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Drones
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Resilient Networking and Task Allocation for Drone Swarms
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Resilient Networking and Task Allocation for Drone Swarms

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

Deadline for manuscript submissions: 20 April 2025 | Viewed by 10557

Special Issue Editors

Prof. Dr. Jingjing Wang

E-Mail Website
Guest Editor
School of Cyber Science and Technology, Beihang University, Beijing 100191, China
Interests: underwater acoustic communication; routing protocols; medium access control; UAV networks
Special Issues, Collections and Topics in MDPI journals
Dr. Yibo Zhang

E-Mail Website
Guest Editor
School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100101, China
Interests: UAV; space-air-ground integrated; radio access network and IoT

Special Issue Information

Dear Colleagues,

With the rapid development of unmanned technology, the mode of the multi-drone cluster has begun to receive widespread attention. Compared to the traditional single drone, drone swarms can collaboratively complete complex tasks with higher efficiency, especially in harsh environments. Resilient cooperation between drones is essential to enable information sharing and joint missions, and to achieve autonomous drone swarms. However, traditional networking and task allocation schemes cannot address the unique characteristics of drone swarms, such as high dynamic topology and capability constraints. Therefore, researchers have to study new and specific solutions for possible issues in resilient networking and task allocation for drone swarms, where transmission delay and reliability, the performance and complexity of the cooperation strategy, and even the swarm flight control strategy are the key factors affecting the implementation of the tasks. This requires innovative ideas to propose new solutions, especially when the tasks tend to be complex and the scale of the drone swarm is large.

This Special Issue on “Resilient Networking and Task Allocation for Drone Swarms” aims to collect studies on the recent advances in collaboration strategy for multi-drones in a wide range of topics, including (but not limited to) the following:

  1. Cooperative communication and networking for drone swarms;
  2. Resilient access strategy for drone swarms;
  3. Resilient Edge computing for drone swarms;
  4. Cooperative formation for drone swarms;
  5. Complex task driven drone swarm cooperation;
  6. Resilient sensing, communication and computing integrated drone swarms;
  7. Resilient game and confrontation for drone swarms;
  8. Resilient resource allocation for drone swarms.

Prof. Dr. Jingjing Wang
Dr. Yibo Zhang
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. Drones is an international peer-reviewed open access monthly 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 2600 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

  • drone swarms
  • UAV ad hoc networks
  • swarm cooperative communications
  • swarm secure issues
  • swarm cooperative decision making

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Published Papers (4 papers)

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Research

18 pages, 1152 KiB  
Article
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 4 | Viewed by 2288
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  
Article
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 2074
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  
Article
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 2217
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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Graphical abstract

26 pages, 868 KiB  
Article
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 4 | Viewed by 1671
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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