Application and Challenges of UAV in Space-Air-Ground Integrated Communication Network

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Communications".

Deadline for manuscript submissions: closed (16 January 2024) | Viewed by 9588

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Guest Editor
Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
Interests: semantic computing; future internet architecture; network virtualization; artificial intelligence for networking
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Guest Editor
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: satellite communication; space reconnaissance; array signal processing

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Guest Editor
Faculty of Computing and Information Technology, Sohar University, Sohar P.O. Box 44, Oman
Interests: energy efficiency; Internet of Things; edge and cloud infrastructure; scheduling and resource management; algorithms, machine learning; mobile edge computing
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Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China
Interests: radio wave propagation over fading channels; wave scattering in random media; optical wireless communications
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Special Issue Information

Dear Colleagues,

At present, the continuous progress of information and network technology has become the leading force of innovation-driven development, which has a profound impact on the political, military, economic, and cultural fields, as well as others. It is driving the transformation and reconstruction of the social system. As an important information infrastructure supporting social development, the ground information network and space-based information network have been developed independently for a long time, with increasingly prominent limitations. It is difficult to meet the needs of global network coverage, security, autonomous control, and flexible access for various users. In recent years, the information network has been gradually integrated and developed. Relevant research has put forward the development vision of the space–space integrated information network and the space–space integrated wireless communication network based on the 6th generation mobile communication technology (6G). As a new network architecture paradigm, the technical development of the space–air–ground integrated network has gradually become a main research topic.

UAV has a wide range of applications and many advantages in mobile communication. However, due to its small size and limited load capacity and energy storage, it cannot work for a long time in a long distance. Therefore, the research on energy supply and low power consumption is still the main problem.

This topic aims to bring together relevant researchers from the industry and academia to share their latest discoveries and developments in this field. The topics of interest include, but are not limited to, the following:

  • Space–air–ground integrated networks: review and prospect.
  • Optimal deployment of THE gateway and SDN controller in space–air–ground integrated network.
  • Optimizing space–air–ground integrated networks by artificial intelligence.
  • Resource allocation and trajectory optimization of UAV security communication system.
  • Energy efficient resource allocation for UAV-assisted space–air–ground Internet of remote things networks.
  • Computing over space–air–ground integrated networks.
  • Algorithm for optimizing the throughput of space–air–ground integrated network.
  • Research on UAV networking technology in space–air–ground integration networks.
  • Space–air–ground integration network technology based on software definition.
  • The multi-layer architecture of space–air–ground integrated network for Internet of Things.
  • Routing protocol of space–air–ground integrated network.
  • Task scheduling in space–air–ground integrated network.
  • UAV communication link selection strategy in the space–air–ground integrated network.
  • Mobile edge computing technology for space–air–ground integrated network.
  • UAV auxiliary communication deployment strategy based on reinforcement learning.

Dr. Peiying Zhang
Prof. Dr. Sheng Wu
Dr. Zakarya Muhammad
Prof. Dr. Guanjun Xu
Guest Editors

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Keywords

  • space–air–ground integrated network
  • mobile edge computing
  • UAV
  • communication network

Published Papers (4 papers)

