Unmanned Aerial Systems (UAVs) have been very effective in collecting aerial image data for various Internet-of-Things (IoT)/smart cities applications such as search and rescue, surveillance, vehicle detection, counting, intelligent transportation systems, to name a few. However, the real-time processing of collected data on edge in the context of the Internet-of-Drones remains an open challenge because UAVs have limited energy capabilities, while computer vision techniques consume excessive energy and require abundant resources. This fact is even more critical when deep learning algorithms, such as convolutional neural networks (CNNs), are used for classification and detection.
In this talk, we present the challenges to deploy AI-based applications on Unammedn Aerial Systems. We discuss the scenarios of cloud-deployment versus edge-deployment in addition to the hybrid deployment approach. We conduct a comprehensive experimental study to evaluate the performance in terms of energy, bandwidth, and delay of the cloud computation offloading approach versus the edge computing approach of object detection and face recognition applications in the context of UAVs. In particular, we investigate the tradeoff between the communication cost and the computation of the two candidate approaches experimentally.
Anis Koubaa is the Director of the Research and Initiatives Center and the leader of the Robotics and Internet-of-Things Lab at Prince Sultan University. He is a Full Professor in Computer Science and has been working on several R\&D projects on data science and unmanned system, deep learning, robotics, and Internet-of-Things. He is a Senior Fellow of the Higher Education Academy of the UK. He presented several training programs on drones, data science, Python programming, deep learning, and several other technologies. He is known for his course series and books on Robot Operating System (ROS).