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23 pages, 4654 KiB

Open AccessArticle

Energy Consumption Minimization for UAV-Assisted Network in Hotspot Area

by Jinxi Zhang, Saidiwaerdi Maimaiti, Weidong Gao and Kaisa Zhang

Drones 2025, 9(3), 178; https://doi.org/10.3390/drones9030178 - 28 Feb 2025

Viewed by 470

Abstract

Unmanned aerial vehicles (UAVs) play a crucial role in enhancing network coverage and capacity, especially in areas with high user density or limited infrastructure. This paper proposes an effective UAV-assisted offloading framework to minimize the energy consumption of both users and UAVs in [...] Read more.

Unmanned aerial vehicles (UAVs) play a crucial role in enhancing network coverage and capacity, especially in areas with high user density or limited infrastructure. This paper proposes an effective UAV-assisted offloading framework to minimize the energy consumption of both users and UAVs in an air-to-ground (A2G) network. First, UAVs are deployed by jointly considering the user distribution and guaranteeing the quality of service (QoS) of users. Further, user association, power control, and bandwidth allocation are jointly optimized, aiming to minimize the power consumption of users. Considering user mobility, the positions of UAVs are continuously refined using the double deep Q-network (DDQN) algorithm to reduce the weighted energy consumption of users and UAVs. Simulation results show that the proposed algorithm has better performance in reducing the total energy consumption compared with benchmark schemes. Full article

(This article belongs to the Special Issue Resilient Networking and Task Allocation for Drone Swarms)

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24 pages, 14086 KiB

Open AccessArticle

Seismic Data Acquisition Utilizing a Group of UAVs

by Artem Timoshenko, Grigoriy Yashin, Valerii Serpiva, Rustam Hamadov, Dmitry Fedotov, Mariia Kartashova and Pavel Golikov

Drones 2025, 9(3), 156; https://doi.org/10.3390/drones9030156 - 20 Feb 2025

Viewed by 2507

Abstract

Seismic exploration in hard-to-reach hazardous environments like deserts is a very expensive and time-consuming process that involves a lot of human resources and equipment. These difficulties can be overcome with the implementation of robots, providing flexible mission design, safe operation, and high precision [...] Read more.

Seismic exploration in hard-to-reach hazardous environments like deserts is a very expensive and time-consuming process that involves a lot of human resources and equipment. These difficulties can be overcome with the implementation of robots, providing flexible mission design, safe operation, and high precision data acquisition. This work presents an autonomous robotic system to assist seismic crews in advanced data acquisition for near-surface characterization, shallow cavity detection, and acquisition grid infill. The developed system consists of a swarm control station and a swarm of unmanned aerial vehicles (UAVs) equipped with seismic sensors. The architecture of the swarm control station, its individual blocks, features of UAV exploitation for seismic data acquisition tasks, hardware and software tool limitations are considered. Algorithms for planning UAV swarm flight paths, their comparison and trajectory examples are presented. Experiments utilizing 9 and 16 UAVs to record 171 and 144 target points, respectively, in harsh desert conditions are described. The results demonstrate the feasibility of the proposed system for seismic data acquisition. The developed robotic system offers flexibility in seismic survey design and planning, enabling efficient coverage of vast areas and facilitating comprehensive data acquisition, which enhances the accuracy and resolution of subsurface seismic imaging. Full article

(This article belongs to the Special Issue Resilient Networking and Task Allocation for Drone Swarms)

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22 pages, 1322 KiB

Open AccessArticle

A Consensus-Driven Distributed Moving Horizon Estimation Approach for Target Detection Within Unmanned Aerial Vehicle Formations in Rescue Operations

by Salvatore Rosario Bassolillo, Egidio D’Amato and Immacolata Notaro

Drones 2025, 9(2), 127; https://doi.org/10.3390/drones9020127 - 9 Feb 2025

Cited by 1 | Viewed by 733

Abstract

In the last decades, the increasing employment of unmanned aerial vehicles (UAVs) in civil applications has highlighted the potential of coordinated multi-aircraft missions. Such an approach offers advantages in terms of cost-effectiveness, operational flexibility, and mission success rates, particularly in complex scenarios such [...] Read more.

