Recently, the role of mobile devices has changed from a calling or entertaining device to a tool for making life easier. However, this growing role is associated with extensive computing requirements to execute tasks such as object detection. Moreover, executing heavy tasks in a mobile with limited resources takes a long processing time and consumes much energy. This paper presents a computational offloading framework to improve the performance of object detection tasks. The framework uses a simple decision model to select between local processing and offloading based on the network context. A demo has been developed to evaluate the framework performance. The experimental work includes different image sizes and employs 3G and Wi-Fi networks. The results show a response time speedup that could reach five times for small images and 1.5 times for big images. The energy saving also ranges from 80% to 50%. Furthermore, offloading through a Wi-Fi network shows more performance stability than a 3G network. Finally, results demonstrate that offloading the object detection computation decreases the memory allocations to less than 1% in comparison with local processing.

A Computational Offloading Framework for Object Detection in Mobile Devices / Abdelaty, M.; Mokhtar, A.. - 639:(2018), pp. 97-107. (Intervento presentato al convegno 3rd International Conference on Advanced Intelligent Systems and Informatics, AISI 2017 tenutosi a egy nel 2017) [10.1007/978-3-319-64861-3_9].

A Computational Offloading Framework for Object Detection in Mobile Devices

AbdelAty M.;
2018-01-01

Abstract

Recently, the role of mobile devices has changed from a calling or entertaining device to a tool for making life easier. However, this growing role is associated with extensive computing requirements to execute tasks such as object detection. Moreover, executing heavy tasks in a mobile with limited resources takes a long processing time and consumes much energy. This paper presents a computational offloading framework to improve the performance of object detection tasks. The framework uses a simple decision model to select between local processing and offloading based on the network context. A demo has been developed to evaluate the framework performance. The experimental work includes different image sizes and employs 3G and Wi-Fi networks. The results show a response time speedup that could reach five times for small images and 1.5 times for big images. The energy saving also ranges from 80% to 50%. Furthermore, offloading through a Wi-Fi network shows more performance stability than a 3G network. Finally, results demonstrate that offloading the object detection computation decreases the memory allocations to less than 1% in comparison with local processing.
2018
Advances in Intelligent Systems and Computing
Springer
Springer Verlag
978-3-319-64860-6
978-3-319-64861-3
Abdelaty, M.; Mokhtar, A.
A Computational Offloading Framework for Object Detection in Mobile Devices / Abdelaty, M.; Mokhtar, A.. - 639:(2018), pp. 97-107. (Intervento presentato al convegno 3rd International Conference on Advanced Intelligent Systems and Informatics, AISI 2017 tenutosi a egy nel 2017) [10.1007/978-3-319-64861-3_9].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/333732
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