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Article

DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications

1
Department of Computer Science, College of Computer & Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
2
CISTER Research Centre, ISEP, Polytechnic Institute of Porto, 4200-465 Porto, Portugal
3
Faculty of Computers and Artificial Intelligence, Benha University, Banha 13511, Egypt
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(18), 5240; https://doi.org/10.3390/s20185240
Received: 9 August 2020 / Revised: 8 September 2020 / Accepted: 9 September 2020 / Published: 14 September 2020
(This article belongs to the Special Issue Time-Sensitive Networks for Unmanned Aircraft Systems)
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. View Full-Text
Keywords: unmanned aerial vehicles (UAVs); deep learning; cloud computing; smart cities; Internet-of-Things; remote sensing unmanned aerial vehicles (UAVs); deep learning; cloud computing; smart cities; Internet-of-Things; remote sensing
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MDPI and ACS Style

Koubaa, A.; Ammar, A.; Alahdab, M.; Kanhouch, A.; Azar, A.T. DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications. Sensors 2020, 20, 5240. https://doi.org/10.3390/s20185240

AMA Style

Koubaa A, Ammar A, Alahdab M, Kanhouch A, Azar AT. DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications. Sensors. 2020; 20(18):5240. https://doi.org/10.3390/s20185240

Chicago/Turabian Style

Koubaa, Anis, Adel Ammar, Mahmoud Alahdab, Anas Kanhouch, and Ahmad Taher Azar. 2020. "DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications" Sensors 20, no. 18: 5240. https://doi.org/10.3390/s20185240

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