Energy-Aware Dynamic Resource Allocation in UAV Assisted Mobile Edge Computing Over Social Internet of Vehicles

Zhang, Long; Zhao, Zhen; Wu, Qiwu; Zhao, Hui; Xu, Haitao; Wu, Xiaobo

doi:10.1109/access.2018.2872753

Cited by 132 publications

(74 citation statements)

References 35 publications

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“…However, for users located at cell edge, such an offloading strategy may even cause more transmission energy and/or longer delay than local computation due to the limited communication rate with the AP/BS. To address this problem, UAVs with highly controllable mobility can be used as the flying cloudlets to achieve more efficient computation offloading for the users by moving significantly closer to them [149,234]- [239].…”

Section: Mobile Edge Computingmentioning

confidence: 99%

Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond

2019

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Unmanned aerial vehicles (UAVs) have found numerous applications and are expected to bring fertile business opportunities in the next decade. Among various enabling technologies for UAVs, wireless communication is essential and has drawn significantly growing attention in recent years. Compared to the conventional terrestrial communications, UAVs' communications face new challenges due to their high altitude above the ground and great flexibility of movement in the threedimensional (3D) space. Several critical issues arise, including the line-of-sight (LoS) dominant UAV-ground channels and resultant strong aerial-terrestrial network interference, the distinct communication quality of service (QoS) requirements for UAV control messages versus payload data, the stringent constraints imposed by the size, weight and power (SWAP) limitations of UAVs, as well as the exploitation of the new design degree of freedom (DoF) brought by the highly controllable 3D UAV mobility. In this paper, we give a tutorial overview of the recent advances in UAV communications to address the above issues, with an emphasis on how to integrate UAVs into the forthcoming fifth-generation (5G) and future cellular networks. In particular, we partition our discussions into two promising research and application frameworks of UAV communications, namely UAVassisted wireless communications and cellular-connected UAVs, where UAVs serve as aerial communication platforms and users, respectively. Furthermore, we point out promising directions for future research and investigation. KeywordsUnmanned aerial vehicle (UAV), wireless communication, cellular network, channel model, antenna model, energy efficiency, air-ground interference, 3D placement and trajectory, optimization. TechnologyDescription Advantages Disadvantages Direct linkDirect point-to-point communication with ground node Simple, low cost Limited range, low data rate, vulnerable to interference, non-scalable Satellite Communication and Internet access via satellite Global coverage Costly, heavy/bulky/energyconsuming communication equipment, high latency, large signal attenuation Ad-hoc network Dynamically self-organizing and infrastructure-free network Robust and adaptable, support for high mobility Costly, low spectrum efficiency, intermittent connectivity, complex routing protocol Cellular network Enabling UAV communications by using cellular infrastructure and technologies Almost ubiquitous accessibility, cost-effective, superior performance and scalability Unavailable in remote areas, potential interference with terrestrial communications and high reliability/throughput for both air-to-air and airto-ground wireless communications in the three-dimensional (3D) space. Towards this end, four candidate communication technologies are listed and compared in Table III, including i) direct link; ii) satellite; iii) ad-hoc network; and iv) cellular network. In the following, we discuss the advantages as well as limitations of each of these technologies in detail.1) Direct Link: Due to its simpli...

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Section: Mobile Edge Computingmentioning

confidence: 99%

Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond

2019

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“…The authors in [20] proposed a spectrum and energy-efficient scheme for UAV-enabled relay network, in which UAV path is optimized by allocating the communication time slots between source and destination nodes. In [21], the authors proposed an energy-aware power allocation scheme in the UAV-assisted edge networks while utilizing the internet of vehicles for the computation offloading. The work in [22] developed a UAV placement scheme to maximize the served users while consuming the minimum transmit power.…”

Section: A Energy Efficiency In Uav-assisted Cellular Networkmentioning

confidence: 99%

“…The maximum power bound p max helps to serve more users by allocating the leftover power to low channel gain users. This is performed by adjusting the water level, λ j , using (21). This is illustrated in Fig.…”

Section: Power Allocation To Embb Usersmentioning

confidence: 99%

Energy Efficient Resource Allocation in UAV-based Heterogeneous Networks

Manzoor

Kim

Hong

2019

2019 20th Asia-Pacific Network Operations and Management Symposium (APNOMS)

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Unmanned aerial vehicles (UAVs) can provide an effective solution for improving the coverage, capacity, and the overall performance of terrestrial wireless cellular networks. In particular, UAV-assisted cellular networks can meet the stringent performance requirements of the fifth generation new radio (5G NR) applications. In this paper, the problem of energy-efficient resource allocation in UAV-assisted cellular networks is studied under the reliability and latency constraints of 5G NR applications. The framework of ruin theory is employed to allow solar-powered UAVs to capture the dynamics of harvested and consumed energies. First, the surplus power of every UAV is modeled, and then it is used to compute the probability of ruin of the UAVs. The probability of ruin denotes the vulnerability of draining out the power of a UAV. Next, the probability of ruin is used for efficient user association with each UAV.Then, power allocation for 5G NR applications is performed to maximize the achievable network rate using the water-filling approach. Simulation results demonstrate that the proposed ruin-based scheme can enhance the flight duration up to 61% and the number of served users in a UAV flight by up to 58%, compared to a baseline SINR-based scheme.

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“…For instance, [6] jointly optimized the UAV trajectory and bit allocation under latency and UAV energy constraints. Later on, [7] studied a fixed UAV Q. Liu, L. Sun, J. Li, and F. Shu are with School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China. E-mail:{qianliu6767, sunlinlin, jun.li, shufeng}@njust.edu.cn.…”

Section: Introductionmentioning

confidence: 99%

“…How to design the UAV trajectory to serve mobile TUs in the MEC networks remains challenging and primarily motivates our work. On the other hand, the trajectory optimization relies on either dynamic programming [6] or successive convex approximation method [7] [8]. A major concern lies in that the optimization for the offline trajectory designs in [6]- [8] may not be feasible to deal with the mobile TUs in MEC networks.…”

Section: Introductionmentioning

confidence: 99%

Path Planning for UAV-Mounted Mobile Edge Computing With Deep Reinforcement Learning

Liu

Shi

Sun

et al. 2020

IEEE Trans. Veh. Technol.

199

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In this letter, we study an unmanned aerial vehicle (UAV)-mounted mobile edge computing network, where the UAV executes computational tasks offloaded from mobile terminal users (TUs) and the motion of each TU follows a Gauss-Markov random model. To ensure the quality-of-service (QoS) of each TU, the UAV with limited energy dynamically plans its trajectory according to the locations of mobile TUs. Towards this end, we formulate the problem as a Markov decision process, wherein the UAV trajectory and UAV-TU association are modeled as the parameters to be optimized. To maximize the system reward and meet the QoS constraint, we develop a QoS-based action selection policy in the proposed algorithm based on double deep Q-network. Simulations show that the proposed algorithm converges more quickly and achieves a higher sum throughput than conventional algorithms.

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Energy-Aware Dynamic Resource Allocation in UAV Assisted Mobile Edge Computing Over Social Internet of Vehicles

Cited by 132 publications

References 35 publications

Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond

Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond

Energy Efficient Resource Allocation in UAV-based Heterogeneous Networks

Path Planning for UAV-Mounted Mobile Edge Computing With Deep Reinforcement Learning

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