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Satellite and UAV for Internet of Things (IoT)

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Engineering Remote Sensing".

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 24657

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Special Issue Editors

Dr. Rui Chen

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Guest Editor

School of Communication Engineering, Xidian University, Xi’an 710071, China
Interests: 5G/6G wireless communications; positioning and navigation; intelligent transportation

Prof. Dr. Nan Cheng

E-Mail Website
Guest Editor

School of Communication Engineering, Xidian University, Xi’an 710071, China
Interests: space-air-ground integrated networks; big data in vehicular networks; self-driving system

Special Issue Information

Dear Colleagues,

In recent years, a growing number of physical objects have been connected to the Internet at an unprecedented rate, calcifying the idea of the Internet of Things (IoT). In many paradigms of IoT applications, satellites and unmanned aerial vehicles (UAVs) for IoT have attracted a lot of attention and have experienced rapid development. As for satellites, due to their global positioning and ultra-long distance communication, they are particularly essential when sensors and actuators are located in remote areas without service from terrestrial access networks. As for UAVs, owing to their superiority in maneuverability and cost, they have found an increasingly wide utilization in many IoT scenarios such as express transportation, environment monitoring, and disaster relief.

This Special Issue aims at studies covering various technologies of satellites and UAVs in IoT applications. Topics may cover anything from space–air–ground integration, satellite communication, channel modeling, and communication protocols, to UAV swarm coordination, positioning, and trajectory design. Therefore, approaches to or studies on communication technology, and network security of satellites and UAVs in IoT applications, among other issues, are welcome.

Articles may address, but are not limited to, the following topics:

Satellite and UAV channel modeling
IoT communication protocols
Satellite communications
Positioning and navigation
Space-air-ground integration
5G/6G wireless communications
UAV swarm
UAV communications
UAV trajectory design
Network and physical layer security.

Dr. Rui Chen
Prof. Dr. Nan Cheng
Guest Editors

Manuscript Submission Information

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Keywords

IoT
satellite
UAV
space–air–ground integration
5G/6G

Published Papers (10 papers)

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23 pages, 2781 KiB

Open AccessArticle

Multi-Agent Deep Reinforcement Learning Framework Strategized by Unmanned Aerial Vehicles for Multi-Vessel Full Communication Connection

by Jiabao Cao, Jinfeng Dou, Jilong Liu, Xuanning Wei and Zhongwen Guo

Remote Sens. 2023, 15(16), 4059; https://doi.org/10.3390/rs15164059 - 16 Aug 2023

Cited by 1 | Viewed by 826

Abstract

In the Internet of Vessels (IoV), it is difficult for any unmanned surface vessel (USV) to work as a coordinator to establish full communication connections (FCCs) among USVs due to the lack of communication connections and the complex natural environment of the sea surface. The existing solutions do not include the employment of some infrastructure to establish USVs’ intragroup FCC while relaying data. To address this issue, considering the high-dimension continuous action space and state space of USVs, we propose a multi-agent deep reinforcement learning framework strategized by unmanned aerial vehicles (UAVs). UAVs can evaluate and navigate the multi-USV cooperation and position adjustment to establish a FCC. When ensuring FCCs, we aim to improve the IoV’s performance by maximizing the USV’s communication range and movement fairness while minimizing their energy consumption, which cannot be explicitly expressed in a closed-form equation. We transform this problem into a partially observable Markov game and design a separate actor–critic structure, in which USVs act as actors and UAVs act as critics to evaluate the actions of USVs and make decisions on their movement. An information transition in UAVs facilitates effective information collection and interaction among USVs. Simulation results demonstrate the superiority of our framework in terms of communication coverage, movement fairness, and average energy consumption, and that it can increase communication efficiency by at least 10% compared to DDPG, with the highest exceeding 120% compared to other baselines. Full article

(This article belongs to the Special Issue Satellite and UAV for Internet of Things (IoT))

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27 pages, 6551 KiB

Open AccessArticle

Cross-Domain Automatic Modulation Classification Using Multimodal Information and Transfer Learning

by Wen Deng, Qiang Xu, Si Li, Xiang Wang and Zhitao Huang

Remote Sens. 2023, 15(15), 3886; https://doi.org/10.3390/rs15153886 - 05 Aug 2023

