Completion Time and Energy Optimization in the UAV-Enabled Mobile-Edge Computing System

Zhan, Cheng; Hu, Han; Sui, Xiufeng; Liu, Zhi; Niyato, Dusit

doi:10.1109/jiot.2020.2993260

Cited by 191 publications

(68 citation statements)

References 35 publications

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“…Energy issues and computation time are critical for the UAV-enabled MEC system due to the limited onboard energy of UAV. In [106], it investigates the minimization of UAV energy consumption and task completion time, respectively, while an individual UAV flying at a horizon plane provides computation offloading opportunities to IoT devices. Since the objective of only minimizing the energy consumption or only maximizing the computation rates may not satisfy energy and computation optimization simultaneously, the concept of computation efficiency is introduced in [107], [108].…”

Section: ) Offloading To Uavmentioning

confidence: 99%

Air-Ground Integrated Mobile Edge Networks: A Survey

Zhang

et al. 2020

IEEE Access

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With proliferation of smart devices and wireless applications, the recent few years have witnessed data surge. These massive data needs to be stored, transmitted, and processed in time to exploit their value for decision making. Conventional cloud computing requires transmission of massive amount of data in and out of core network, which can lead to longer service latency and potential traffic congestion. As a new platform, mobile edge computing (MEC) moves computation and storage resources to edge network in proximity to the data source. With MEC, data can be processed locally, and thus mitigate issues of latency and congestion. However, it is very challenging to reap the benefits of MEC everywhere due to geographic constraints, expensive deployment cost, and immoveable base stations. Because of easy deployment and high mobility of unmanned aerial vehicles (UAVs), air-ground integrated mobile edge networks (AGMEN) is proposed, where UAVs are employed to assist the MEC network. Such an AGMEN expects to provide MEC services ubiquitously and reliably. In this article, we first introduce the characteristics and components of UAV. Then, we will review the applications, key challenges, and current research technologies of AGMEN, from perspectives of communication, computation, and caching, respectively. Finally, we will discuss some essential research directions for AGMEN.

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Section: ) Offloading To Uavmentioning

confidence: 99%

Air-Ground Integrated Mobile Edge Networks: A Survey

Zhang

et al. 2020

IEEE Access

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“…The bits allocation and trajectory of the cloudlet were jointly optimized with orthogonal and non-orthogonal multiple access schemes. In [ 41 ], the authors studied joint design of computation offloading and resource allocation as well as UAV trajectory for minimization of energy consumption and completion time of the UAV in the UAV-enabled MEC system for Internet of Things. In [ 42 ], the authors studied the energy reduction problem in UAV-enabled edge by smartly making offloading decisions, allocating transmitted bits in both uplink and downlink, as well as designing UAV trajectory.…”

Section: Introductionmentioning

confidence: 99%

An Energy Efficient Design of Computation Offloading Enabled by UAV

Wen

et al. 2020

Sensors

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The data volume is exploding due to various newly-developing applications that call for stringent communication requirements towards 5th generation wireless systems. Fortunately, mobile edge computing makes it possible to relieve the heavy computation pressure of ground users and decrease the latency and energy consumption. What is more, the unmanned aerial vehicle has the advantages of agility and easy deployment, which gives the unmanned aerial vehicle enabled mobile edge computing system opportunities to fly towards areas with communication demand, such as hotspot areas. However, the limited endurance time of unmanned aerial vehicle affects the performance of mobile edge computing services, which results in the incomplete mobile edge computing services under the time limit. Consequently, this paper concerns the energy-efficient scheme design of the unmanned aerial vehicle while providing high-quality offloading services for ground users, particularly in the regions where the ground communication infrastructures are overloaded or damaged after natural disasters. Firstly, the model of energy-efficient design of the unmanned aerial vehicle is set up taking the constraints of the energy limitation of the unmanned aerial vehicle, the data causality, and the speed of the unmanned aerial vehicle into account. Subsequently, aiming at maximizing the energy efficiency of the unmanned aerial vehicle in the unmanned aerial vehicle enabled mobile edge computing system, the bits allocation in each time slot and the trajectory of the unmanned aerial vehicle are jointly optimized. Secondly, a successive convex approximation based alternating algorithm is brought forward to deal with the non-convex energy efficiency maximization problem. Finally, it is proved that the proposed energy efficient scheme design of the unmanned aerial vehicle is superior to other benchmark schemes by the simulation results. Besides, how the performance of proposed scheme design change under different parameters is discussed.

