due to the damage of communication facilities. Unmanned aerial vehicles can be exploited as mobile edge servers to provide emergency service for ground users due to its mobility and flexibility. In this paper, a robust UAV-aided wireless-powered mobile edge computing (MEC) system in post disaster areas is proposed, where the UAV provides charging and computing service for users in the disaster area. Considering the estimation error of users’ locations, our target is to maximize the energy acquisition of each user by jointly optimizing the computing offloading process and the UAV trajectory. Due to the strongly coupled connectionbetween optimization variables and the non-convex nature for trajectory optimization, the problem is difficult to solve. Furthermore, the semi-infinity of the users’ possible location makes the problem even more intractable. To tackle these difficulties, we ignore the estimation error of users’ location firstly, and propose an iterative algorithm by using Lagrange dual method and successive convex approximation (SCA) technology. Then, we propose a cutting-set method to deal with the uncertainty of users’ location. In this method, we degrade the influence of location uncertainty by alternating between optimization step and pessimization step. Finally, simulation results show that the proposed robust algorithm can effectively improve the user energy acquisition.

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“…In order to recognize or control things, a much higher level of precise location estimation is required, and precise location estimation is required for services for the vulnerable populations. Nonterrestrial communication is required not only for providing coverage to areas that 5G cannot cover, but also for control and communication of urban air mobility (UAM)/unmanned aerial vehicle (UAV) services that have recently begun to be considered [4].…”

mentioning

confidence: 99%

6G R&D vision: Requirements and candidate technologies

et al. 2022

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The Korean Institute of Communications and Information Sciences (KICS), which is the largest information and communication technology institute in Korea, has been active in working towards development and standardization of mobile communication technology. In response to the need to meet upcoming 6G technical challenges in innovative applications such as hologram telepresence, extended reality (XR), digital twin, and connected robotics, the KICS 6G research initiative (KICS 6GRI) group has been created to establish a vision for key 6G technologies and identify research trends and directions. This article, therefore, covers major performance indicators and requirements envisioned by the KICS. In addition, we provide a comprehensive discussion of various candidate 6G technologies including (sub-)terahertz (THz), intelligent reflecting surface (IRS), artificial intelligence (AI)-based techniques, and non-terrestrial network (NTN).Index Terms-6G, the Korean Institute of Communications and Information Sciences (KICS), mobile communication standard. I. INTRODUCTIONF ROM the early stages of second-generation mobile communication development to the fifth-generation (5G), the

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Joint deployment and trajectory optimization in UAV-assisted vehicular edge computing networks

Abstract: Article that has been accepted for inclusion in a future issue of a journal. Content is final as presented, with the exception of pagination.

Cited by 51 publications

References 37 publications

Joint UAV deployment, SF placement, and collaborative task scheduling in heterogeneous multi‐UAV‐empowered edge intelligence

Joint UAV deployment, SF placement, and collaborative task scheduling in heterogeneous multi‐UAV‐empowered edge intelligence

Robust Resource Allocation and Trajectory Planning of UAV-Aided Mobile Edge Computing in Post-Disaster Areas

6G R&D vision: Requirements and candidate technologies

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