Completion Time and Energy Consumption Minimization for UAV-Enabled Multicasting

Song, Qing; Jin, Shi; Zheng, Fu‐Chun

doi:10.1109/lwc.2019.2894684

Cited by 50 publications

(39 citation statements)

References 8 publications

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“…The work [18] applies the genetic algorithm to design the trajectory with the least energy consumption to visit all BSs and return to the UAV station. Reference [19] minimizes the completion time and energy consumption problems for a fixed-wing UAV-enabled multicasting system via jointly optimizing the flying speed, UAV altitude, and antenna beamwidth. In [20], the authors consider the joint problem of the sensor nodes' wake-up schedule and the trajectory to minimize the maximum energy consumption while guaranteeing the reliability of the data collected from the sensors.…”

Section: Introductionmentioning

confidence: 99%

Coarse Trajectory Design for Energy Minimization in UAV-Enabled

Tran

Chatzinotas

et al. 2020

IEEE Trans. Veh. Technol.

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In this paper, we design the UAV trajectory to minimize the total energy consumption while satisfying the requested timeout (RT) requirement and energy budget, which is accomplished via jointly optimizing the path and UAV's velocities along subsequent hops. The corresponding optimization problem is difficult to solve due to its non-convexity and combinatorial nature. To overcome this difficulty, we solve the original problem via two consecutive steps. Firstly, we propose two algorithms, namely heuristic search, and dynamic programming (DP) to obtain a feasible set of paths without violating the GU's RT requirements based on the traveling salesman problem with time window (TSPTW). Then, they are compared with exhaustive search and traveling salesman problem (TSP) used as reference methods. While the exhaustive algorithm achieves the best performance at a high computation cost, the heuristic algorithm exhibits poorer performance with low complexity. As a result, the DP is proposed as a practical trade-off between the exhaustive and heuristic algorithms. Specifically, the DP algorithm results in near-optimal performance at a much lower complexity. Secondly, for given feasible paths, we propose an energy minimization problem via a joint optimization of the UAV's velocities along subsequent hops. Finally, numerical results are presented to demonstrate the effectiveness of our proposed algorithms. The results show that the DP-based algorithm approaches the exhaustive search's performance with a significantly reduced complexity. It is also shown that the proposed solutions outperform the stateof-the-art benchmarks in terms of both energy consumption and outage performance.

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Section: Introductionmentioning

confidence: 99%

Coarse Trajectory Design for Energy Minimization in UAV-Enabled

Tran

Chatzinotas

et al. 2020

IEEE Trans. Veh. Technol.

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“…Also, UAV has the potential to provide computation for the remote devices [1], which called as UAV-assisted/enabled MEC. By exploiting the mobility, UAV can be utilized to shorten the transmit latency [10]- [13], which provides the potential to tackle with the latency issue in MEC systems mentioned above. Besides the communication time cost, as discussed in [10]- [13], UAVassisted MEC incurs an additional time overhead on computation that has to be carefully designed since the UAV has limited computation capability.…”

Section: Introductionmentioning

confidence: 99%

“…By exploiting the mobility, UAV can be utilized to shorten the transmit latency [10]- [13], which provides the potential to tackle with the latency issue in MEC systems mentioned above. Besides the communication time cost, as discussed in [10]- [13], UAVassisted MEC incurs an additional time overhead on computation that has to be carefully designed since the UAV has limited computation capability. In addition, note that the deterministic LoS channel following the free-space pathloss model links are used as the UAV-ground channel model for the literatures mentioned above, which is practically inaccurate in urban/suburban environment.…”

Section: Introductionmentioning

confidence: 99%

Completion Time Minimization for UAV-Assisted Mobile-Edge Computing Systems

Zhang

Loo

et al. 2021

IEEE Trans. Veh. Technol.

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The explosive computation demands in the Internet of Things (IoT) have triggered the research interests on unmanned aerial vehicle (UAV) assisted mobile-edge computing (MEC) systems even though there are still many challenges, such as computing delay requirement, multi-UAV cooperation, and resource management. This letter focuses on the computing delay issue in MEC systems assisted by multiple UAVs with the goal of task completion time minimization. In particular, both the partial offloading a nd b inary o ffloading mo des ar e co nsidered by jointly optimizing time slot size, terminal devices scheduling, computation resource allocation, and UAVs' trajectories. Particularly, an non-LoS channel model is adopted for UAV-ground communication. To handle the formulated problems, we develop alternating optimization algorithms by invoking the successive convex approximation method, Karush-Kuhn-Tucker conditions and penalized method. Numerical results show that the completion time is significantly decreased by the proposed algorithms.

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“…Specifically, reference [28] proposes a fly-and-hovercommunication protocol that users are sequentially served by the UAV as it hovers above the center of each cell. In order to be energy efficient, Song et al [29] further propose a fly-and-communication protocol where the UAV flies with a zigzag pattern to cover the given area. Completion time and energy consumption minimization problem is formulated by jointly optimizing the flying speed, altitude, and beamwidth.…”

Section: Introductionmentioning

confidence: 99%

“…Completion time and energy consumption minimization problem is formulated by jointly optimizing the flying speed, altitude, and beamwidth. However, the proposed protocols in [28] and [29] are strictly sub-optimal since they do not make full use of the UAV mobility to enhance system performance. In [30], for a UAV-enabled wireless power transfer (WPT) system, the minimum energy of two users is maximized over a limited charging period via the joint design of 3D trajectory and beamwidth.…”

Section: Introductionmentioning

confidence: 99%

Joint Maneuver and Beamwidth Optimization for UAV-Enabled Multicasting

Tang

et al. 2019

IEEE Access

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Unmanned aerial vehicles (UAVs)-assisted communications have been an essential complement of conventional wireless networks. In this paper, we consider a UAV-enabled multicasting system, where a UAV with a directional antenna of adjustable beamwidth is employed to disseminate a common file to a group of ground users. By minimizing the mission completion time, we investigate how beamwidth control would affect the UAV's three-dimensional (3D) location/trajectory. First, we consider the quasistationary UAV scenario, where the UAV is deployed at a static location. In this case, we jointly optimize the 3D UAV location and antenna beamwidth under the practical constraints on the UAV's altitude and beamwidth, while ensuring that all users are covered by the main lobe of the UAV antenna. Although this problem is nonconvex, its global optimality can be obtained by using a two-step algorithm, where semiclosed solutions of the 3D location and beamwidth are derived. Next, in the mobile UAV scenario, a joint 3D UAV trajectory and beamwidth design is proposed, additionally constrained by the horizontal and vertical speed. To tackle this nonconvex problem, we develop an iterative optimization algorithm based on successive convex approximation techniques, which updates the 3D trajectory and beamwidth simultaneously in each iteration. Numerical results demonstrate the effectiveness of the proposed design as compared to benchmark schemes.

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Completion Time and Energy Consumption Minimization for UAV-Enabled Multicasting

Cited by 50 publications

References 8 publications

Coarse Trajectory Design for Energy Minimization in UAV-Enabled

Coarse Trajectory Design for Energy Minimization in UAV-Enabled

Completion Time Minimization for UAV-Assisted Mobile-Edge Computing Systems

Joint Maneuver and Beamwidth Optimization for UAV-Enabled Multicasting

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