In the last decades, the increasing employment of unmanned aerial vehicles (UAVs) in civil applications has highlighted the potential of coordinated multi-aircraft missions. Such an approach offers advantages in terms of cost-effectiveness, operational flexibility, and mission success rates, particularly in complex scenarios such as search and rescue operations, environmental monitoring, and surveillance. However, achieving global situational awareness, although essential, represents a significant challenge, due to computational and communication constraints. This paper proposes a Distributed Moving Horizon Estimation (DMHE) technique that integrates consensus theory and Moving Horizon Estimation to optimize computational efficiency, minimize communication requirements, and enhance system robustness. The proposed DMHE framework is applied to a formation of UAVs performing target detection and tracking in challenging environments. It provides a fully distributed architecture that enables UAVs to estimate the position and velocity of other fleet members while simultaneously detecting static and dynamic targets. The effectiveness of the technique is proved by several numerical simulation, including an in-depth sensitivity analysis of key algorithm parameters, such as fleet network topology and consensus iterations and the evaluation of the robustness against node faults and information losses. Full article

(This article belongs to the Special Issue Resilient Networking and Task Allocation for Drone Swarms)

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18 pages, 1152 KiB

Open AccessArticle

Research on Data Link Channel Decoding Optimization Scheme for Drone Power Inspection Scenarios

by Haizhi Yu, Kaisa Zhang, Xu Zhao, Yubing Zhang, Bingfeng Cui, Shujuan Sun, Gengshuo Liu, Bo Yu, Chao Ma, Ying Liu and Weidong Gao

Drones 2023, 7(11), 662; https://doi.org/10.3390/drones7110662 - 6 Nov 2023

Cited by 5 | Viewed by 2705

Abstract

With the rapid development of smart grids, the deployment number of transmission lines has significantly increased, posing significant challenges to the detection and maintenance of power facilities. Unmanned aerial vehicles (UAVs) have become a common means of power inspection. In the context of [...] Read more.

With the rapid development of smart grids, the deployment number of transmission lines has significantly increased, posing significant challenges to the detection and maintenance of power facilities. Unmanned aerial vehicles (UAVs) have become a common means of power inspection. In the context of drone power inspection, drone clusters are used as relays for long-distance communication to expand the communication range and achieve data transmission between patrol drones and base stations. Most of the communication occurs in the air-to-air channel between UAVs, which requires high reliability of communication between drone relays. Therefore, the main focus of this paper is on decoding schemes for drone air-to-air channels. Given the limited computing resources and battery capacity of a drone, as well as the large amount of power data that needs to be transmitted between drone relays, this paper aims to design a high-accuracy and low-complexity decoder for LDPC long-code decoding. We propose a novel shared-parameter neural-network-normalized minimum sum decoding algorithm based on codebook quantization, applying deep learning to traditional LDPC decoding methods. In order to achieve high decoding performance while reducing complexity, this scheme utilizes codebook-based weight quantization and parameter sharing methods to improve the neural-network-normalized minimum sum (NNMS) decoding algorithm. Simulation experimental results show that the proposed method has a better BER performance and low computational complexity. Therefore, the LDPC decoding algorithm designed effectively meets the drone characteristics and the high channel decoding performance requirements. This ensures efficient and reliable data transmission on the data link between drone relays. Full article

(This article belongs to the Special Issue Resilient Networking and Task Allocation for Drone Swarms)

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20 pages, 13425 KiB

Open AccessArticle

Three-Dimensional Trajectory and Resource Allocation Optimization in Multi-Unmanned Aerial Vehicle Multicast System: A Multi-Agent Reinforcement Learning Method

by Dongyu Wang, Yue Liu, Hongda Yu and Yanzhao Hou

Drones 2023, 7(10), 641; https://doi.org/10.3390/drones7100641 - 19 Oct 2023

Cited by 2 | Viewed by 2506

Abstract

Unmanned aerial vehicles (UAVs) are able to act as movable aerial base stations to enhance wireless coverage for edge users with poor ground communication quality. However, in urban environments, the link between UAVs and ground users can be blocked by obstacles, especially when [...] Read more.

