UAV-Enabled Mobile Edge Computing: Offloading Optimization and Trajectory Design

Zhou, Fuhui; Wu, Yongpeng; Sun, Haijian; Chu, Zheng

doi:10.1109/icc.2018.8422277

Cited by 172 publications

(100 citation statements)

References 19 publications

(51 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Notice that the WPT power of the UAV antenna is usually far greater than the communication transmit power of the IoTD antenna [34], [35]. If the IoTD transmits its data and the UAV transfers wireless energy at the same time, the communication signal will be drowned out by the WPT signal.…”

Section: B the Tdma Based Workflow Modelmentioning

confidence: 99%

See 1 more Smart Citation

Joint Resources and Workflow Scheduling in UAV-Enabled Wirelessly-Powered MEC for IoT Systems

Yang

Wang

et al. 2019

IEEE Trans. Veh. Technol.

180

View full text Add to dashboard Cite

This paper considers a UAV-enabled mobile edge computing (MEC) system, where a UAV first powers the Internet of things device (IoTD) by utilizing Wireless Power Transfer (WPT) technology. Then each IoTD sends the collected data to the UAV for processing by using the energy harvested from the UAV. In order to improve the energy efficiency of the UAV, we propose a new time division multiple access (TDMA) based workflow model, which allows parallel transmissions and executions in the UAV-assisted system. We aim to minimize the total energy consumption of the UAV by jointly optimizing the IoTDs association, computing resources allocation, UAV hovering time, wireless powering duration and the services sequence of the IoTDs. The formulated problem is a mixed-integer nonconvex problem, which is very difficult to solve in general. We transform and relax it into a convex problem and apply flow-shop scheduling techniques to address it. Furthermore, an alternative algorithm is developed to set the initial point closer to the optimal solution. Simulation results show that the total energy consumption of the UAV can be effectively reduced by the proposed scheme compared with the conventional systems.Index Terms-Internet of things, unmanned aerial vehicle (UAV), mobile edge computing (MEC), wireless power transfer (WPT), resources allocation, flow-shop scheduling.

show abstract

Section: B the Tdma Based Workflow Modelmentioning

confidence: 99%

“…In eq. (35a)-(35c), we divide the optimal solution F * as f * ij,a , f * ij,b and f * ij,c , respectively, in accordance with the three parts of µ's defined domain in (35). Let µ i,a , µ i,b and µ i,c represent three different kinds of µ i in (35) intervals.…”

Section: Computing Resources Allocationmentioning

confidence: 99%

Joint Resources and Workflow Scheduling in UAV-Enabled Wirelessly-Powered MEC for IoT Systems

Yang

Wang

et al. 2019

IEEE Trans. Veh. Technol.

180

View full text Add to dashboard Cite

show abstract

“…In [10], a UAV-enabled wireless-powered MEC system is studied and a power minimization problem is formulated by jointly optimizing the number of the offloading computation bits and the UAV flight path. A similar UAV-based MEC system is presented in [11], where the UAV trajectory was optimized under latency and UAVs energy budget constraints.…”

Section: Related Workmentioning

confidence: 99%

Edge Cloud Resource-aware Flight Planning for Unmanned Aerial Vehicles

Bekkouche

Taleb

Bagaa

et al. 2019

2019 IEEE Wireless Communications and Networking Conference (WCNC)

View full text Add to dashboard Cite

Unmanned Aerial Vehicles (UAVs) can offer a plethora of applications, provided that the appropriate ground control and complementary computing and storage services are available in close proximity. To accomplish this, edge cloud platforms, deployed at or close to the base stations, are essential. However, current UAV travel planning does not take into account the resource constraints of such edge cloud platforms. This paper introduces an aligned process for UAV flight planning and networking resource allocation, minimizing the total traveled distance. It proposes two solutions, namely (i) a Multi-access Edge Computing (MEC)-Aware UAVs' Path planning (MAUP) based on integer linear programming and (ii) an Accelerated MAUP (AMAUP), i.e., a heuristic and scalable approach that adopts the shortest weighted path algorithm considering directed graphs. The performance of the two solutions are evaluated using computer-based simulations and the obtained results demonstrate the effectiveness of the two solutions in achieving their design goals.

show abstract

“…In [9], the authors investigate a UAV-mounted cloudlet in which UAVs equipped with a computing processor offload and compute the tasks offloaded from ground devices. The authors in [10] study UAV-enabled wireless powered mobile edge computing system. The authors in [11] propose a relaying system that uses a UAV to store the processed data in a buffer and optimize the receiving and transmitting data size to minimize the energy consumption.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, x I is updated to the value given by max (x I , 1/(d i /r j * )) where j * = argmin j 1/r j , j ∈ J . The update of x I satisfies constraints (10) and (11). Otherwise, if the UAVs in J i do not satisfy (2) and 3, then, the tasks arriving after task i cannot be computed, i.e., y ij = 0.…”

mentioning

confidence: 99%

Online Optimization for UAV-Assisted Distributed Fog Computing in Smart Factories of Industry 4.0

Lee

Saad

Bennis

2018

2018 IEEE Global Communications Conference (GLOBECOM)

View full text Add to dashboard Cite

In this paper, the problem of unmanned aerial vehicle (UAV)-assisted fog computing in Industry 4.0 smart factories is studied. In particular, a novel online framework is proposed to enable a source UAV to offload computing tasks from ground sensors within a smart factory and allocate them to neighboring fog UAVs for distributed task computing, before the source UAV arrives at its destination. The online nature of the framework allows the UAVs to optimize their task allocation and decide on which neighbors to use for fog computing, even when the tasks are revealed to the source UAV in an online manner, and the information on future task arrivals is unknown. The proposed framework essentially maximizes the number of computed tasks by jointly considering the communication and computation latency. To solve the problem, an online greedy algorithm is designed and solved by using the primal-dual approach. Since the primal problem provides an upper bound of the original dual problem, the competitive ratio can be analytically derived as a function of the task sizes and the data rates of the UAVs. Simulation results show that the proposed online algorithm can achieve a near-optimal task allocation with an optimality gap that is no higher than 7.5% compared to the offline, optimal solution with complete knowledge of all tasks.

show abstract

UAV-Enabled Mobile Edge Computing: Offloading Optimization and Trajectory Design

Cited by 172 publications

References 19 publications

Joint Resources and Workflow Scheduling in UAV-Enabled Wirelessly-Powered MEC for IoT Systems

Joint Resources and Workflow Scheduling in UAV-Enabled Wirelessly-Powered MEC for IoT Systems

Edge Cloud Resource-aware Flight Planning for Unmanned Aerial Vehicles

Online Optimization for UAV-Assisted Distributed Fog Computing in Smart Factories of Industry 4.0

Contact Info

Product

Resources

About