Cited by 2 | Viewed by 1090

Abstract

Automatic modulation classification (AMC) based on deep learning (DL) is gaining increasing attention in dynamic spectrum access for 5G/6G wireless communications. However, inconsistent feature parameters between the training (source) and testing (target) data lead to performance degradation or even failure of existing DL-based AMC. The primary reason for this is the difficulty in obtaining sufficient labeled training data in the target domain. Therefore, we propose a novel cross-domain AMC algorithm based on multimodal information and transfer learning, utilizing abundant unlabeled target domain data. We achieve complementary gains by fusing multimodal information such as amplitude, phase, and spectrum, which are used to train a network. Additionally, we apply domain adversarial neural network technology from transfer learning to learn from a large number of unlabeled data samples in the target domain to address the issue of decreased accuracy in cross-domain AMC caused by differences in sampling rate, signal-to-noise ratio, and channel variations. Furthermore, we introduce class weight weighting and entropy weighting to solve the partial domain adaptation problem, considering that the target domain has fewer modulation signal classes than the source domain. Experimental results on two designed modulation datasets demonstrate improved performance gains, thus validating the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Satellite and UAV for Internet of Things (IoT))

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22 pages, 5621 KiB

Open AccessArticle

Energy-Efficient Multi-UAVs Cooperative Trajectory Optimization for Communication Coverage: An MADRL Approach

by Tianyong Ao, Kaixin Zhang, Huaguang Shi, Zhanqi Jin, Yi Zhou and Fuqiang Liu

Remote Sens. 2023, 15(2), 429; https://doi.org/10.3390/rs15020429 - 11 Jan 2023

Cited by 5 | Viewed by 3345

Abstract

Unmanned Aerial Vehicles (UAVs) can be deployed as aerial wireless base stations which dynamically cover the wireless communication networks for Ground Users (GUs). The most challenging problem is how to control multi-UAVs to achieve on-demand coverage of wireless communication networks while maintaining connectivity among them. In this paper, the cooperative trajectory optimization of UAVs is studied to maximize the communication efficiency in the dynamic deployment of UAVs for emergency communication scenarios. We transform the problem into a Markov game problem and propose a distributed trajectory optimization algorithm, Double-Stream Attention multi-agent Actor-Critic (DSAAC), based on Multi-Agent Deep Reinforcement Learning (MADRL). The throughput, safety distance, and power consumption of UAVs are comprehensively taken into account for designing a practical reward function. For complex emergency communication scenarios, we design a double data stream network structure that provides a capacity for the Actor network to process state changes. Thus, UAVs can sense the movement trends of the GUs as well as other UAVs. To establish effective cooperation strategies for UAVs, we develop a hierarchical multi-head attention encoder in the Critic network. This encoder can reduce the redundant information through the attention mechanism, which resolves the problem of the curse of dimensionality as the number of both UAVs and GUs increases. We construct a simulation environment for emergency networks with multi-UAVs and compare the effects of the different numbers of GUs and UAVs on algorithms. The DSAAC algorithm improves communication efficiency by 56.7%, throughput by 71.2%, energy saving by 19.8%, and reduces the number of crashes by 57.7%. Full article

(This article belongs to the Special Issue Satellite and UAV for Internet of Things (IoT))

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22 pages, 1674 KiB

Open AccessArticle

Adaptive Data Collection and Offloading in Multi-UAV-Assisted Maritime IoT Systems: A Deep Reinforcement Learning Approach

by Ziyi Liang, Yanpeng Dai, Ling Lyu and Bin Lin

Remote Sens. 2023, 15(2), 292; https://doi.org/10.3390/rs15020292 - 04 Jan 2023

Cited by 7 | Viewed by 2490

Abstract

This paper studies the integration of data collection and offloading for maritime Internet of Things (IoT) systems with multiple unmanned aerial vehicles (UAVs). In the considered multi-UAV maritime IoT system, the UAVs act as the aerial base stations to complete the missions of data collection from buoys and data offloading to the offshore base station (OBS). In this case, the UAVs need to adaptively select the mission mode between data collection and data offloading according to the network resources and mission requirements. In this paper, we aimed to minimize the completion time of data collection and offloading missions for all UAVs by jointly optimizing the UAV trajectories, mission mode selection, transmit power of buoys, and association relationships between the UAVs and buoy/OBS. In order to solve the mixed-integer non-convex minimization problem, we first designed a multi-agent deep reinforcement learning algorithm based on a hybrid discrete and continuous action space to preliminarily obtain the UAV trajectories, mission mode selection, and the transmit power of buoys. Then, we propose an algorithm based on the stable marriage problem to determine the buoy–UAV and UAV–OBS association relationships. Finally, the simulation results show that the proposed algorithms can effectively shorten the total mission completion time of data collection and offloading for the multi-UAV-assisted maritime IoT system. Full article

(This article belongs to the Special Issue Satellite and UAV for Internet of Things (IoT))

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18 pages, 4563 KiB

Open AccessArticle

A Real-Time Tracking Algorithm for Multi-Target UAV Based on Deep Learning

by Tao Hong, Hongming Liang, Qiye Yang, Linquan Fang, Michel Kadoch and Mohamed Cheriet