Unmanned aerial vehicles (UAVs) are able to act as movable aerial base stations to enhance wireless coverage for edge users with poor ground communication quality. However, in urban environments, the link between UAVs and ground users can be blocked by obstacles, especially when complicated terrestrial infrastructures increase the probability of non-line-of-sight (NLoS) links. In this paper, in order to improve the average throughput, we propose a multi-UAV multicast system, where a multi-agent reinforcement learning method is utilized to help UAVs determine the optimal altitude and trajectory. Intelligent reflective surfaces (IRSs) are also employed to reflect signals to solve the blocking problem. Furthermore, since the UAV’s onboard power is limited, this paper aims to minimize the UAVs’ energy consumption and maximize the transmission rate for edge users by jointly optimizing the UAVs’ 3D trajectory and transmit power. Firstly, we deduce the channel capacity of ground users in different multicast groups. Subsequently, the K-medoids algorithm is utilized for the multicast grouping problem of edge users based on transmission rate requirements. Then, we employ the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to learn an optimal solution and eliminate the non-stationarity of multi-agent training. Finally, the simulation results show that the proposed system can increase the average throughput by

14 %

approximately compared to the non-grouping system, and the MADDPG algorithm can achieve a

20 %

improvement in reducing the energy consumption of UAVs compared to traditional deep reinforcement learning (DRL) methods. Full article

(This article belongs to the Special Issue Resilient Networking and Task Allocation for Drone Swarms)

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24 pages, 5117 KiB

Open AccessArticle

A Hierarchical Blockchain-Based Trust Measurement Method for Drone Cluster Nodes

by Jinxin Zuo, Ruohan Cao, Jiahao Qi, Peng Gao, Ziping Wang, Jin Li, Long Zhang and Yueming Lu

Drones 2023, 7(10), 627; https://doi.org/10.3390/drones7100627 - 8 Oct 2023

Cited by 2 | Viewed by 2872

Abstract

In response to the challenge of low accuracy in node trust evaluation due to the high dynamics of entry and exit of drone cluster nodes, we propose a hierarchical blockchain-based trust measurement method for drone cluster nodes. This method overcomes the difficulties related [...] Read more.

In response to the challenge of low accuracy in node trust evaluation due to the high dynamics of entry and exit of drone cluster nodes, we propose a hierarchical blockchain-based trust measurement method for drone cluster nodes. This method overcomes the difficulties related to trust inheritance for dynamic nodes, trust re-evaluation of dynamic clusters, and integrated trust calculation for drone nodes. By utilizing a multi-layer unmanned cluster blockchain for trusted historical data storage and verification, we achieve scalability in measuring intermittent trust across time intervals, ultimately improving the accuracy of trust measurement for drone cluster nodes. We design a resource-constrained multi-layer unmanned cluster blockchain architecture, optimize the computing power balance within the cluster, and establish a collaborative blockchain mechanism. Additionally, we construct a dynamic evaluation method for trust in drone nodes based on task perception, integrating and calculating the comprehensive trust of drone nodes. This approach addresses trusted sharing and circulation of task data and resolves the non-inheritability of historical data. Experimental simulations conducted using NS3 and MATLAB demonstrate the superior performance of our trust value measurement method for unmanned aerial vehicle cluster nodes in terms of accurate malicious node detection, resilience to trust value fluctuations, and low resource delay retention. Full article

(This article belongs to the Special Issue Resilient Networking and Task Allocation for Drone Swarms)

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26 pages, 868 KiB

Open AccessArticle

Joint Resource Allocation and Drones Relay Selection for Large-Scale D2D Communication Underlaying Hybrid VLC/RF IoT Systems

by Xuewen Liu, Shuman Huang, Kaisa Zhang, Saidiwaerdi Maimaiti, Gang Chuai, Weidong Gao, Xiangyu Chen, Yijian Hou and Peiliang Zuo

Drones 2023, 7(9), 589; https://doi.org/10.3390/drones7090589 - 19 Sep 2023

Cited by 6 | Viewed by 2261

Abstract

Relay-aided Device-to-Device (D2D) communication combining visible light communication (VLC) with radio frequency (RF) is a promising paradigm in the internet of things (IoT). Static relay limits the flexibility and maintaining connectivity of relays in Hybrid VLC/RF IoT systems. By using a drone as [...] Read more.

