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A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 21845

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Special Issue Editors

Dr. Wenzheng Xu

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Guest Editor

College of Computer Science, Sichuan University, Chengdu 510006, China
Interests: IoT networks; UAV networks; social networks; approximation algorithm

Prof. Dr. Tang Liu

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Guest Editor

College of Computer Science, Sichuan Normal University, Chengdu 610101, China
Interests: IoT networks and mobile computing

Prof. Dr. Weifa Liang

E-Mail Website
Guest Editor

Department of Computer Science, City University of Hong Kong, Hong Kong
Interests: wireless sensor networks; mobile edge computing; network function virtulization; Internet of Things

Special Issue Information

Dear Colleagues,

Traditional Internet of Things (IoT) networks have limited coverage areas due to economical deployment cost constraints, and may break down due to the disfunction of a few key IoT devices. Unmanned aerial vehicles (UAVs) can significantly extend the coverage of IoT networks and help relay information among different components of a broken IoT network. UAV-assisted IoT networks are perfect for environmental monitoring, disaster area monitoring, battlefield monitoring, intelligent transportation, power line inspection, emergent communication networking, etc. UAV-assisted IoT networks have some unique characteristics, such as relative large sensing and communication ranges due to far less obstacles for UAVs in the air, limited energy, and computing capacity due to limited payloads of most commercial UAVs, as well as heterogeneous sensing/computing/energy resources of different UAVs.

This Special Issue focuses on latest research solutions to efficient sensing, data collections, data transmissions, and computing in UAV-assisted IoT networks, such as cooperative flying trajectory scheduling of multiple UAVs, fast and dynamic UAV networking, coordination between monitoring UAVs and communication UAVs, the charging scheduling of UAVs, simultaneous sensing and charging, flying edge computing, etc.

Papers are solicited in areas related to these topics, including, but not limited to, the following:

The path scheduling of multiple UAVs for mobile sensing and data collection;
Efficient, dynamic, robust, and/or fair UAV networking;
Energy and sensing resource management of UAVs;
Real-time (edge) computing scheduling in UAV-assisted networks;
Spectrum management in UAV-assisted networks.

Dr. Wenzheng Xu
Prof. Dr. Tang Liu
Prof. Dr. Weifa Liang
Guest Editors

Manuscript Submission Information

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Keywords

Internet of Things
UAVs
mobile sensing
mobile data collection
UAV networking
UAV edge computing

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Published Papers (10 papers)

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Research

23 pages, 1763 KiB

Open AccessArticle

FedRDR: Federated Reinforcement Distillation-Based Routing Algorithm in UAV-Assisted Networks for Communication Infrastructure Failures

by Jie Li, Anqi Liu, Guangjie Han, Shuang Cao, Feng Wang and Xingwei Wang

Drones 2024, 8(2), 49; https://doi.org/10.3390/drones8020049 - 4 Feb 2024

Cited by 2 | Viewed by 1605

Abstract

Traditional Internet of Things (IoT) networks have limited coverage and may experience failures due to natural disasters affecting critical IoT devices, making it difficult for them to provide communication services. Therefore, how to establish network communication service more efficiently in the presence of fault points is the problem we solve in this paper. To address this issue, this study constructs a hierarchical multi-domain data transmission architecture for an emergency network with unmanned aerial vehicles (UAVs) employed as core communication devices. This architecture expands the functionality of UAVs as key network devices and provides a theoretical basis for their feasibility as intelligent network controllers and switches. Firstly, the UAV controllers perceive the network status and learn the spatio-temporal characteristics of air-to-ground network links. Secondly, a routing algorithm within the domain based on federated reinforcement distillation (FedRDR) is developed, which enhances the generalization capability of the routing decision model by increasing the training data samples. Simulation experiments are conducted, and the results show that the average communication data size between each domain controller and the server is approximately 45.3 KB when using the FedRDR algorithm. Compared to the transmission of parameters through federated reinforcement learning algorithms, FedRDR reduces the transmitted parameter size by approximately 29%. Therefore, the FedRDR routing algorithm helps to facilitate knowledge transfer, accelerate the training process of intelligent agents within the domain, and reduce communication costs in resource-constrained scenarios for UAV networks and has practical value. Full article

