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Drones
Special Issues
Low-Latency Communication for Real-Time UAV Applications
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Low-Latency Communication for Real-Time UAV Applications

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Communications".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 3730

Editors

Prof. Dr. Angelos Stavrou

E-Mail Website
Guest Editor
Bradley Department of Electrical & Computer Engineering, Virginia Tech, Arlington, VA 24061-0002, USA
Interests: computer security; IoT security; software security
Dr. Tolga O. Atalay

E-Mail Website
Guest Editor
A2 Labs LLC, 800 N. Glebe Road Suite 720A, Arlington, VA 22203, USA
Interests: 5G; cloud computing; system security
Dr. Alireza Famili

E-Mail Website
Guest Editor
WayWave Inc., 5352 Brandon Ridge Way, Fairfax, VA 22032, USA
Interests: wireless communications; indoor localization; 5G/6G; UAVs; AR/VR/MR

Special Issue Information

Dear Colleagues,

Drones are increasingly deployed in critical applications where real-time data and control are paramount (e.g., emergency response, industrial inspection, urban air mobility, autonomous delivery, aerial mapping for smart cities, and immersive XR streaming). These use cases demand communication networks with ultra-low latency and high reliability, as even minimal delays can impact performance or safety. Emerging wireless technologies (e.g., 5G, 6G, and beyond) offer ultra-reliable low-latency communication (URLLC) capabilities, making end-to-end latencies of only a few milliseconds possible. Ensuring near-instantaneous, robust links between unmanned aerial vehicles (UAVs), ground stations, and edge/cloud systems is crucial for unlocking autonomous drone operations, swarm coordination, and other time-sensitive UAV applications.

This Special Issue aims to gather cutting-edge research on communication architectures, protocols, and technologies that enable low-latency, real-time UAV operations. The focus is on bridging the gap between emerging network innovations and the stringent latency requirements of drone missions, in line with Drones’ scope on UAV communications. We welcome contributions showing how novel wireless network designs, edge computing paradigms, or cross-layer optimizations can support latency-sensitive UAV use cases. By highlighting fast and reliable connectivity solutions, this issue underscores the vital role of communications in expanding UAV capabilities.

Topics of interest include, but are not limited to:

  • Next-generation (5G/6G) networks for UAVs: ultra-reliable low-latency links, network slicing, and UAV-oriented URLLC techniques.
  • Edge and fog computing for drones: mobile edge computing, fog nodes, and AI-driven network optimization to minimize latency.
  • Real-time UAV network protocols and architectures: novel MAC and routing protocols, mesh/ad hoc networks, and UAV-to-X communications for drone swarms.
  • Coordinated multi-UAV systems: swarm communications, cooperative sensing, and collision avoidance requiring instantaneous data exchange.
  • Latency-critical UAV applications and testbeds: teleoperation, live video streaming, autonomous delivery, search-and-rescue, etc., demonstrating minimal-delay performance.

Research articles and review papers with strong theoretical analysis, real-world implementations, proof-of-concept systems, or rigorous and thorough simulations are especially encouraged.

Prof. Dr. Angelos Stavrou
Dr. Tolga O. Atalay
Dr. Alireza Famili
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • low-latency communication
  • real-time UAV applications
  • 5G/6G networks
  • ultra-reliable low-latency communication (URLLC)
  • edge computing for drones
  • UAV swarm communication
  • next-generation wireless systems
  • mobile ad hoc networks (MANETs) for UAVs
  • autonomous drone networks
  • latency-aware protocols

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Published Papers (3 papers)

