Joint Resource Allocation and Trajectory Optimization in UAV-Enabled Wirelessly Powered MEC for Large Area

Zeng, Yaoping; Chen, Shisen; Cui, Yanpeng; Yang, Jie; Fu, Yin-juan

doi:10.1109/jiot.2023.3265507

Cited by 11 publications

(6 citation statements)

References 38 publications

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“…The SCA is an optimization technique that is particularly used for solving non-convex optimization problems. In the context of trajectory-aware offloading in UAV-aided MEC, the SCA is employed to iteratively solve complex optimization problems, such as resource allocation, trajectory planning, and offloading decision-making processes [94]. In this technique, the non-convex problem is broken down into a series of convex subprob- [57], AO [86], PDD [20], JSORT [87], BCD [88], DQN [15], DDQN [89], DDPG [90], MADDPG [91], MAPPO [92], MO-AVC [13], and GNN-A2C [93].…”

Section: Successive Convex Approximation (Sca)mentioning

confidence: 99%

Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey

Baidya,

Nabi,

Moh

2024

Sensors

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Recently, the integration of unmanned aerial vehicles (UAVs) with edge computing has emerged as a promising paradigm for providing computational support for Internet of Things (IoT) applications in remote, disaster-stricken, and maritime areas. In UAV-aided edge computing, the offloading decision plays a central role in optimizing the overall system performance. However, the trajectory directly affects the offloading decision. In general, IoT devices use ground offload computation-intensive tasks on UAV-aided edge servers. The UAVs plan their trajectories based on the task generation rate. Therefore, researchers are attempting to optimize the offloading decision along with the trajectory, and numerous studies are ongoing to determine the impact of the trajectory on offloading decisions. In this survey, we review existing trajectory-aware offloading decision techniques by focusing on design concepts, operational features, and outstanding characteristics. Moreover, they are compared in terms of design principles and operational characteristics. Open issues and research challenges are discussed, along with future directions.

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Section: Successive Convex Approximation (Sca)mentioning

confidence: 99%

Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey

Baidya,

Nabi,

Moh

2024

Sensors

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“…In some scenarios such as earthquakes, traffic jams, and remote areas, when the ground infrastructure is damaged and the computing and communication resources of edge facilities are insufficient, it is difficult for IoT devices to benefit from the quality of service [13]. To improve the quality of service for IoT devices, UAVs are widely studied by many scholars as edge nodes to collect and process data [14].…”

Section: Related Workmentioning

confidence: 99%

Joint UAV Deployment and Task Offloading Scheme for Multi-UAV-Assisted Edge Computing

Luo

Qiao

et al. 2023

Drones

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With the development of the Internet of Things (IoT), IoT devices are increasingly being deployed in scenarios with large footprints, remote locations, and complex geographic environments. In these scenarios, base stations are usually not easily deployed and are easily destroyed, so unmanned aerial vehicle (UAV)-based edge computing is a good solution. However, the UAV cannot accomplish the computing tasks and efficiently achieve better resource allocation considering the limited communication and computing resources of the UAV. In this paper, a multi-UAV-assisted mobile edge computing (MEC) system is considered where multiple UAVs cooperate to provide a service to IoT devices. We formulate an optimization function to minimize the energy consumption of a multi-UAV-assisted MEC system. The optimization function is a complex problem with non-convex and multivariate coupling. Thus, a joint UAV deployment and task scheduling optimization algorithm are designed to achieve optimal values of UAV numbers, the hovering position of each UAV, and the best strategy for offloading and resource allocation. Experimental results demonstrate that the algorithm has positive convergence performance and can accomplish more tasks under the constraint of delay compared to the two benchmark algorithms. The proposed algorithm can effectively reduce the system energy consumption compared to the two state-of-the-art algorithms.

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“…Optimization QoE in the U-MEC network while taking trajectory planning, channel condition, and offloading decision mode in user devices into consideration is still a significant challenge. Zeng et al [7] studied UAVs with energy harvesting and wireless power transfer (WPT) devices assisting user equipments (UEs) in task offloading within a large area, aiming to minimize device waiting delay. In [8], the authors aim to minimize the overall cost, including energy consumption, completion time, and maintenance cost of UAVs, by jointly optimizing the trajectories of UAVs and phase shifts of IRSs.…”

Section: Introductionmentioning

confidence: 99%

“…It has been proven that the transformed optimization problem is equivalent to the original when the penalty factor λ is sufficiently large [15]. Given that the binary constraint is no longer valid, the corresponding resource allocation constraints (7), (11) do not take into account the two different scenarios according m . Thus, P2.1 can be reformulated equivalently as follows:…”

mentioning

confidence: 99%

Enhancing QoE in Large-Scale U-MEC Networks via Joint Optimization of Task Offloading and UAV Trajectories

He,

Yang,

Shen

et al. 2024

Preprint

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Joint Resource Allocation and Trajectory Optimization in UAV-Enabled Wirelessly Powered MEC for Large Area

Cited by 11 publications

References 38 publications

Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey

Trajectory-Aware Offloading Decision in UAV-Aided Edge Computing: A Comprehensive Survey

Joint UAV Deployment and Task Offloading Scheme for Multi-UAV-Assisted Edge Computing

Enhancing QoE in Large-Scale U-MEC Networks via Joint Optimization of Task Offloading and UAV Trajectories

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