(This article belongs to the Special Issue UAV-Assisted Internet of Things)

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14 pages, 3210 KiB

Open AccessArticle

Optimizing Topology in Satellite–UAV Collaborative IoT: A Graph Partitioning Simulated Annealing Approach

by Ming Zhuo, Yiming Feng, Peng Yang, Zhiwen Tian, Leyuan Liu and Shijie Zhou

Drones 2024, 8(2), 44; https://doi.org/10.3390/drones8020044 - 1 Feb 2024

Viewed by 2038

Abstract

Currently, space-based information networks, represented by satellite Internet, are rapidly developing. UAVs can serve as airborne mobile terminals, representing a novel node in satellite IoT, offering more accurate and robust data streaming for connecting global satellite–UAV collaborative IoT systems. It is characterized by high-speed dynamics, with node distances and visibility constantly changing over time. Therefore, there is a need for faster and higher-quality topology optimization research. A reliable, secure, and adaptable network topology optimization algorithm has been proposed to handle various complex scenarios. Additionally, considering the dynamic and time-varying nature of these types of networks, the concept of time slices has been introduced to accelerate the iterative efficiency of problem-solving. Experimental results demonstrate that the proposed algorithm is expected to exhibit better convergence and performance in subsequent iterations compared with traditional solutions. Besides being a solution for topology optimization, the proposed algorithm offers a new way of thinking, enabling the handling of larger satellite–UAV collaborative IoT systems. Full article

(This article belongs to the Special Issue UAV-Assisted Internet of Things)

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17 pages, 2661 KiB

Open AccessArticle

A Novel Adversarial Detection Method for UAV Vision Systems via Attribution Maps

by Zhun Zhang, Qihe Liu, Chunjiang Wu, Shijie Zhou and Zhangbao Yan

Drones 2023, 7(12), 697; https://doi.org/10.3390/drones7120697 - 7 Dec 2023

Cited by 1 | Viewed by 2025

Abstract

With the rapid advancement of unmanned aerial vehicles (UAVs) and the Internet of Things (IoTs), UAV-assisted IoTs has become integral in areas such as wildlife monitoring, disaster surveillance, and search and rescue operations. However, recent studies have shown that these systems are vulnerable to adversarial example attacks during data collection and transmission. These attacks subtly alter input data to trick UAV-based deep learning vision systems, significantly compromising the reliability and security of IoTs systems. Consequently, various methods have been developed to identify adversarial examples within model inputs, but they often lack accuracy against complex attacks like C&W and others. Drawing inspiration from model visualization technology, we observed that adversarial perturbations markedly alter the attribution maps of clean examples. This paper introduces a new, effective detection method for UAV vision systems that uses attribution maps created by model visualization techniques. The method differentiates between genuine and adversarial examples by extracting their unique attribution maps and then training a classifier on these maps. Validation experiments on the ImageNet dataset showed that our method achieves an average detection accuracy of 99.58%, surpassing the state-of-the-art methods. Full article

(This article belongs to the Special Issue UAV-Assisted Internet of Things)

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12 pages, 8529 KiB

Open AccessCommunication

UAV-Aided Wireless Energy Transfer for Sustaining Internet of Everything in 6G

by Yueling Che, Zeyu Zhao, Sheng Luo, Kaishun Wu, Lingjie Duan and Victor C. M. Leung