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Research

22 pages, 4328 KB  
Article
UAV-Supported Vehicle Platooning in NOMA-Enhanced VANETs: Latency Optimization and Performance Analysis
by Fanghui Huang, Junbin Lou, Dawei Wang, Baolei Wang and Yixin He
Drones 2026, 10(6), 431; https://doi.org/10.3390/drones10060431 - 2 Jun 2026
Viewed by 365
Abstract
In vehicular ad hoc networks (VANETs), using vehicle platooning can improve traffic efficiency, reduce driving energy consumption, and ease traffic congestion. However, since land-based stations have limited coverage (about 7% of the Earth’s surface), ensuring low-latency communication is challenging. To address this issue, [...] Read more.
In vehicular ad hoc networks (VANETs), using vehicle platooning can improve traffic efficiency, reduce driving energy consumption, and ease traffic congestion. However, since land-based stations have limited coverage (about 7% of the Earth’s surface), ensuring low-latency communication is challenging. To address this issue, the introduction of solar-powered unmanned aerial vehicles (UAVs) as aerial base stations provides flexible and extensive communication support for vehicle platooning. Additionally, intelligent connected vehicles (ICVs) adopt non-orthogonal multiple access (NOMA) techniques for uplink transmission to further enhance transmission performance. Motivated by the above, this paper investigates the latency optimization problem of UAV-supported vehicle platooning by jointly considering multi-dimensional resource allocation and imperfect channel state information (CSI) affected by mobility. To solve this problem, we propose an iterative optimization approach with polynomial complexity, where the transmitted power and channel allocation are tackled in turn. Then, an analytical framework is developed to analyze the probability that NOMA is superior to OMA, guiding parameter settings for UAV-supported vehicle platooning. Finally, the simulation results show that the proposed latency optimization scheme can achieve lower total and average latencies on the uplink compared to state-of-the-art works and the benchmark scheme using OMA. Moreover, this paper elucidates the convergence, performance gap, and computational complexity associated with the proposed iterative optimization approach. Furthermore, the probability of NOMA outperforming OMA is quantified through Monte Carlo experiments, which validates the correctness of the developed analytical framework. Full article
(This article belongs to the Special Issue Low-Latency Communication for Real-Time UAV Applications)
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23 pages, 4407 KB  
Article
Measurement-Informed Latency Limits for Real-Time UAV Swarm Coordination
by Rodolfo Vera-Amaro, Alberto Luviano-Juárez, Mario E. Rivero-Ángeles, Diego Márquez-González and Danna P. Suárez-Ángeles
Drones 2026, 10(4), 310; https://doi.org/10.3390/drones10040310 - 21 Apr 2026
Viewed by 1254
Abstract
Communication latency is one of the main factors limiting the practical scalability of unmanned aerial vehicle (UAV) swarms operating with distributed formation control. In real-time UAV missions, such as coordinated swarm navigation, autonomous inspection, and aerial monitoring, delayed information exchange directly affects formation [...] Read more.
Communication latency is one of the main factors limiting the practical scalability of unmanned aerial vehicle (UAV) swarms operating with distributed formation control. In real-time UAV missions, such as coordinated swarm navigation, autonomous inspection, and aerial monitoring, delayed information exchange directly affects formation stability and operational safety. In practical aerial networks, inter-UAV communication latency is influenced by stochastic effects including jitter, burst delays, and multi-hop propagation, which are rarely captured by the simplified deterministic delay assumptions commonly adopted in analytical formation-control studies. This paper introduces a measurement-informed stochastic delay model and a communication–control delay-feasibility framework that jointly account for per-link latency behavior, multi-hop delay accumulation, and controller-level delay tolerance. The proposed framework is evaluated using an attractive–repulsive distance-based potential field (ARD–PF) formation controller, for which the maximum admissible end-to-end delay is quantified as a function of swarm size and inter-UAV separation. The delay model is calibrated and validated using more than 15,000 in-flight communication delay samples collected from a multi-UAV LoRa platform operating under realistic flight conditions. The results show that different mechanisms limit swarm operation under different operating scenarios. In some configurations, stochastic communication latency becomes the dominant constraint, whereas in others, formation geometry or network load determines the feasible operating region. Based on these elements, the proposed framework characterizes delay-feasible operating regions and predicts the maximum feasible swarm size under distributed formation control and realistic multi-hop communication latency. Full article
(This article belongs to the Special Issue Low-Latency Communication for Real-Time UAV Applications)
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25 pages, 4969 KB  
Article
Energy–Latency–Accuracy Trade-Off in UAV-Assisted VECNs: A Robust Optimization Approach Under Channel Uncertainty
by Tiannuo Liu, Menghan Wu, Hanjun Yu, Yixin He, Dawei Wang, Li Li and Hongbo Zhao
Drones 2026, 10(2), 86; https://doi.org/10.3390/drones10020086 - 26 Jan 2026
Viewed by 963
Abstract
Federated learning (FL)-based vehicular edge computing networks (VECNs) are emerging as a key enabler of intelligent transportation systems, as their privacy-preserving and distributed architecture can safeguard vehicle data while reducing latency and energy consumption. However, conventional roadside units face processing bottlenecks in dense [...] Read more.
Federated learning (FL)-based vehicular edge computing networks (VECNs) are emerging as a key enabler of intelligent transportation systems, as their privacy-preserving and distributed architecture can safeguard vehicle data while reducing latency and energy consumption. However, conventional roadside units face processing bottlenecks in dense traffic and at the network edge, motivating the adoption of unmanned aerial vehicle (UAV)-assisted VECNs. To address this challenge, this paper proposes a UAV-assisted VECN framework with FL, aiming to improve model accuracy while minimizing latency and energy consumption during computation and transmission. Specifically, a reputation-based client selection mechanism is introduced to enhance the accuracy and reliability of federated aggregation. Furthermore, to address the channel dynamics induced by high vehicle mobility, we design a robust reinforcement learning-based resource allocation scheme. In particular, an asynchronous parallel deep deterministic policy gradient (APDDPG) algorithm is developed to adaptively allocate computation and communication resources in response to real-time channel states and task demands. To ensure consistency with real vehicular communication environments, field experiments were conducted and the obtained measurements were used as simulation parameters to analyze the proposed algorithm. Compared with state-of-the-art algorithms, the developed APDDPG algorithm achieves 20% faster convergence, 9% lower energy consumption, a FL accuracy of 95.8%, and the most robust standard deviation under varying channel conditions. Full article
(This article belongs to the Special Issue Low-Latency Communication for Real-Time UAV Applications)
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