Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.6
4.4
Journals
Drones
Special Issues
Mobile Fog and Edge Computing in Drone Swarms
share announcement

Mobile Fog and Edge Computing in Drone Swarms

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

Deadline for manuscript submissions: 29 April 2025 | Viewed by 18316

Special Issue Editors

Dr. Angelo Trotta

E-Mail Website
Guest Editor
Department of Computer Science and Engineering—DISI, Alma Mater Studiorum—University of Bologna, mura Anteo Zamboni, 7, 40126 Bologna, Italy
Interests: multirobot wireless networks; unmanned aerial systems; IoT; autonomous battery recharge
Dr. Gokhan Secinti

E-Mail Website
Guest Editor
Computer Engineering Department - İstanbul Technical University (ITU), 34467 İstanbul, Turkey
Interests: aerial networks; software-defined networks
Dr. Marco Di Felice

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University of Bologna, 40126 Bologna, Italy
Interests: unmanned aerial networks; Internet of Things; edge computing
Special Issues, Collections and Topics in MDPI journals
Prof. Zhangyu Guan

E-Mail Website
Guest Editor
Department of Electrical Engineering, University at Buffalo, The State University of New York, NY, USA
Interests: network design automation; new spectrum technologies; wireless network security

Special Issue Information

Dear Colleagues,

In the last few years, unmanned aerial vehicles (UAVs, also known as drones), have been rapidly developed due to device miniaturization and cost reduction. These Aerial cooperative systems can provide fast, cost-effective, and safe solutions for many civil and military applications. Drone swarms, made of highly mobile self-organizing vehicles, are characterized by the coordination and mobility of nodes that can accomplish distributed sensing and actuation tasks. However, these applications may require reliable communication as well as intensive computation leading to high energy consumption. Unfortunately, UAVs are in general battery-powered and are equipped with devices that are not capable of providing a fast and reliable reply to user applications. In this respect, mobile fog and edge computing applied to drone swarm (SwarmFEC) draws an adaptive and agile approach by enabling cross-domain control and management protocols to be deployed, thus revolutionizing the way swarm computation is executed.

This Special Issue aims to push computation and data services toward the edge of the network, closer to the origin of the demand in order to mitigate network load as well as improve service quality by reducing end-to-end latency and overall backhaul bandwidth demand. Potential research directions are fostered for this Special Issue, ranging from security and privacy issues to SwarmFEC deployment, from mobility management to resource optimization, and from joint coordination of aerial vehicles to wireless communications.

Possible topics include but are not limited to:

  • Communication models and protocols for SwarmFEC;
  • Dynamic fog/edge computing deployment in drone swarms;
  • Cooperative computing and scheduling strategy in SwarmFEC;
  • Costs of applications migration and workloads in SwarmFEC;
  • SwarmFEC support for the Internet of Things (IoT);
  • Security and privacy in services deployment for SwarmFEC;
  • Resource allocation and mobility models for energy management in SwarmFEC;
  • Software-defined networking support for SwarmFEC;
  • Optimization, learning, and AI to manage application deployment in SwarmFEC;
  • Spectrum coexistence and optimization for SwarmFEC communications;
  • SwarmFEC modeling, simulation, emulation, and experimentation;
  • 5G, beyond 5G, and 6G Integration in SwarmFEC.

Dr. Angelo Trotta
Dr. Gokhan Secinti
Prof. Marco Di Felice
Prof. Zhangyu Guan
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

  • UAV swarm
  • mobile fog and edge computing
  • energy-aware computing
  • wireless network

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

30 pages, 6408 KiB  
Article
Construction of a Deep Learning Model for Unmanned Aerial Vehicle-Assisted Safe Lightweight Industrial Quality Inspection in Complex Environments
by Zhongyuan Jing and Ruyan Wang
Drones 2024, 8(12), 707; https://doi.org/10.3390/drones8120707 - 27 Nov 2024
Viewed by 449
Abstract
With the development of mobile communication technology and the proliferation of the number of Internet of Things (IoT) terminal devices, a large amount of data and intelligent applications are emerging at the edge of the Internet, giving rise to the demand for edge [...] Read more.
With the development of mobile communication technology and the proliferation of the number of Internet of Things (IoT) terminal devices, a large amount of data and intelligent applications are emerging at the edge of the Internet, giving rise to the demand for edge intelligence. In this context, federated learning, as a new distributed machine learning method, becomes one of the key technologies to realize edge intelligence. Traditional edge intelligence networks usually rely on terrestrial communication base stations as parameter servers to manage communication and computation tasks among devices. However, this fixed infrastructure is difficult to adapt to the complex and ever-changing heterogeneous network environment. With its high degree of flexibility and mobility, the introduction of unmanned aerial vehicles (UAVs) into the federated learning framework can provide enhanced communication, computation, and caching services in edge intelligence networks, but the limited communication bandwidth and unreliable communication environment increase system uncertainty and may lead to a decrease in overall energy efficiency. To address the above problems, this paper designs a UAV-assisted federated learning with a privacy-preserving and efficient data sharing method, Communication-efficient and Privacy-protection for FL (CP-FL). A network-sparsifying pruning training method based on a channel importance mechanism is proposed to transform the pruning training process into a constrained optimization problem. A quantization-aware training method is proposed to automate the learning of quantization bitwidths to improve the adaptability between features and data representation accuracy. In addition, differential privacy is applied to the uplink data on this basis to further protect data privacy. After the model parameters are aggregated on the pilot UAV, the model is subjected to knowledge distillation to reduce the amount of downlink data without affecting the utility. Experiments on real-world datasets validate the effectiveness of the scheme. The experimental results show that compared with other federated learning frameworks, the CP-FL approach can effectively mitigate the communication overhead, as well as the computation overhead, and has the same outstanding advantage in terms of the balance between privacy and usability in differential privacy preservation. Full article
(This article belongs to the Special Issue Mobile Fog and Edge Computing in Drone Swarms)
►▼ Show Figures