Drones 2023, 7(10), 628; https://doi.org/10.3390/drones7100628 - 9 Oct 2023

Cited by 4 | Viewed by 2269

Abstract

Unmanned aerial vehicles (UAVs) are a promising technology used to provide on-demand wireless energy transfer (WET) and sustain various low-power ground devices (GDs) for the Internet of Everything (IoE) in sixth generation (6G) wireless networks. However, an individual UAV has limited battery energy, which may confine the required wide-range mobility in a complex IoE scenario. Furthermore, the heterogeneous GDs in IoE applications have distinct non-linear energy harvesting (EH) properties and diversified energy and/or communication demands, which poses new requirements on the WET and trajectory design of UAVs. In this article, to reflect the non-linear EH properties of GDs, we propose the UAV’s effective-WET zone (E-zone) above each GD, where a GD is assured to harvest non-zero energy from the UAV only when the UAV transmits into the E-zone. We then introduce the free space optics (FSO) powered UAV with enhanced mobility, and propose its adaptive WET for the GDs with non-linear EH. Considering the time urgency of the different energy demands of the GDs, we propose a new metric called the energy latency time, which is the time duration that a GD can wait before becoming fully charged. By proposing the energy-demand aware UAV trajectory, we further present a novel hierarchical WET scheme to meet the GDs’ diversified energy latency time. Moreover, to efficiently sustain IoE communications, the multi-UAV enabled WET is employed by unleashing their cooperative diversity gain and the joint design with the wireless information transfer (WIT). The numerical results show that our proposed multi-UAV cooperative WET scheme under the energy-aware trajectory design achieves the shortest task completion time as compared to the state-of-the-art benchmarks. Finally, the new directions for future research are also provided. Full article

(This article belongs to the Special Issue UAV-Assisted Internet of Things)

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18 pages, 3609 KiB

Open AccessArticle

Learning Template-Constraint Real-Time Siamese Tracker for Drone AI Devices via Concatenation

by Zhewei Wu, Qihe Liu, Shijie Zhou, Shilin Qiu, Zhun Zhang and Yi Zeng

Drones 2023, 7(9), 592; https://doi.org/10.3390/drones7090592 - 20 Sep 2023

Cited by 1 | Viewed by 1528

Abstract

Significant progress has been made in object tracking tasks thanks to the application of deep learning. However, current deep neural network-based object tracking methods often rely on stacking sub-modules and introducing complex structures to improve tracking accuracy. Unfortunately, these approaches are inefficient and limit the feasibility of deploying efficient trackers on drone AI devices. To address these challenges, this paper introduces ConcatTrk, a high-speed object tracking method designed specifically for drone AI devices. ConcatTrk utilizes a lightweight network architecture, enabling real-time tracking on edge devices. Specifically, the proposed method primarily uses the concatenation operation to construct its core tracking steps, including multi-scale feature fusion, intra-frame feature matching, and dynamic template updating, which aim to reduce the computational overhead of the tracker. To ensure tracking performance in UAV tracking scenarios, ConcatTrk implements a learnable feature matching operator along with a simple and efficient template constraint branch, which enables accurate tracking by discriminatively matching features and incorporating periodic template updates. Results of comprehensive experiments on popular benchmarks, including UAV123, OTB100, and LaSOT, show that ConcatTrk has achieved promising accuracy and attained a tracking speed of 41 FPS on an edge AI device, Nvidia AGX Xavier. ConcatTrk runs 8× faster than the SOTA tracker TransT while using 4.9× fewer FLOPs. Real-world tests on the drone platform have strongly validated its practicability, including real-time tracking speed, reliable accuracy, and low power consumption. Full article

(This article belongs to the Special Issue UAV-Assisted Internet of Things)