Relay-aided Device-to-Device (D2D) communication combining visible light communication (VLC) with radio frequency (RF) is a promising paradigm in the internet of things (IoT). Static relay limits the flexibility and maintaining connectivity of relays in Hybrid VLC/RF IoT systems. By using a drone as a relay station, it is possible to avoid obstacles such as buildings and to communicate in a line-of-sight (LoS) environment, which naturally aligns with the requirement of VLC Systems. To further support the application of VLC in the IoT, subject to the challenges imposed by the constrained coverage, the lack of flexibility, poor reliability, and connectivity, drone relay-aided D2D communication appears on the horizon and can be cost-effectively deployed for the large-scale IoT. This paper proposes a joint resource allocation and drones relay selection scheme, aiming to maximize the D2D system sum rate while ensuring the quality of service (QoS) requirements for cellular users (CUs) and D2D users (DUs). First, we construct a two-phase coalitional game to tackle the resource allocation problem, which exploits the combination of VLC and RF, as well as incorporates a greedy strategy. After that, a distributed cooperative multi-agent reinforcement learning (MARL) algorithm, called WoLF policy hill-climbing (WoLF-PHC), is proposed to address the drones relay selection problem. Moreover, to further reduce the computational complexity, we propose a lightweight neighbor–agent-based WoLF-PHC algorithm, which only utilizes historical information of neighboring DUs. Finally, we provide an in-depth theoretical analysis of the proposed schemes in terms of complexity and signaling overhead. Simulation results illustrate that the proposed schemes can effectively improve the system performance in terms of the sum rate and outage probability with respect to other outstanding algorithms. Full article

(This article belongs to the Special Issue Resilient Networking and Task Allocation for Drone Swarms)

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Review

Jump to: Research

28 pages, 4077 KiB

Open AccessReview

A Comprehensive Survey on Short-Distance Localization of UAVs

by Luka Kramarić, Niko Jelušić, Tomislav Radišić and Mario Muštra

Drones 2025, 9(3), 188; https://doi.org/10.3390/drones9030188 - 4 Mar 2025

Viewed by 1574

Abstract

The localization of Unmanned Aerial Vehicles (UAVs) is a critical area of research, particularly in applications requiring high accuracy and reliability in Global Positioning System (GPS)-denied environments. This paper presents a comprehensive overview of short-distance localization methods for UAVs, exploring their strengths, limitations, [...] Read more.

The localization of Unmanned Aerial Vehicles (UAVs) is a critical area of research, particularly in applications requiring high accuracy and reliability in Global Positioning System (GPS)-denied environments. This paper presents a comprehensive overview of short-distance localization methods for UAVs, exploring their strengths, limitations, and practical applications. Among short-distance localization methods, ultra-wideband (UWB) technology has gained significant attention due to its ability to provide accurate positioning, resistance to multipath interference, and low power consumption. Different approaches to the usage of UWB sensors, such as time of arrival (ToA), time difference of arrival (TDoA), and double-sided two-way ranging (DS-TWR), alongside their integration with complementary sensors like Inertial Measurement Units (IMUs), cameras, and visual odometry systems, are explored. Furthermore, this paper provides an evaluation of the key factors affecting UWB-based localization performance, including anchor placement, synchronization, and the challenges of combined use with other localization technologies. By highlighting the current trends in UWB-related research, including its increasing use in swarm control, indoor navigation, and autonomous landing, potential researchers could benefit from this study by using it as a guide for choosing the appropriate localization techniques, emphasizing UWB technology’s potential as a foundational technology in advanced UAV applications. Full article

(This article belongs to the Special Issue Resilient Networking and Task Allocation for Drone Swarms)

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