User Association and Path Planning for UAV-Aided Mobile Edge Computing With Energy Restriction

Qian, Yuwen; Wang, Feifei; Li, Jun; Shi, Long; Cai, Kui; Shu, Feng

doi:10.1109/lwc.2019.2913843

Cited by 87 publications

(37 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Also, Du et al [27] study the energy efficiency of the UAV in a MEC system, by minimizing the hovering energy and computation energy of the UAV. In addition, the work [28] study the problem described as the offloading bits from users to UAV maximization, subject to each user's quality of service (QoS). These existing works related to UAV-assisted MEC systems mainly focus on the computing bits offloading only between UAV(s) and users.…”

Section: Introductionmentioning

confidence: 99%

Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT

Zhang

Loo

et al. 2020

IEEE Trans. Ind. Inf.

333

108

View full text Add to dashboard Cite

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent concept, where a UAV equipped with a MEC server is deployed to serve a number of terminal devices (TDs) of Internet of Things (IoT) in a finite period. In this paper, each TD has a certain latency-critical computation task in each time slot to complete. Three computation strategies can be available to each TD. First, each TD can operate local computing by itself. Second, each TD can partially offload task bits to the UAV for computing. Third, each TD can choose to offload task bits to access point (AP) via UAV relaying. We propose a new optimization problem formulation that aims to minimize the total energy consumption including communication-related energy, computation-related energy and UAV's flight energy by optimizing the bits allocation, time slot scheduling and power allocation as well as UAV trajectory design. As the formulated problem is nonconvex and difficult to find the optimal solution, we solve the problem by two parts, and obtain the near optimal solution with within a dozen of iterations. Finally, numerical results are given to validate the proposed algorithm, which is verified to be efficient and superior to the other benchmark cases.

show abstract

Section: Introductionmentioning

confidence: 99%

Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT

Zhang

Loo

et al. 2020

IEEE Trans. Ind. Inf.

333

108

View full text Add to dashboard Cite

show abstract

“…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.

Self Cite

222

View full text Add to dashboard Cite

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.

show abstract

“…Different from conventional ground base stations (BSs), UAVs are capable of flexible movement, ondemand deployment as well as high probability of line-of-sight (LoS) wireless links [3]. Thus, it has many appealing applications, e.g., serving as relays to provide ubiquitous communications for remote mobile users, resuming Internet access for special areas, aggregating or delivering content items in IoT applications [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Latency-Minimized Design of secure transmissions in UAV-Aided Communications

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations to provide communication service for remote mobile users due to their high mobility and flexible deployment. However, the lineof-sight (LoS) wireless links are vulnerable to be intercepted by the eavesdropper (Eve), which presents a major challenge for UAV-aided communications. In this paper, we propose a latency-minimized transmission scheme for satisfying legitimate users' (LUs') content requests securely against Eve. By leveraging physical-layer security (PLS) techniques, we formulate a transmission latency minimization problem by jointly optimizing the UAV trajectory and user association. The resulting problem is a mixed-integer nonlinear program (MINLP), which is known to be NP hard. Furthermore, the dimension of optimization variables is indeterminate, which again makes our problem very challenging. To efficiently address this, we utilize bisection to search for the minimum transmission delay and introduce a variational penalty method to address the associated subproblem via an inexact block coordinate descent approach. Moreover, we present a characterization for the optimal solution. Simulation results are provided to demonstrate the superior performance of the proposed design.

show abstract

User Association and Path Planning for UAV-Aided Mobile Edge Computing With Energy Restriction

Cited by 87 publications

References 9 publications

Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT

Joint Computation and Communication Design for UAV-Assisted Mobile Edge Computing in IoT

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

Latency-Minimized Design of secure transmissions in UAV-Aided Communications

Contact Info

Product

Resources

About