Machine Learning in Internet of Unmanned Aerial Vehicles
A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Internet of Things (IoT) and Industrial IoT".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 32
Special Issue Editors
Interests: mobile computing; data privacy; internet of things; mobile edge computing; federated learning and its applications
Interests: drones; robots; swarm drones; swarm robotics; IoT; smart sensors; mechatronics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Unmanned aerial vehicles (UAVs) with wide coverage and high mobility provide new opportunities for supporting IoT data collection and transmissions, which has the advantages of high agility, high flexibility, and low cost. The rise in popularity of Deep Neural Networks (DNNs) has spawned a research effort to deploy various kinds of DNN models on vehicles initially. DNN models can both accomplish complicated vehicular tasks and enable the construction of intelligent vehicular networks. However, the application of DNNs to UAVs still faces some challenges due to hardware and energy limitations.
Several studies have emerged to reduce the computational costs of training DNNs and pursue a desired trade-off between accuracy and latency. Data quantization is another significant technique that enables on-device light DNNs. According to distinct quantization targets, quantization methods can be roughly divided into two major categories: inference quantization and training quantization. Inference quantization aims to quantize weight and or activation to accelerate the forward pass, while training quantization needs to further quantize the gradient to accelerate the whole training process. This Special Issue invites original and breakthrough research in the field of machine learning for the Internet of UAVs.
Dr. ** Zhao
Prof. Dr. Subhas Mukhopadhyay
Guest Editors
Manuscript Submission Information
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Keywords
- unmanned aerial vehicles
- machine learning
- neural networks
- deep learning
- artificial intelligence
- data quantization
- inference quantization
- training quantization
