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. 


Dr. Angelo Trotta, Department of Computer Science and Engineering, University of Bologna, Italy

Dr. Gokhan Secinti, Computer Engineering Department - ─░stanbul Technical University (ITU), Turkey

Prof. Marco Di Felice, Department of Computer Science and Engineering, University of Bologna, Italy

Prof. Zhangyu Guan, Department of Electrical Engineering, University at Buffalo, NY, USA

Guest Editors