Call for Papers

With the explosive development of the Internet of Things (IoT) and 5G communications, multi-access edge computing (MEC) has emerged as an effective solution to help mobile devices deal with computation-intensive and delay-sensitive applications. However, computing servers are usually embedded in fixed access points (APs) or base stations (BSs), and thus pose many disadvantages. Recently, a new paradigm of drone-enabled aerial computing has drawn extensive attention due to drones’ mobility, flexibility, and maneuverability. When the computing servers embedded in APs/BSs are overloaded or unavailable, drones can be quickly deployed to designated areas to meet temporary and/or unexpected demands. Moreover, benefiting from line-of-sight properties of air-ground links, aerial computing can effectively reduce task delay and transmission energy consumption as compared to terrestrial computing. In particular, aerial computing can play an important role in disaster areas, emergency relief and battlefields, which are lacking available terrestrial infrastructures.

Despite its many advantages, aerial computing also presents some challenges. For instance, due to the high mobility of drones, aerial computing is significantly different from terrestrial computing. Specifically, wireless links to/from a drone vary significantly over time, thus requiring elaborate design of the drone’s trajectory, task allocation and resource management. Meanwhile, in order to ensure low transmission energy consumption and task delay, computing and communication resources also need to be precisely allocated over time. The energy efficient trajectory plan of a drone is very important to extend its service time. In addition, due to the limited computing ability of a single drone, multiple drones are worth considering to simultaneously provide computing service, wherein the movement control, cooperation, and the resource allocation of multiple drones all require elaborate design.

This Special Issue (SI) brings together the latest research and innovation on aerial computing. All submissions should neither have been published previously nor be currently under consideration for publication elsewhere. This special issue covers research, development, application, and all other aspects of this field. Authors are invited to submit manuscripts on topics including, but not limited to, the following:

• New architectures, frameworks, and protocols for aerial computing
• Computation offloading, trajectory design and resource allocation for aerial computing
• Spectrum management and multiple access schemes for aerial computing
• Energy efficiency, energy harvesting, and green operation for aerial computing
• Joint optimization of computing, communication, caching, and control for aerial computing
• Cooperative design and resource allocation for multiple drones
• Machine learning and artificial intelligence for aerial computing
• Aerial computing for edge intelligence in large-scale sensor networks
• Security and privacy-preserving approaches for aerial computing
• Design and optimization for aerial-ground cooperative computing
• Aerial computing for industrial IoT applications
• Edge caching and computing for mobile AR/VR and tactile Internet

Submission Guidelines

Manuscripts should conform to the standard format as indicated in the Information for Authors section of the Paper Submission Guidelines. All manuscripts to be considered for publication must be submitted by the deadline through Manuscript Central. Select “October 2021/Aerial Computing: Drones for Multi-Access Edge Computing” from the drop-down menu of topics.

Important Dates

Manuscript Submission Deadline: 15 January 2021
Initial Decision Date: 1 April 2021
Revised Manuscript Due: 1 May 2021
Final Decision Date: 1 June 2021
Final Manuscript Due: 15 August 2021
Publication Date: October 2021

Guest Editors

Jianchao Zheng
National Innovation Institute of Defense Technology, China

Alagan Anpalagan
Ryerson University, Canada

Mohsen Guizani
Qatar University, Qatar

Yuan Wu
University of Macau, China

Ning Zhang
University of Windsor, Canada

Xianfu Chen
VTT Technical Research Centre of Finland, Finland

F. Richard Yu
Carleton University, Canada