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The Convergence of Remote Sensing, Communication, and Computing for 6G Space-Air-Ground Integrated Networks

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Satellite Missions for Earth and Planetary Exploration".

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 23436

Special Issue Editors

Prof. Dr. Chen Chen

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Guest Editor
State Key Laboratory of Integrated Services Networks, Xidian University, No. 2 Taibai Road, Xi'an 70071, China
Interests: Internet of Things; intelligent transportation systems; wireless communications
Special Issues, Collections and Topics in MDPI journals
Dr. Ying Ju

E-Mail Website
Guest Editor
State Key Laboratory of Integrated Services Networks, Xidian University, No. 2 Taibai Road, Xi’an 70071, China
Interests: wireless communication security; mmWave commiunications; satellite networks
Prof. Dr. Shaohua Wan

E-Mail Website
Guest Editor
Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China
Interests: deep learning; Internet of Things; edge computing
Special Issues, Collections and Topics in MDPI journals
Dr. Stefano Berretti

E-Mail Website
Guest Editor
Media Integration and Communication Center (MICC) & Department of Information Engineering (DINFO), University of Firenze, Via S. Marta 3, 50139 Firenze, Italy
Interests: multimedia; 3D computer vision; articifial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Michele Nappi

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Guest Editor
Department of Computer Science, University of Salerno Fisciano, Via Giovanni Paolo II, 132, 84084 Fisciano, SA, Italy
Interests: multibiometric systems; pattern recognition; image processing; compression and indexing; multimedia databases; human-computer interaction; VR/AR

Special Issue Information

Dear Colleagues,

As the trajectory of human mobility continues to expand, the need for communication in deserts, oceans, and other places is also increasing. In this context, global communication coverage can be achieved through 6G space–air–ground integration technology. For the high Earth orbit (HEO) satellites, the transmission delay over long distances has greatly affected the development of satellite communication technology. At the same time, in terms of wide-area Internet of Things (IoT) perception, such as environmental monitoring, smart agriculture, and intelligent power grid, the low Earth orbit (LEO) satellite platform only serves as a collection and return facility for IoT nodes. Massive data still need to be sent back to the ground stations for processing. Meanwhile, detecting and tracking high-mobility targets are not timely enough, and further improvement is needed in terms of low service delay and timely remote sensing information feedback. This requires the guarantee of satellite computing capability. The integration of edge computing technology into the satellite communication network and remote sensing will greatly reduce the service time, improve the quality of satellite communication service, and strengthen the satellite task processing capability so as to enhance the performance of the space–air–ground integrated network (SAGIN) to a greater extent.

Prof. Dr. Chen Chen
Dr. Ying Ju
Prof. Dr. Shaohua Wan
Dr. Stefano Berretti
Prof. Dr. Michele Nappi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • integration of remote sensing, communication, and computing for 6G SAGIN
  • distributed intelligence-aided sensing, communication, and computing for 6G SAGIN
  • blockchain-empowered 6G SAGIN
  • security and privacy issues for the integration of sensing, communication, and computing in 6G SAGIN
  • sensing and communication coexistence/spectrum sharing in 6G SAGIN
  • system-level simulation, prototyping, and field tests for 6G SAGIN

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Published Papers (7 papers)