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16 pages, 1362 KiB

Open AccessEditor’s ChoiceArticle

DECCo-A Dynamic Task Scheduling Framework for Heterogeneous Drone Edge Cluster

by Zhiyang Zhang, Die Wu, Fengli Zhang and Ruijin Wang

Drones 2023, 7(8), 513; https://doi.org/10.3390/drones7080513 - 3 Aug 2023

Cited by 3 | Viewed by 1941

Abstract

The heterogeneity of unmanned aerial vehicle (UAV) nodes and the dynamic service demands make task scheduling particularly complex in the drone edge cluster (DEC) scenario. In this paper, we provide a universal intelligent collaborative task scheduling framework, named DECCo, which schedules dynamically changing task requests for the heterogeneous DEC. Benefiting from the latest advances in deep reinforcement learning (DRL), DECCo autonomously learns task scheduling strategies with high response rates and low communication latency through a collaborative Advantage Actor–Critic algorithm, which avoids the interference of resource overload and local downtime while ensuring load balancing. To better adapt to the real drone collaborative scheduling scenario, DECCo switches between heuristic and DRL-based scheduling solutions based on real-time scheduling performance, thus avoiding suboptimal decisions that severely affect Quality of Service (QoS) and Quality of Experience (QoE). With flexible parameter control, DECCo can adapt to various task requests on drone edge clusters. Google Cluster Usage Traces are used to verify the effectiveness of DECCo. Therefore, our work represents a state-of-the-art method for task scheduling in the heterogeneous DEC. Full article

(This article belongs to the Special Issue UAV-Assisted Internet of Things)

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18 pages, 739 KiB

Open AccessArticle

A Q-Learning-Based Two-Layer Cooperative Intrusion Detection for Internet of Drones System

by Moran Wu, Zhiliang Zhu, Yunzhi Xia, Zhengbing Yan, Xiangou Zhu and Nan Ye

Drones 2023, 7(8), 502; https://doi.org/10.3390/drones7080502 - 1 Aug 2023

Cited by 6 | Viewed by 1343

Abstract

The integration of unmanned aerial vehicles (UAVs) and the Internet of Things (IoT) has opened up new possibilities in various industries. However, with the increasing number of Internet of Drones (IoD) networks, the risk of network attacks is also rising, making it increasingly difficult to identify malicious attacks on IoD systems. To improve the accuracy of intrusion detection for IoD and reduce the probability of false positives and false negatives, this paper proposes a Q-learning-based two-layer cooperative intrusion detection algorithm (Q-TCID). Specifically, Q-TCID employs an intelligent dynamic voting algorithm that optimizes multi-node collaborative intrusion detection strategies at the host level, effectively reducing the probability of false positives and false negatives in intrusion detection. Additionally, to further reduce energy consumption, an intelligent auditing algorithm is proposed to carry out system-level auditing of the host-level detections. Both algorithms employ Q-learning optimization strategies and interact with the external environment in their respective Markov decision processes, leading to close-to-optimal intrusion detection strategies. Simulation results demonstrate that the proposed Q-TCID algorithm optimizes the defense strategies of the IoD system, effectively prolongs the mean time to failure (MTTF) of the system, and significantly reduces the energy consumption of intrusion detection. Full article

(This article belongs to the Special Issue UAV-Assisted Internet of Things)

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26 pages, 2181 KiB

Open AccessArticle

Optimization Algorithms for UAV-and-MUV Cooperative Data Collection in Wireless Sensor Networks

by Yu Lu, Yi Hong, Chuanwen Luo, Deying Li and Zhibo Chen

Drones 2023, 7(7), 408; https://doi.org/10.3390/drones7070408 - 21 Jun 2023

Cited by 3 | Viewed by 1875

Abstract

The deployment of unmanned aerial vehicles (UAVs) has significantly improved the efficiency of data collection for wireless sensor networks (WSNs). The freshness of collected information from sensors can be measured by the age of information (AoI), which is an important factor to consider in data collection. For data collection during long-term mission, the energy limitation of UAVs may cause mission interruption, which makes supplementation of the UAVs’ energy more necessary. To this end, we introduce the mobile unmanned vehicle (MUV) to guarantee the UAVs’ energy supplementation. In this paper, we investigate the problem of multi-UAVs and single-MUV cooperative trajectory planning (MUSM-CTP) for data collection in WSNs with consideration for the AoI the collected data and the limited battery capacity of UAVs. The objective of this problem is to find cooperative flight trajectories for multiple UAVs and to determine the MUV’s travel plan to replace batteries for the UAVs, such that the average AoI of all collected data is minimized. We prove the NP-hardness of the problem and design the algorithm via three phases to solve this: determining candidate hover points based on the affinity propagation (AP) clustering method, constructing the flight trajectories of multiple UAVs based on the genetic algorithm (GA), and designing a travel plan for the MUV. The simulation results verify the effectiveness of the proposed algorithm in improving the freshness of the information collected from all of the sensors. Full article

