DeepBrain: Experimental Evaluation of Cloud-Based Computation Offloading and Edge Computing in the Internet-of-Drones for Deep Learning Applications
| Autor: | Mahmoud Alahdab, Anas Kanhouch, Anis Koubaa, Adel Ammar, Ahmad Taher Azar |
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| Přispěvatelé: | Repositório Científico do Instituto Politécnico do Porto |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Real-time computing Throughput Cloud computing Context (language use) 02 engineering and technology lcsh:Chemical technology Biochemistry Article Analytical Chemistry 0203 mechanical engineering 0202 electrical engineering electronic engineering information engineering Computation offloading lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Intelligent transportation system Edge computing Unmanned aerial vehicles (UAVs) business.industry Deep learning 020206 networking & telecommunications 020302 automobile design & engineering Remote sensing Atomic and Molecular Physics and Optics Internet-of-Things Enhanced Data Rates for GSM Evolution Artificial intelligence business Smart cities |
| Zdroj: | Repositório Científico de Acesso Aberto de Portugal Repositório Científico de Acesso Aberto de Portugal (RCAAP) instacron:RCAAP Sensors, Vol 20, Iss 5240, p 5240 (2020) Sensors (Basel, Switzerland) Sensors Volume 20 Issue 18 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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