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Research

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20 pages, 3582 KiB  
Article
A Node Selection Strategy in Space-Air-Ground Information Networks: A Double Deep Q-Network Based on the Federated Learning Training Method
by Weidong Wang, Siqi Li, Jihao Zhang, Dan Shan, Guangwei Zhang and Xiang Gao
Remote Sens. 2024, 16(4), 651; https://doi.org/10.3390/rs16040651 - 9 Feb 2024
Viewed by 1331
Abstract
The Space-Air-Ground Information Network (SAGIN) provides extensive coverage, enabling global connectivity across a diverse array of sensors, devices, and objects. These devices generate large amounts of data that require advanced analytics and decision making using artificial intelligence techniques. However, traditional deep learning approaches [...] Read more.
The Space-Air-Ground Information Network (SAGIN) provides extensive coverage, enabling global connectivity across a diverse array of sensors, devices, and objects. These devices generate large amounts of data that require advanced analytics and decision making using artificial intelligence techniques. However, traditional deep learning approaches encounter drawbacks, primarily, the requirement to transmit substantial volumes of raw data to central servers, which raises concerns about user privacy breaches during transmission. Federated learning (FL) has emerged as a viable solution to these challenges, addressing both data volume and privacy issues effectively. Nonetheless, the deployment of FL faces its own set of obstacles, notably the excessive delay and energy consumption caused by the vast number of devices and fluctuating channel conditions. In this paper, by considering the heterogeneity of devices and the instability of the network state, the delay and energy consumption models of each round of federated training are established. Subsequently, we introduce a strategic node selection approach aimed at minimizing training costs. Building upon this, we propose an innovative, empirically driven Double Deep Q Network (DDQN)-based algorithm called low-cost node selection in federated learning (LCNSFL). The LCNSFL algorithm can assist edge servers in selecting the optimal set of devices to participate in federated training before the start of each round, based on the collected system state information. This paper culminates with a simulation-based comparison, showcasing the superior performance of LCNSFL against existing algorithms, thus underscoring its efficacy in practical applications. Full article
(This article belongs to the Special Issue The Convergence of Remote Sensing, Communication, and Computing for 6G Space-Air-Ground Integrated Networks)
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20 pages, 919 KiB  
Article
TD3-Based Optimization Framework for RSMA-Enhanced UAV-Aided Downlink Communications in Remote Areas
by Tri-Hai Nguyen, Luong Vuong Nguyen, L. Minh Dang, Vinh Truong Hoang and Laihyuk Park
Remote Sens. 2023, 15(22), 5284; https://doi.org/10.3390/rs15225284 - 8 Nov 2023
Cited by 3 | Viewed by 1485
Abstract
The need for reliable wireless communication in remote areas has led to the adoption of unmanned aerial vehicles (UAVs) as flying base stations (FlyBSs). FlyBSs hover over a designated area to ensure continuous communication coverage for mobile users on the ground. Moreover, rate-splitting [...] Read more.
The need for reliable wireless communication in remote areas has led to the adoption of unmanned aerial vehicles (UAVs) as flying base stations (FlyBSs). FlyBSs hover over a designated area to ensure continuous communication coverage for mobile users on the ground. Moreover, rate-splitting multiple access (RSMA) has emerged as a promising interference management scheme in multi-user communication systems. In this paper, we investigate an RSMA-enhanced FlyBS downlink communication system and formulate an optimization problem to maximize the sum-rate of users, taking into account the three-dimensional FlyBS trajectory and RSMA parameters. To address this continuous non-convex optimization problem, we propose a TD3-RFBS optimization framework based on the twin-delayed deep deterministic policy gradient (TD3). This framework overcomes the limitations associated with the overestimation issue encountered in the deep deterministic policy gradient (DDPG), a well-known deep reinforcement learning method. Our simulation results demonstrate that TD3-RFBS outperforms existing solutions for FlyBS downlink communication systems, indicating its potential as a solution for future wireless networks. Full article
(This article belongs to the Special Issue The Convergence of Remote Sensing, Communication, and Computing for 6G Space-Air-Ground Integrated Networks)
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20 pages, 25160 KiB  
Article
Edge Consistency Feature Extraction Method for Multi-Source Image Registration
by Yang Zhou, Zhen Han, Zeng Dou, Chengbin Huang, Li Cong, Ning Lv and Chen Chen
Remote Sens. 2023, 15(20), 5051; https://doi.org/10.3390/rs15205051 - 21 Oct 2023
Cited by 2 | Viewed by 1460
Abstract
Multi-source image registration has often suffered from great radiation and geometric differences. Specifically, grayscale and texture from similar landforms in different source images often show significantly different visual features, and these differences disturb the corresponding point extraction in the following image registration process. [...] Read more.
Multi-source image registration has often suffered from great radiation and geometric differences. Specifically, grayscale and texture from similar landforms in different source images often show significantly different visual features, and these differences disturb the corresponding point extraction in the following image registration process. Considering that edges between heterogeneous images can provide homogeneous information and more consistent features can be extracted based on image edges, an edge consistency radiation-change insensitive feature transform (EC-RIFT) method is proposed in this paper. Firstly, the noise and texture interference are reduced by preprocessing according to the image characteristics. Secondly, image edges are extracted based on phase congruency, and an orthogonal Log-Gabor filter is performed to replace the global algorithm. Finally, the descriptors are built with logarithmic partition of the feature point neighborhood, which improves the robustness of the descriptors. Comparative experiments on datasets containing multi-source remote sensing image pairs show that the proposed EC-RIFT method outperforms other registration methods in terms of precision and effectiveness. Full article
(This article belongs to the Special Issue The Convergence of Remote Sensing, Communication, and Computing for 6G Space-Air-Ground Integrated Networks)
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21 pages, 3933 KiB  
Article
