P. McEnroe, S. Wang and M. Liyanage, "A Survey on the Convergence of Edge Computing and AI for UAVs: Opportunities and Challenges," in IEEE Internet of Things Journal, vol. 9, no. 17, pp. 15435-15459, 1 Sept.1, 2022, http://dx.doi.org/10.1109/jiot.2022.3176400
A survey on the convergence of edge computing and AI for UAVs : opportunities and challenges
|Author:||McEnroe, Patrick1; Wang, Shen1; Liyanage, Madhusanka1,2|
1School of Computer Science, University College Dublin, Dublin 4, D04 V1W8, Ireland
2Center for Wireless Communications, University of Oulu, 90570 Oulu, Finland
|Online Access:||PDF Full Text (PDF, 3.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021101150651
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2022-10-03
The latest 5G mobile networks have enabled many exciting Internet of Things (IoT) applications that employ unmanned aerial vehicles (UAVs/drones). The success of most UAV-based IoT applications is heavily dependent on artificial intelligence (AI) technologies, for instance, computer vision and path planning. These AI methods must process data and provide decisions while ensuring low latency and low energy consumption. However, the existing cloud-based AI paradigm finds it difficult to meet these strict UAV requirements. Edge AI, which runs AI on-device or on edge servers close to users, can be suitable for improving UAV-based IoT services. This article provides a comprehensive analysis of the impact of edge AI on key UAV technical aspects (i.e., autonomous navigation, formation control, power management, security and privacy, computer vision, and communication) and applications (i.e., delivery systems, civil infrastructure inspection, precision agriculture, search and rescue (SAR) operations, acting as aerial wireless base stations (BSs), and drone light shows). As guidance for researchers and practitioners, this article also explores UAV-based edge AI implementation challenges, lessons learned, and future research directions.
IEEE internet of things journal
|Pages:||15435 - 15459|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the Science Foundation Ireland (SFI) Center for Research Training in Machine Learning under Grant 18/CRT/6183; in part by SFI CONNECT Center Phase 2 under Grant 13/RC/2077_P2; and in part the 6Genesis Flagship Project under Grant 318927.
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
© The Author(s) 2022. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0.