(This article belongs to the Special Issue UAV-Assisted Internet of Things)

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25 pages, 847 KiB

Open AccessArticle

Optimal Energy and Delay Tradeoff in UAV-Enabled Wireless Sensor Networks

by Jiapin Xie, Qiyong Fu, Riheng Jia, Feilong Lin, Ming Li and Zhonglong Zheng

Drones 2023, 7(6), 368; https://doi.org/10.3390/drones7060368 - 1 Jun 2023

Cited by 6 | Viewed by 1864

Abstract

Unmanned aerial vehicles (UAVs) are promising in large-area data collection due to their flexibility and easy maintenance. In this work, we study a UAV-enabled wireless sensor network (WSN), where K UAVs are dispatched to collect a certain amount of data from each node on the ground. Most existing works assume that the flight energy is either distance-related or duration-related, which may not suit the practical scenario. Given the practical speed-related flight energy model, we focus on deriving the optimal energy and delay tradeoff for the K UAVs such that each node can successfully upload a certain amount of data to one of the K UAVs. Intuitively, the higher flight speed of the UAV results in the shorter completion time of the data collection task, which may however cause the higher flight energy consumption of UAVs during the task. Specifically, we first model the total energy consumption of the UAV during the flight for collecting data within the WSN and then design the flight speed as well as the flight trajectory of each UAV for achieving different Pareto-optimal tradeoffs between the maximum single-UAV energy consumption among all UAVs and the task completion time. To achieve this goal, we propose a novel multi-objective ant colony optimization framework based on the adaptive coordinate method (MOACO-ACM). Firstly, the adaptive coordinate method is developed to decide the nodes visited by each of the K UAVs, respectively. Secondly, the ant colony algorithm is incorporated to optimize the visiting order of nodes for each UAV. Finally, we discuss the impact of UAVs’ speeds scheduling on the tradeoff between the task completion time and the maximum single-UAV energy consumption among all UAVs. Extensive simulations validate the effectiveness of our designed algorithm and further highlight the importance of UAVs’ flight speeds in achieving both energy-efficient and time-efficient data collection. Full article

(This article belongs to the Special Issue UAV-Assisted Internet of Things)

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21 pages, 505 KiB

Open AccessArticle

Drones Routing with Stochastic Demand

by Nan Yu, Bin Dong, Yuben Qu, Mingwei Zhang, Yanyan Wang, Haipeng Dai and Changhua Yao

Drones 2023, 7(6), 362; https://doi.org/10.3390/drones7060362 - 30 May 2023

Cited by 2 | Viewed by 1862

Abstract

Motivated by the increasing number of drones used for package delivery, we first study the problem of Multiple drOne collaborative Routing dEsign (MORE) in this article. That is, given a fixed number of drones and customers, determining the delivery trip for drones under capacity constraint with stochastic demand for customers such that the overall expected traveling cost is minimized. To address the MORE problem, we first prove that MORE falls into the realm of the classical vehicle routing problem with stochastic demand and then propose an effective algorithm for MORE. Next, we have a scheme of resplitting customers into different individual delivery trips while the stochastic demands are determined. Moreover, we consider a variety of MORE, MORE-TW, and design an effective algorithm to address it. We conduct simulation experiments for MORE to verify our theoretical findings. The results show that our algorithm outperforms other comparison algorithms by at least

79.60 %

. Full article

(This article belongs to the Special Issue UAV-Assisted Internet of Things)

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