Infrared and Visible Image Homography Estimation Based on Feature Correlation Transformers for Enhanced 6G Space–Air–Ground Integrated Network Perception
by Xingyi Wang, Yinhui Luo, Qiang Fu, Yun Rui, Chang Shu, Yuezhou Wu, Zhige He and Yuanqing He
Remote Sens. 2023, 15(14), 3535; https://doi.org/10.3390/rs15143535 - 13 Jul 2023
Cited by 3 | Viewed by 1574
Abstract
The homography estimation of infrared and visible images, a key technique for assisting perception, is an integral element within the 6G Space–Air–Ground Integrated Network (6G SAGIN) framework. It is widely applied in the registration of these two image types, leading to enhanced environmental [...] Read more.
The homography estimation of infrared and visible images, a key technique for assisting perception, is an integral element within the 6G Space–Air–Ground Integrated Network (6G SAGIN) framework. It is widely applied in the registration of these two image types, leading to enhanced environmental perception and improved efficiency in perception computation. However, the traditional estimation methods are frequently challenged by insufficient feature points and the low similarity in features when dealing with these images, which results in poor performance. Deep-learning-based methods have attempted to address these issues by leveraging strong deep feature extraction capabilities but often overlook the importance of precisely guided feature matching in regression networks. Consequently, exactly acquiring feature correlations between multi-modal images remains a complex task. In this study, we propose a feature correlation transformer method, devised to offer explicit guidance for feature matching for the task of homography estimation between infrared and visible images. First, we propose a feature patch, which is used as a basic unit for correlation computation, thus effectively coping with modal differences in infrared and visible images. Additionally, we propose a novel cross-image attention mechanism to identify correlations between varied modal images, thus transforming the multi-source images homography estimation problem into a single-source images problem by achieving source-to-target image mapping in the feature dimension. Lastly, we propose a feature correlation loss (FCL) to induce the network into learning a distinctive target feature map, further enhancing source-to-target image mapping. To validate the effectiveness of the newly proposed components, we conducted extensive experiments to demonstrate the superiority of our method compared with existing methods in both quantitative and qualitative aspects. Full article
(This article belongs to the Special Issue The Convergence of Remote Sensing, Communication, and Computing for 6G Space-Air-Ground Integrated Networks)
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21 pages, 731 KiB  
Article
Distributed Coordination of Space–Ground Multiresources for Remote Sensing Missions
by Runzi Liu, Xu Ding, Weihua Wu and Wei Guo
Remote Sens. 2023, 15(13), 3362; https://doi.org/10.3390/rs15133362 - 30 Jun 2023
Cited by 3 | Viewed by 1037
Abstract
As data relay satellites (DRSs) play an increasingly important supporting role in remote sensing missions, efficient coordination across space–ground multiresources becomes a significant problem. Owing to the implementation problem of the centralized coordinate methods, this paper studies a distributed coordinate resource scheduling method [...] Read more.
As data relay satellites (DRSs) play an increasingly important supporting role in remote sensing missions, efficient coordination across space–ground multiresources becomes a significant problem. Owing to the implementation problem of the centralized coordinate methods, this paper studies a distributed coordinate resource scheduling method which is realizable in the current space network structure. To be specific, we first formulate the multiple resource coordination problem into an MILP problem based on a modified time-expanded graph. Then, the problem is transferred and decomposed into subproblems for remote sensing satellite (RSS) systems and DRS systems to solve distributedly. Afterwards, we propose a distributed iterative scheme for the RSS systems and DRS systems based on alternating direction method of multipliers (ADMM), in which only the schedule information of the inter-satellite links are required to exchange between RSS systems and DRS systems. Simulation results are provided to validate the effectiveness of our distributed coordinated resource scheduling algorithm. Full article
(This article belongs to the Special Issue The Convergence of Remote Sensing, Communication, and Computing for 6G Space-Air-Ground Integrated Networks)
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21 pages, 3266 KiB  
Article
DRL-Based Load-Balancing Routing Scheme for 6G Space–Air–Ground Integrated Networks
by Feihu Dong, Jiaxin Song, Yasheng Zhang, Yuqi Wang and Tao Huang
Remote Sens. 2023, 15(11), 2801; https://doi.org/10.3390/rs15112801 - 28 May 2023
Cited by 3 | Viewed by 2119
Abstract
Due to the rapid development of the space–air–ground integrated network (SAGIN), a satellite communication system has the advantages of wide coverage and low requirements for a geographical environment and is gradually becoming the main competitive technology for 6G. The low-earth-orbit (LEO) satellite network [...] Read more.
Due to the rapid development of the space–air–ground integrated network (SAGIN), a satellite communication system has the advantages of wide coverage and low requirements for a geographical environment and is gradually becoming the main competitive technology for 6G. The low-earth-orbit (LEO) satellite network has the characteristics of low transmission delay, small propagation loss, and global coverage, and its exploration has become the main research object of contemporary satellite communications. However, traditional routing algorithms cannot adapt to the characteristics of the high dynamics and load-balancing requirements of LEO satellite networks. In this paper, a load-balancing routing algorithm for LEO satellites based on Deep Q-Network (DQN-LLRA) is proposed by using deep reinforcement learning. Making use of the model obtained by the DQN training, satellite nodes can select the best routing results according to the delay, bandwidth, and queue utilization of the surrounding satellite nodes. The simulation and analysis show that the path load obtained by the proposed algorithm is low. Compared with the Q-learning-based algorithm, this algorithm reduces the maximum queue utilization rate of the routing path by 5%, reduces the average queue utilization rate of the routing path by 13%, and effectively balances the load in the network. Full article
(This article belongs to the Special Issue The Convergence of Remote Sensing, Communication, and Computing for 6G Space-Air-Ground Integrated Networks)
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Review