Figure 1

17 pages, 1058 KiB  
Article
UAV-Mounted RIS-Aided Mobile Edge Computing System: A DDQN-Based Optimization Approach
by Min Wu, Shibing Zhu, Changqing Li, Jiao Zhu, Yudi Chen, Xiangyu Liu and Rui Liu
Drones 2024, 8(5), 184; https://doi.org/10.3390/drones8050184 - 7 May 2024
Cited by 3 | Viewed by 1555
Abstract
Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are increasingly employed in mobile edge computing (MEC) systems to flexibly modify the signal transmission environment. This is achieved through the active manipulation of the wireless channel facilitated by the mobile deployment of UAVs [...] Read more.
Unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) are increasingly employed in mobile edge computing (MEC) systems to flexibly modify the signal transmission environment. This is achieved through the active manipulation of the wireless channel facilitated by the mobile deployment of UAVs and the intelligent reflection of signals by RISs. However, these technologies are subject to inherent limitations such as the restricted range of UAVs and limited RIS coverage, which hinder their broader application. The integration of UAVs and RISs into UAV–RIS schemes presents a promising approach to surmounting these limitations by leveraging the strengths of both technologies. Motivated by the above observations, we contemplate a novel UAV–RIS-aided MEC system, wherein UAV–RIS plays a pivotal role in facilitating communication between terrestrial vehicle users and MEC servers. To address this challenging non-convex problem, we propose an energy-constrained approach to maximize the system’s energy efficiency based on a double-deep Q-network (DDQN), which is employed to realize joint control of the UAVs, passive beamforming, and resource allocation for MEC. Numerical results demonstrate that the proposed optimization scheme significantly enhances the system efficiency of the UAV–RIS-aided time division multiple access (TDMA) network. Full article
(This article belongs to the Special Issue Mobile Fog and Edge Computing in Drone Swarms)
►▼ Show Figures

Figure 1

32 pages, 9731 KiB  
Article
Hybrid LoRa-IEEE 802.11s Opportunistic Mesh Networking for Flexible UAV Swarming
by Luca Davoli, Emanuele Pagliari and Gianluigi Ferrari
Drones 2021, 5(2), 26; https://doi.org/10.3390/drones5020026 - 15 Apr 2021
Cited by 31 | Viewed by 13722
Abstract
Unmanned Aerial Vehicles (UAVs) and small drones are nowadays being widely used in heterogeneous use cases: aerial photography, precise agriculture, inspections, environmental data collection, search-and-rescue operations, surveillance applications, and more. When designing UAV swarm-based applications, a key “ingredient” to make them effective is [...] Read more.
Unmanned Aerial Vehicles (UAVs) and small drones are nowadays being widely used in heterogeneous use cases: aerial photography, precise agriculture, inspections, environmental data collection, search-and-rescue operations, surveillance applications, and more. When designing UAV swarm-based applications, a key “ingredient” to make them effective is the communication system (possible involving multiple protocols) shared by flying drones and terrestrial base stations. When compared to ground communication systems for swarms of terrestrial vehicles, one of the main advantages of UAV-based communications is the presence of direct Line-of-Sight (LOS) links between flying UAVs operating at an altitude of tens of meters, often ensuring direct visibility among themselves and even with some ground Base Transceiver Stations (BTSs). Therefore, the adoption of proper networking strategies for UAV swarms allows users to exchange data at distances (significantly) longer than in ground applications. In this paper, we propose a hybrid communication architecture for UAV swarms, leveraging heterogeneous radio mesh networking based on long-range communication protocols—such as LoRa and LoRaWAN—and IEEE 802.11s protocols. We then discuss its strengths, constraints, viable implementation, and relevant reference use cases. Full article
(This article belongs to the Special Issue Mobile Fog and Edge Computing in Drone Swarms)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop