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From the Sensor to the Cloud: Intelligence Partitioning for Smart Camera Applications
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design. (SMART)ORCID iD: 0000-0002-3774-4850
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 23, article id 5162Article in journal (Refereed) Published
Abstract [en]

The Internet of Things has grown quickly in the last few years, with a variety of sensing, processing and storage devices interconnected, resulting in high data traffic. While some sensors such as temperature, or humidity sensors produce a few bits of data periodically, imaging sensors output data in the range of megabytes every second. This raises a complexity for battery operated smart cameras, as they would be required to perform intensive image processing operations on large volumes of data, within energy consumption constraints. By using intelligence partitioning we analyse the effects of different partitioning scenarios for the processing tasks between the smart camera node, the fog computing layer and cloud computing, in the node energy consumption as well as the real time performance of the WVSN (Wireless Vision Sensor Node). The results obtained show that traditional design space exploration approaches are inefficient for WVSN, while intelligence partitioning enhances the energy consumption performance of the smart camera node and meets the timing constraints.

Place, publisher, year, edition, pages
Switzerland, 2019. Vol. 19, no 23, article id 5162
Keywords [en]
intelligence partitioning, smart camera, WVSN, IoT, in-sensor processing, fog, cloud, energy-efficiency
National Category
Embedded Systems
Identifiers
URN: urn:nbn:se:miun:diva-34612DOI: 10.3390/s19235162Scopus ID: 2-s2.0-85075687795OAI: oai:DiVA.org:miun-34612DiVA, id: diva2:1373814
Available from: 2019-11-28 Created: 2019-11-28 Last updated: 2020-01-10Bibliographically approved

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