Remote Sens. 2023, 15(1), 2; https://doi.org/10.3390/rs15010002 - 20 Dec 2022

Cited by 10 | Viewed by 3896

Abstract

UAV technology is a basic technology aiming to help realize smart living and the construction of smart cities. Its vigorous development in recent years has also increased the presence of unmanned aerial vehicles (UAVs) in people’s lives, and it has been increasingly used in logistics, transportation, photography and other fields. However, the rise in the number of drones has also put pressure on city regulation. Using traditional methods to monitor small objects flying slowly at low altitudes would be costly and ineffective. This study proposed a real-time UAV tracking scheme that uses the 5G network to transmit UAV monitoring images to the cloud and adopted a machine learning algorithm to detect and track multiple targets. Aiming at the difficulties in UAV detection and tracking, we optimized the network structure of the target detector yolo4 (You Only Look Once V4) and improved the target tracker DeepSORT, adopting the detection-tracking mode. In order to verify the reliability of the algorithm, we built a data set containing 3200 pictures of four UAVs in different environments, conducted training and testing on the model, and achieved 94.35% tracking accuracy and 69FPS detection speed under the GPU environment. The model was then deployed on ZCU104 to prove the feasibility of the scheme. Full article

(This article belongs to the Special Issue Satellite and UAV for Internet of Things (IoT))

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23 pages, 1338 KiB

Open AccessArticle

UAV-Assisted Fair Communication for Mobile Networks: A Multi-Agent Deep Reinforcement Learning Approach

by Yi Zhou, Zhanqi Jin, Huaguang Shi, Zhangyun Wang, Ning Lu and Fuqiang Liu

Remote Sens. 2022, 14(22), 5662; https://doi.org/10.3390/rs14225662 - 09 Nov 2022

Cited by 2 | Viewed by 1991

Abstract

Unmanned Aerial Vehicles (UAVs) can be employed as low-altitude aerial base stations (UAV-BSs) to provide communication services for ground users (GUs). However, most existing works mainly focus on optimizing coverage and maximizing throughput, without considering the fairness of the GUs in communication services. This may result in certain GUs being underserviced by UAV-BSs in pursuit of maximum throughput. In this paper, we study the problem of UAV-assisted communication with the consideration of user fairness. We first design a Ratio Fair (RF) metric by weighting fairness and throughput to evaluate the tradeoff between fairness and communication efficiency when UAV-BSs serve GUs. The problem is formulated as a mixed-integer non-convex optimization problem based on the RF metric and we propose a UAV-Assisted Fair Communication (UAFC) algorithm based on multi-agent deep reinforcement learning to maximize the fair throughput of the system. The UAFC algorithm comprehensively considers fair throughput, UAV-BSs coverage, and flight status to design a reasonable reward function. In addition, the UAFC algorithm establishes an information sharing mechanism based on gated functions by sharing neural networks, which effectively reduces the distributed decision-making uncertainty of UAV-BSs. To reduce the impact of state dimension imbalance on the convergence of the algorithm, we design a new state decomposing and coupling actor network architecture. Simulation results show that the proposed UAFC algorithm increases fair throughput by 5.62%, 26.57% and fair index by 1.99%, 13.82% compared to the MATD3 and MADDPG algorithms, respectively. Meanwhile, UAFC can also meet energy consumption limitation and network connectivity requirement. Full article

(This article belongs to the Special Issue Satellite and UAV for Internet of Things (IoT))

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23 pages, 1714 KiB

Open AccessArticle

ConstDet: Control Semantics-Based Detection for GPS Spoofing Attacks on UAVs

by Xiaomin Wei, Cong Sun, Minjie Lyu, Qipeng Song and Yue Li

Remote Sens. 2022, 14(21), 5587; https://doi.org/10.3390/rs14215587 - 05 Nov 2022

Cited by 7 | Viewed by 2313

Abstract

UAVs are widely used in agriculture, the military, and industry. However, it is easy to perform GPS spoofing attacks on UAVs, which can lead to catastrophic consequences. In this paper, we propose ConstDet, a control semantics-based detection approach for GPS spoofing attacks of UAVs using machine learning algorithms. Various real experiments are conducted to collect real flight data, on the basis of which ConstDet is designed as a practical detection framework. To train models for the detection of GPS spoofing attacks, specified flight data types are selected as features based on the control semantics, including the altitude control process and the horizontal position control process, since these data are able to represent the dynamic flight and control processes. Multiple machine learning algorithms are used to train and generate the best classifier for GPS spoofing attacks. ConstDet is further implemented and deployed on a real UAV to support onboard detection. Experiments and evaluations validate that ConstDet can effectively detect GPS spoofing attacks and the detection rate can reach

97.70 %

. The experimental comparison demonstrates that ConstDet has better performance than existing detection approaches. Full article

(This article belongs to the Special Issue Satellite and UAV for Internet of Things (IoT))