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36 pages, 28226 KiB  
Review
An Overview of Emergency Communication Networks
by Qian Wang, Wenfeng Li, Zheqi Yu, Qammer Abbasi, Muhammad Imran, Shuja Ansari, Yusuf Sambo, Liwen Wu, Qiang Li and Tong Zhu
Remote Sens. 2023, 15(6), 1595; https://doi.org/10.3390/rs15061595 - 15 Mar 2023
Cited by 22 | Viewed by 12690
Abstract
In recent years, major natural disasters and public safety accidents have frequently occurred worldwide. In order to deal with various disasters and accidents using rapidly deployable, reliable, efficient, and stable emergency communication networks, all countries in the world are strengthening and improving emergency [...] Read more.
In recent years, major natural disasters and public safety accidents have frequently occurred worldwide. In order to deal with various disasters and accidents using rapidly deployable, reliable, efficient, and stable emergency communication networks, all countries in the world are strengthening and improving emergency communication network construction and related technology research. Motivated by these situations, in this paper, we provide a state-of-the-art survey of the current situation and development of emergency communication networks. In this detailed investigation, our primary focus is the extensive discussion of emergency communication network technology, including satellite networks, ad hoc networks, cellular networks, and wireless private networks. Then, we explore and analyze the networks currently applied in emergency rescue, such as the 370M narrowband private network, broadband cluster network, and 5G constellation plan. We propose a broadband-narrowband integrated emergency communication network to provide an effective solution for visual dispatch of emergency rescue services. The main findings derived from the comprehensive survey on the emergency communication network are then summarized, and possible research challenges are noted. Lastly, we complete this survey by shedding new light on future directions for the emergency communication network. In the future, the emergency network will develop in the direction of intelligence, integration, popularization, and lower cost, and space-air-ground-sea integrated networks. This survey provides a reference basis for the construction of networks to mitigate major natural disasters and public safety accidents. Full article
(This article belongs to the Special Issue The Convergence of Remote Sensing, Communication, and Computing for 6G Space-Air-Ground Integrated Networks)
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