Recent Advances in UAVs for Wireless Networks

Dear Colleagues,

Unmanned Aerial Vehicles (UAVs) such as drones in 6G are envisaged to be capable of understanding the semantics of spatial and temporal environmental changes, adjusting their trajectories, intelligently collecting local data, orchestrating their platoons and immediately responding to unforeseen events while interactively collaborating with other drones, machines, and humans. Traditional information theory establishes a basic theoretical limit on the speed of reliable communications where every bit is treated identically, and the system is unaware of the meaning or application of the information. While this limit has successfully served content delivery networks for decades, many emerging applications with UAVs, drones, ranging from tactile internet and autonomous vehicles to disaster response and haptic applications, involve interactions between machines and humans where information content would play a role in the design and performance of the communication channel on the move.

This Special Issue aims to collect cutting-edge leapfrog submissions of distributed optimization, decentralized computing, and distributed learning (e.g., federated learning) over future drones' or UAVs networks from academia and industry, thereby advancing the development of fundamental theories for communication-efficient spatial learning and the application of blockchain and machine learning algorithms at scales relevant for semantic communication.

Blockchain, machine learning and data-driven space networking of UAVs/drones have recently been highly regarded as important facilitators for the 6G networks. Most existing space learning systems are centralized with data streamed from devices, drones, and satellites. Nevertheless, such a centralized approach may lead to privacy issues, breach applications' latency restrictions, or become ineffective due to high cost, bandwidth, or border constraints (such as energy, battery life and data rate). This summons a real momentum towards semantic communication-based distributed learning in the space, which is anticipated to be an exciting approach for solving impending issues by providing UAVs/drones that collaboratively train a context-aware learning model using data generated and shared in real-time.

Potential topics may include, but are not limited to, the following: 

• Drones IoT applications, Principles and Challenges for Semantic Communication: such as the autonomous shipping, platooning, autonomous vehicles, smart ocean, tactile internet, and disaster response systems.
• Novel distributed optimization, distributed ledger technology (DLT) theories and machine learning techniques for drones IoT, edge computing, platooning and age of information.
• Transport, network, and physical layer diversity protocols for semantic communication of the UAVs/drone's system
• Techniques reducing the number/frequency of drones' communications in distributed learning and streaming applications (e.g., applying transfer learning, zero/one-shot learning, etc.)
• Quantization, sparsification, and network coding methods for context-aware communication via distributed learning of UAVs/drones
• Novel methods for distributed learning and context-aware model training over drones with limited communication resources such as energy and bandwidth
• Impact of drones' network dynamic topology (e.g., time-varying graph) on efficient distributed learning and for semantic communication
• Distributed learning with practical semantic communication conditions, such as wireless interference, noisy/time-varying/fading channels, and multiple access
• Fundamental performance limits for distributed learning and semantic communication over UAVs/drones with limited communication resources
• Joint communication, computing, and sensing for DLT decentralized learning over future context aware swarm networks.
• New architectures for automation and orchestration of platooning, decentralized learning and semantic communication over UAVs/drones
• DLT-based Privacy and security issues of distributed learning for the drones IoT
• Building UAVs testbed and drones simulators for DLT for distributed learning and communication over the space
• Adaptive DLT design and dynamic semantic optimization of IoT space networks for improving the performance of remote federated/transfer learning
• Revolutionary drones IoT network protocol designs for remote collaborative federated/transfer learning and semantic communication
• Decentralized learning for intelligent drones' data processing, signal processing, space signal detection and estimation.

Dr. Shiva Raj Pokhrel
Prof. Dr. Hai L. Vu
Prof. Dr. Jinho Choi
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-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.