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26 pages, 1962 KiB

Open AccessArticle

Joint Flying Relay Location and Routing Optimization for 6G UAV–IoT Networks: A Graph Neural Network-Based Approach

by Xiucheng Wang, Lianhao Fu, Nan Cheng, Ruijin Sun, Tom Luan, Wei Quan and Khalid Aldubaikhy

Remote Sens. 2022, 14(17), 4377; https://doi.org/10.3390/rs14174377 - 03 Sep 2022

Cited by 32 | Viewed by 3099

Abstract

Unmanned aerial vehicles (UAVs) are widely used in Internet-of-Things (IoT) networks, especially in remote areas where communication infrastructure is unavailable, due to flexibility and low cost. However, the joint optimization of locations of UAVs and relay path selection can be very challenging, especially when the numbers of IoT devices and UAVs are very large. In this paper, we formulate the joint optimization of UAV locations and relay paths in UAV-relayed IoT networks as a graph problem, and propose a graph neural network (GNN)-based approach to solve it in an efficient and scalable way. In the training procedure, we design a reinforcement learning-based relay GNN (RGNN) to select the best relay path for each user. The theoretical analysis shows that the time complexity of RGNN is two orders lower than the conventional optimization method. Then, we jointly exploit location GNN (LGNN) and RGNN trained to optimize the locations of all UAVs. Both GNNs can be trained without relying on the training data, which is usually unavailable in the context of wireless networks. In inference procedure, LGNN is first used to optimize the location of UAVs, and then RGNN is used to select the best relay path based on the output of LGNN. Simulation results show that the proposed approach can achieve comparable performance to brute-force search with much lower time complexity when the network is relatively small. Remarkably, the proposed approach is highly scalable to large-scale networks and adaptable to dynamics in the environment, which can hardly be achieved using conventional methods. Full article

(This article belongs to the Special Issue Satellite and UAV for Internet of Things (IoT))

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20 pages, 743 KiB

Open AccessArticle

Deep Reinforcement Learning Based Freshness-Aware Path Planning for UAV-Assisted Edge Computing Networks with Device Mobility

by Yingsheng Peng, Yong Liu, Dong Li and Han Zhang

Remote Sens. 2022, 14(16), 4016; https://doi.org/10.3390/rs14164016 - 18 Aug 2022

Cited by 7 | Viewed by 1920

Abstract

As unmanned aerial vehicles (UAVs) can provide flexible and efficient services concerning the sparse network distribution, we study a UAV-assisted mobile edge computing (MEC) network. To satisfy the freshness requirement of IoT applications, the age of information (AoI) is incorporated as an important performance metric. Then, the path planning problem is formulated to simultaneously minimize the AoIs of mobile devices and the energy consumption of the UAV, where the movement randomness of IoT devices are taken into account. Concerning the dimension explosion, the deep reinforcement learning (DRL) framework is exploited, and a double deep Q-learning network (DDQN) algorithm is proposed to realize the intelligent and freshness-aware path planning of the UAV. Extensive simulation results validate the effectiveness of the proposed freshness-aware path planning scheme and unveil the effects of the moving speed of devices and the UAV on the achieved AoI. Full article

(This article belongs to the Special Issue Satellite and UAV for Internet of Things (IoT))

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16 pages, 4280 KiB

Open AccessTechnical Note

Load Estimation Based Dynamic Access Protocol for Satellite Internet of Things

by Mingchuan Yang, Guanchang Xue, Botao Liu, Yupu Yang and Yanyong Su

Remote Sens. 2022, 14(24), 6402; https://doi.org/10.3390/rs14246402 - 19 Dec 2022

Cited by 2 | Viewed by 1319

Abstract

In recent years, the Internet of Things (IoT) industry has become a research hotspot. With the advancement of satellite technology, the satellite Internet of Things is further developed along with a new generation of information technology and commercial markets. However, existing random access protocols cannot cope with the access of a large number of sensors and short burst transmissions. The current satellite Internet of Things application scenarios are divided into two categories, one has only sensor nodes and no sink nodes, and the other has sink nodes. A time-slot random access protocol based on Walsh code is proposed for the satellite Internet-of-Things scenario with sink nodes. In this paper, the load estimation algorithm is used to reduce the resource occupancy rate in the case of medium and low load, and a dynamic Walsh code slot random access protocol is proposed to select the appropriate Walsh code length and frame length h. The simulation results show that the slotted random access protocol based on Walsh code can effectively improve the throughput of the system under high load. The introduction of load estimation in the case of medium and low load can effectively reduce the resource utilization of the system, and ensure that the performance of the access protocol based on Walsh codes does not deteriorate. However, in the case of high load, a large resource overhead is still required to ensure the access performance of the system. Full article

(This article belongs to the Special Issue Satellite and UAV for Internet of Things (IoT))

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