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Drones
Special Issues
Resilient Networking and Task Allocation for Drone Swarms: 2nd Edition
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Resilient Networking and Task Allocation for Drone Swarms: 2nd Edition

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

Deadline for manuscript submissions: 25 December 2025 | Viewed by 144

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
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; IoT
Special Issues, Collections and Topics in MDPI journals
Dr. Qi Li

E-Mail Website
Guest Editor
School of Information Science and Technology, Beijing University of Technology, Beijing, China
Interests: UAV swarm network; multi-dimensional resource integration mechanism; mobility management

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue of Drones on “Resilient Networking and Task Allocation for Drone Swarms: 2nd Edition”.

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 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
Dr. Qi Li
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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Related Special Issue

Published Papers (1 paper)

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Research

27 pages, 405 KiB  
Article
Comparative Analysis of Centralized and Distributed Multi-UAV Task Allocation Algorithms: A Unified Evaluation Framework
by Yunze Song, Zhexuan Ma, Nuo Chen, Shenghao Zhou and Sutthiphong Srigrarom
Drones 2025, 9(8), 530; https://doi.org/10.3390/drones9080530 - 28 Jul 2025
Abstract
Unmanned aerial vehicles (UAVs), commonly known as drones, offer unprecedented flexibility for complex missions such as area surveillance, search and rescue, and cooperative inspection. This paper presents a unified evaluation framework for the comparison of centralized and distributed task allocation algorithms specifically tailored [...] Read more.
Unmanned aerial vehicles (UAVs), commonly known as drones, offer unprecedented flexibility for complex missions such as area surveillance, search and rescue, and cooperative inspection. This paper presents a unified evaluation framework for the comparison of centralized and distributed task allocation algorithms specifically tailored to multi-UAV operations. We first contextualize the classical assignment problem (AP) under UAV mission constraints, including the flight time, propulsion energy capacity, and communication range, and evaluate optimal one-to-one solvers including the Hungarian algorithm, the Bertsekas ϵ-auction algorithm, and a minimum cost maximum flow formulation. To reflect the dynamic, uncertain environments that UAV fleets encounter, we extend our analysis to distributed multi-UAV task allocation (MUTA) methods. In particular, we examine the consensus-based bundle algorithm (CBBA) and a distributed auction 2-opt refinement strategy, both of which iteratively negotiate task bundles across UAVs to accommodate real-time task arrivals and intermittent connectivity. Finally, we outline how reinforcement learning (RL) can be incorporated to learn adaptive policies that balance energy efficiency and mission success under varying wind conditions and obstacle fields. Through simulations incorporating UAV-specific cost models and communication topologies, we assess each algorithm’s mission completion time, total energy expenditure, communication overhead, and resilience to UAV failures. Our results highlight the trade-off between strict optimality, which is suitable for small fleets in static scenarios, and scalable, robust coordination, necessary for large, dynamic multi-UAV deployments. Full article
(This article belongs to the Special Issue Resilient Networking and Task Allocation for Drone Swarms: 2nd Edition)
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