Abstract
Unmanned Aerial Vehicles (UAVs) have been very effective in collecting aerial images 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 techniquesconsume 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 paper, we first propose a system architecture of computation offloading for Internet-connected drones. Then, 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 deep learning 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. The main results demonstrate that the computation offloading approach allows us to provide much higher throughput (i.e., frames per second) as compared to the edge computing approach, despite the larger communication delays.
Highlights
Artificial Intelligence (AI), in particular, deep learning, and Unmanned Aerial Systems (UAS) are the two most prominent technologies in the last five years [1]
We present an experimental study of the DeepBrain system to demonstrate its feasibility and effectiveness
The main objective of this study is to assess the impact of the computation offloading of deep learning applications on the drone’s energy consumption, network bandwidth, and real-time guarantees
Summary
Artificial Intelligence (AI), in particular, deep learning, and Unmanned Aerial Systems (UAS) are the two most prominent technologies in the last five years [1]. While the main objective of drones is to collect visual data including aerial images and videos (in addition to other types of data), deep learning algorithms are nowadays the de facto standard. Several recent research works have leveraged deep learning algorithms based on convolutional neural networks to process aerial images collected from drones [5,6,7,8,9]. The works mentioned above demonstrate the recent trend in coupling UAS applications with deep learning algorithms. These works are mostly based on the offline analysis of aerial images collected from drones, and none of them have considered the processing of images or videos in real-time, meaning, as soon as they are collected from the drone
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