Open Access Journal: Special Section on Resource Management in UAV-Assisted Systems

IEEE OJ-CS Special Section 2021

Computer Networks & Wireless Communication





Submission deadline: 15 October 2020
Publication: 2021
Future wireless networks (beyond 5G/6G) are envisioned to provide ubiquitous computing and communication to distributed wireless networks, including various Internet of Things (IoT) verticals, i.e., industrial automation, e-healthcare, connected vehicles, and smart grids. Due to the ever-increasing number of smart devices/objects, including Machine-Type Communications (MTC) devices, it is anticipated that IoT-related applications will grow rapidly in the coming years. This explosive growth in smart devices and IoT applications has raised a critical challenge of providing resource-efficient and reliable connections to the massive number of heterogeneous devices in dynamic wireless environments while satisfying diverse Quality of Service (QoS) requirements. Although there are several recent advances in terrestrial communication technologies towards addressing these issues, they still suffer from the challenges of providing wide-area coverage, high data rate, ultra-high reliability, ultra-low latency, and massive connectivity solutions to heterogeneous and distributed networks. Also, existing terrestrial infrastructures are economically infeasible in emerging scenarios, including disaster management and network service recovery. To address these, intelligent network infrastructure supported by Unmanned Aerial Vehicles (UAVs) is considered an important enabler for future (beyond 5G/6G) networks due to several unique features of UAVs, including ubiquitous coverage, strong line-of-sight, flexible deployment, and controlled mobility. To this end, integration of terrestrial and aerial networks seems promising to address the aforementioned issues of massive connectivity, ubiquitous coverage, diverse QoS requirements, and dynamic traffic demands in future wireless networks.
However, the resource management problem in UAV-assisted networks is significantly different than in terrestrial networks because UAVs are flying and can be anywhere in a swarm or on their own. It becomes challenging to optimize the involved resources (communications, computing, and caching) with the ground entities, and also with the core networks. The main application areas of UAV-assisted networks are UAV-assisted backhaul/fronthaul or backbone networks, UAV-assisted access networks such as flying base stations, and UAV-assisted airborne sensing networks such as IoT networks. In these networks, cross-layer interference mitigation, efficient resource (frequency, power, spatial) allocation, reliability, scalability and latency issues, resource-efficient design of satellite-to-UAV and UAV-to-ground networks, joint optimization of involved resources, and network planning for on-demand deployment are some of the key challenges to be addressed. Furthermore, the conventional protocols/techniques may not be suitable to address the issues of ultra-low latency, ultra-high reliability, high data rate, and low energy consumption demanded by emerging eMBB, URLLC and mMTC services, which demands more intelligent, dynamic, resource- efficient, and reliable techniques for UAV-assisted wireless networks.
This special section brings together the latest research and innovations from academia and industries in the domain of resource management in aerial and terrestrial networks. Submissions should not have been published previously or be currently under consideration for publication elsewhere. This special section covers research, development, application, standardization, and all other aspects of this field. Authors are invited to submit manuscripts on topics including, but not limited to, the following:
Radio resource management architectures in next-generation UAV-assisted networks
Design and modeling of resource management models for UAV-assisted communication systems
UAV-enabled computing offloading for IoT applications
Resource management for UAV-integrated terrestrial cellular networks
AI/ML/DL for intelligent resource management in UAV-assisted networks
Virtualization for resource management in UAV-assisted networks
Software-defined networking for resource management in UAV-assisted networks
Network slicing for resource management in UAV-assisted networks
Scalability, reliability, and latency issues in UAV-assisted wireless networks
Energy efficiency and spectral efficiency maximization in UAV-assisted networks
Resource management for UAV-assisted backhaul/fronthaul or backbone network
Resource management for UAV-assisted access networks such as flying B
Resource management for UAV-assisted airborne sensing networks, i.e., IoT networks
Optimization of caching, computing, and networking resources in UAV-assisted mobile edge-computing networks
Spectrum allocation and link scheduling for satellite-UAV-ground communications
Cooperative and cognitive communication techniques for UAV networks
Joint design of trajectory and communications for multi-UAV networks
Performance modeling, evaluation, and analysis for resource management in UAV-assisted networks
Protocols and standardization for UAV-assisted networking, computing, and communication resource provisioning
Experimental study/results, simulators, and test beds of aerial-terrestrial networks