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“…Meanwhile, to minimize the maximum task latency, authors in [15] designed a novel penalty dual decomposition-based algorithm by jointly optimizing UAV trajectory and task offloading. Due to the limited battery capacity and computation resources of IoT devices, the tradeoff between the energy consumption and time sensitively has also attracted significant attention [16,17,18]. More specifically, authors in [16] utilized the modified genetic algorithm NSGA-II to solve a multi-objective problem of GNs' average energy consumption and latency time, while the authors in [17] aimed at minimizing the weighted sum of the service delay of all IoT devices and UAV energy consumption.…”

Section: Introductionmentioning

confidence: 99%

“…More specifically, authors in [16] utilized the modified genetic algorithm NSGA-II to solve a multi-objective problem of GNs' average energy consumption and latency time, while the authors in [17] aimed at minimizing the weighted sum of the service delay of all IoT devices and UAV energy consumption. Besides, an alternative optimization algorithm based on successive convex approximation (SCA) was derived to minimize UAV energy and completion time by jointly optimizing computing offloading, resource allocation and trajectory in [18].…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, these pioneering works above just investigated the single task of IoT devices, and focused on either data collection [8,9] or computing offloading [11,12,13,14,15,16,17,18], but ignored the effectiveness of data caching. In the meantime, time-sensitivity has been one of most important metric for network performance, and triggered a great deal of research interests in applications related with UAV.…”

Section: Introductionmentioning

confidence: 99%

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Collaborative Offloading for UAV-enabled Time-Sensitive MEC Networks

Zhao

et al. 2020

Preprint

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Recently, unmanned aerial vehicle (UAV) acts as the aerial mobile edge computing (MEC) node to help the battery-limited Internet of Things (IoT) devices relieve burdens from computation and data collection, and prolong the lifetime of operating. However, IoT devices can ONLY ask UAV for either computing or caching help, and collaborative offloading services of UAV is rarely mentioned in the literature. Moreover, IoT device has multiple mutually independent tasks, which make collaborative offloading policy design even more challenging. Therefore, we investigate a UAV-enabled MEC networks with the consideration of multiple tasks either for computing or caching. Taking the quality of experience (QoE) requirement of time-sensitive tasks into consideration, we aim to minimize the total energy consumption of IoT devices by jointly optimizing trajectory, communication and computing resource allocation at UAV, and task offloading decision at IoT devices. Since this problem has highly non-convex objective function and constraints, we first decompose the original problem into three subproblems named as trajectory optimization ($\mathbf{P_T}$), resource allocation at UAV ($\mathbf{P_R}$) and offloading decisions at IoT devices ($\mathbf{P_O}$), then propose an iterative algorithm based on block coordinate descent method to cope with them in a sequence. Numerical results demonstrate that collaborative offloading can effectively reduce IoT devices’ energy consumption while meeting different kinds of offloading services, and satisfy the QoE requirement of time-sensitive tasks at IoT devices.

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Completion Time and Energy Optimization in the UAV-Enabled Mobile-Edge Computing System

Cited by 191 publications

References 35 publications

Air-Ground Integrated Mobile Edge Networks: A Survey

Air-Ground Integrated Mobile Edge Networks: A Survey

An Energy Efficient Design of Computation Offloading Enabled by UAV

Collaborative Offloading for UAV-enabled Time-Sensitive MEC Networks

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