Online Edge Learning Offloading and Resource Management for UAV-Assisted MEC Secure Communications

Ding, Yu; Feng, Yunqi; Lu, Weidang; Zheng, Shilian; Zhao, Nan; Meng, Limin; Nallanathan, Arumugam; Yang, Xiaoniu

doi:10.1109/jstsp.2022.3222910

Cited by 69 publications

(11 citation statements)

References 31 publications

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“…It can be seen that the mission of using multiple UAVs and USVs to collect data over a large area has not been thoroughly studied so far. However, nowadays UAV-assisted MEC technology, as referred to in [39], has been gradually matured to facilitate more efcient data acquisition.…”

Section: Related Workmentioning

confidence: 99%

Data Collection System of IoT Based on the Coordination of Drones and Unmanned Surface Vehicle

Wang

Tian

Lin

et al. 2023

Journal of Advanced Transportation

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With the continuous renewal and rise of various flight equipment, this article designs a method of water data collection, which is realized through the coordination of an unmanned surface vehicle (USV) carrying unmanned aerial vehicles (UAVs, commonly called drones). In reality, multiple UAVs take-off and landing sites, energy consumption, and other complex issues closely related to USV recovery, UAVs must be considered. If mixed integer linear programming (MILP) is used directly, it is difficult to get satisfactory results in an acceptable time range. Therefore, a heuristic algorithm is proposed to solve the problem of data collection by these devices, which can not only quickly solve the large-scale collaborative optimization problem, but also rationally utilize the total energy consumption of the equipment. The effectiveness of the proposed algorithm is verified and analyzed by experimenting a different number of monitoring nodes, different number of UAVs and different task time. It can not only shorten the cycle of data acquisition task, but also reduce the waiting time of the UAV and USV.

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Section: Related Workmentioning

confidence: 99%

Data Collection System of IoT Based on the Coordination of Drones and Unmanned Surface Vehicle

Wang

Tian

Lin

et al. 2023

Journal of Advanced Transportation

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“…In [14], the authors propose the reinforcement learning-based approaches for secured computation offloading to the UAV edge. In [15], the authors maximize the secure computation efficiency in UAV edge system by jointly optimizing offloading decision and resource management.…”

Section: Related Workmentioning

confidence: 99%

Robust Trajectory and Offloading for Energy-Efficient UAV Edge Computing in Industrial Internet of Things

Tang¹,

Zhang²,

Zhang³

et al. 2023

Preprint

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Efficient data processing and computation are essential for the industrial Internet of things (IIoT) to empower various applications, which yet can be significantly bottlenecked by the limited energy capacity and computation capability of the IIoT nodes. In this paper, we employ an unmanned aerial vehicle (UAV) as an edge server to assist IIoT data processing, while considering the practical issue of UAV jittering. Specifically, we propose a joint design on trajectory and offloading strategies to minimize energy consumption due to local and edge computation, as well as data transmission. We particularly address the UAV jittering that induces Gaussian-distributed uncertainties associated with flying waypoints, resulting in probabilistic-form flying speed and data offloading constraints. We exploit the Bernstein-type inequality to reformulate the constraints in deterministic forms and decompose the energy minimization to solve for trajectory and offloading separately within an alternating optimization framework. The subproblems are then tackled with the successive convex approximation technique. Simulation results show that our proposal strictly guarantees robustness under uncertainties and effectively reduces energy consumption as compared with the baselines.

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“…To provide remote areas with the same telecommunication services as large cities, it is impractical to use only terrestrial cellular networks for coverage. Multi-beam satellite communication systems that operate in high frequency bands, have high throughput, and provide broadband Internet access services are indispensable [1][2][3][4][5][6][7][8]. Multibeam satellite system has been studied because of the frequency reuse accomplished in the satellite network, channel capacity of which has been increased [4].…”

Section: Introductionmentioning

confidence: 99%

“…Highthroughput satellite systems are aimed at providing fixed and mobile personal communication services covering the world. Mobile communication in the future is also developing towards the seamless integration of satellite and ground communication systems, and the differences between the two systems will no longer exist [5]. The future mobile communication is developing towards the seamless integration of satellite and terrestrial communication systems, and multi-beam satellites have become an important research direction [6].…”

Section: Introductionmentioning

confidence: 99%

Channel capacity limit of multi-beam satellite system

Yang

Liu²,

Yang³

et al. 2023

Wireless Netw

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The requests of broadband coverage area and the expected user demand is satisfied by the satellite industry by using multiple spot beams of high throughput satellites with fixed multi-beam pattern and footprint planning. The technology core of high-throughput satellite is multi-beam technology, and the means to efficiently reuse spectrum is a full-frequency reuse scheme. In order to study the channel capacity limit of multi-beam satellite systems, the Wiener-Gaussian access model of terrestrial cellular systems is analyzed in this paper. This paper analyzes the Wiener-Gaussian access model of terrestrial cellular systems, then considers the difference between the spatial characteristics and gain characteristics of the multi-beam satellite system compared with the terrestrial cellular system, and proposes a Wiener model suitable for the multi-beam satellite communication system. On this basis, the channel capacity limit of multi-beam satellites under different fading conditions is deduced when using optimal linear precoding and minimum mean square error precoding. It provides theoretical support for the in-depth study of the channel capacity mechanism and system performance of multi-beam satellites.

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Online Edge Learning Offloading and Resource Management for UAV-Assisted MEC Secure Communications

Cited by 69 publications

References 31 publications

Data Collection System of IoT Based on the Coordination of Drones and Unmanned Surface Vehicle

Data Collection System of IoT Based on the Coordination of Drones and Unmanned Surface Vehicle

Robust Trajectory and Offloading for Energy-Efficient UAV Edge Computing in Industrial Internet of Things

Channel capacity limit of multi-beam satellite system

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