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Research

19 pages, 465 KiB  
Article
Joint Task Offloading and Resource Allocation for Space–Air–Ground Collaborative Network
by Chengli Mei, Cheng Gao, Heng Wang, Yanxia Xing, Ningyao Ju and Bo Hu
Drones 2023, 7(7), 482; https://doi.org/10.3390/drones7070482 - 21 Jul 2023
Cited by 1 | Viewed by 1385
Abstract
The space–air–ground collaborative network can provide computing service for ground users in remote areas by deploying edge servers on satellites and high-altitude platform (HAP) drones. However, with the growing number of ground devices required to be severed, it becomes imperative to address the [...] Read more.
The space–air–ground collaborative network can provide computing service for ground users in remote areas by deploying edge servers on satellites and high-altitude platform (HAP) drones. However, with the growing number of ground devices required to be severed, it becomes imperative to address the issue of spectrum demand for the HAP drone to meet the access of a large number of users. In addition, the long propagation distance between devices and the HAP drone, and between the HAP drone and LEO satellites, will lead to high data transmission energy consumption. Motivated by these factors, we introduce a space–air–ground collaborative network that employs the non-orthogonal multiple access (NOMA) technique, enabling all ground devices to access the HAP drone. Therefore, all devices can share the same communication spectrum. Furthermore, the HAP drone can process part of the ground devices’ tasks locally, and offload the rest to satellites within the visible range for processing. Based on this system, we formulate a weighted energy consumption minimization problem considering power control, computing frequency allocation, and task-offloading decision. The problem is solved by the proposed low-complexity iterative algorithm. Specifically, the original problem is decomposed into interconnected coupled subproblems using the block coordinate descent (BCD) method. The first subproblem is to optimize power control and computing frequency allocation, which is solved by a convex algorithm after a series of transformations. The second subproblem is to make an optimal task-offloading strategy, and we solve it using the concave–convex procedure (CCP)-based algorithm after penalty-based transformation on binary variables. Simulation results verify the convergence and performance of the proposed iterative algorithm compared with the two benchmark algorithms. Full article
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20 pages, 1429 KiB  
Article
Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing
by Hongxia Zhang, Shiyu Xi, Hongzhao Jiang, Qi Shen, Bodong Shang and Jian Wang
Drones 2023, 7(6), 383; https://doi.org/10.3390/drones7060383 - 7 Jun 2023
Cited by 12 | Viewed by 2587
Abstract
In emergency situations, such as earthquakes, landslides and other natural disasters, the terrestrial communications infrastructure is severely disrupted and unable to provide services to terrestrial IoT devices. However, tasks in emergency scenarios often require high levels of computing power and energy supply that [...] Read more.
In emergency situations, such as earthquakes, landslides and other natural disasters, the terrestrial communications infrastructure is severely disrupted and unable to provide services to terrestrial IoT devices. However, tasks in emergency scenarios often require high levels of computing power and energy supply that cannot be processed quickly enough by devices locally and require computational offloading. In addition, offloading tasks to server-equipped edge base stations may not always be feasible due to the lack of infrastructure or distance. Since Low Orbit Satellites (LEO) have abundant computing resources, and Unmanned Aerial Vehicles (UAVs) have flexible deployment, offloading tasks to LEO satellite edge servers via UAVs becomes straightforward, which provides computing services to ground-based devices. Therefore, this paper investigates the computational tasks and resource allocation in a UAV-assisted multi-layer LEO satellite network, taking into account satellite computing resources and device task volumes. In order to minimise the weighted sum of energy consumption and delay in the system, the problem is formulated as a constrained optimisation problem, which is then transformed into a Markov Decision Problem (MDP). We propose a UAV-assisted airspace integration network architecture, and a Deep Deterministic Policy Gradient and Long short-term memory (DDPG-LSTM)-based task offloading and resource allocation algorithm to solve the problem. Simulation results demonstrate that the solution outperforms the baseline approach and that our framework and algorithm have the potential to provide reliable communication services in emergency situations. Full article
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23 pages, 6198 KiB  
Article
Study on the Evolution Law of Overlying Strata Structure in Stope Based on “Space–Air–Ground” Integrated Monitoring Network and Discrete Element
by Yuanhao Zhu, Yueguan Yan, Yanjun Zhang, Wanqiu Zhang, Jiayuan Kong and Anjin Dai
Drones 2023, 7(5), 309; https://doi.org/10.3390/drones7050309 - 5 May 2023
Cited by 4 | Viewed by 1258
Abstract
The geological environmental damage caused by coal mining has become a hot issue in current research. Especially in the western mining area, the size of the mining working face is large, the mining intensity is high, while the surface movement and deformation are [...] Read more.
The geological environmental damage caused by coal mining has become a hot issue in current research. Especially in the western mining area, the size of the mining working face is large, the mining intensity is high, while the surface movement and deformation are more intense and wider. Therefore, it is necessary to effectively monitor the surface using appropriate means and carrying out research on the overlying strata structure of the stope. In this paper, by using advantages of various subsidence monitoring technologies and the technical framework of the Internet of Things (IoT), a “space–air–ground” integrated collaborative monitoring network is constructed. The evolution law of overlying strata structure is studied based on discrete element simulations and theoretical analysis. Furthermore, a discrete element mechanical parameter inversion method is proposed. The main results, using numerical simulations, are as follows: The mean square error of monitoring surface subsidence is 33.2 mm, the mean square error of mechanical parameter inversion is 13.4 mm, and relative error is as low as 3.8%. The surface subsidence law of adjacent mining under different working face widths and interval coal pillar widths is revealed. The Boltzmann function model of surface subsidence ratio changing with width–depth ratio and the calculation formula of width reduction coefficient of adjacent mining working face are inverted. The critical failure width of the interval coal pillar is determined as 20.5 m. Based on the theory of “arch–beam” structure and numerical simulation results, the overlying strata structure model of adjacent mining in the mining area is constructed. The research results can provide technical support or theoretical reference for mining damage monitoring, subsidence control, and prediction in western mines. Full article
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14 pages, 2174 KiB  
Article
Deep Reinforcement Learning Based Computation Offloading in UAV-Assisted Edge Computing
by Peiying Zhang, Yu Su, Boxiao Li, Lei Liu, Cong Wang, Wei Zhang and Lizhuang Tan
Drones 2023, 7(3), 213; https://doi.org/10.3390/drones7030213 - 19 Mar 2023
Cited by 6 | Viewed by 2618
Abstract
Traditional multi-access edge computing (MEC) often has difficulty processing large amounts of data in the face of high computationally intensive tasks, so it needs to offload policies to offload computation tasks to adjacent edge servers. The computation offloading problem is a mixed integer [...] Read more.
Traditional multi-access edge computing (MEC) often has difficulty processing large amounts of data in the face of high computationally intensive tasks, so it needs to offload policies to offload computation tasks to adjacent edge servers. The computation offloading problem is a mixed integer programming non-convex problem, and it is difficult to have a good solution. Meanwihle, the cost of deploying servers is often high when providing edge computing services in remote areas or some complex terrains. In this paper, the unmanned aerial vehicle (UAV) is introduced into the multi-access edge computing network, and a computation offloading method based on deep reinforcement learning in UAV-assisted multi-access edge computing network (DRCOM) is proposed. We use the UAV as the space base station of MEC, and it transforms computation task offloading problems of MEC into two sub-problems: find the optimal solution of whether each user’s device is offloaded through deep reinforcement learning; allocate resources. We compared our algorithm with other three offloading methods, i.e., LC, CO, and LRA. The maximum computation rate of our algorithm DRCOM is 142.38% higher than LC, 50.37% higher than CO, and 12.44% higher than LRA. The experimental results demonstrate that DRCOM greatly improves the computation rate. Full article
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