Guest Editorial Special Section on Tiny Machine Learning in Internet of Unmanned Aerial Vehicles
Document Type
Article
Publication Date
6-15-2024
Abstract
With the rapid development of ubiquitous networks and smart devices, artificial intelligence-based unmanned aerial vehicles (UAVs) are drawing more and more attention. The rise in popularity of deep neural networks (DNNs) has spawned a research effort to deploy various kinds of DNN models on vehicles. They have been used to accomplish complicated vehicular tasks and enable the construction of intelligent vehicular networks. Despite the promising prospects, how to train and run them on resource-limited and hardware-constrained UAVs faces huge challenges. Furthermore, the tradeoff between accuracy and latency needs to be considered while reducing the computational cost of DNN training.
Identifier
85196029109 (Scopus)
Publication Title
IEEE Internet of Things Journal
External Full Text Location
https://doi.org/10.1109/JIOT.2024.3396928
e-ISSN
23274662
First Page
20879
Last Page
20884
Issue
12
Volume
11
Recommended Citation
Ning, Zhaolong; Jamalipour, Abbas; Zhou, Meng Chu; and Jedari, Behrouz, "Guest Editorial Special Section on Tiny Machine Learning in Internet of Unmanned Aerial Vehicles" (2024). Faculty Publications. 342.
https://digitalcommons.njit.edu/fac_pubs/342