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Article

Impact of Input Data on Intelligence Partitioning Decisions for IoT Smart Camera Nodes

1
Department of Electronics Design, Mid Sweden University, Holmgatan 10, 851 70 Sundsvall, Sweden
2
System Design Department, IMMS Institut für Mikroelektronik und Mechatronik-Systeme Gemeinnützige GmbH (IMMS GmbH), Ehrenbergstraße 27, 98693 Ilmenau, Germany
3
Institute of Computer Technology, TU Wien (Vienna University of Technology), Gusshausstrasse 27-29/384, 1040 Vienna, Austria
*
Author to whom correspondence should be addressed.
Academic Editors: Abdellah Touhafi and Gianluca Cornetta
Electronics 2021, 10(16), 1898; https://doi.org/10.3390/electronics10161898
Received: 10 July 2021 / Revised: 30 July 2021 / Accepted: 3 August 2021 / Published: 7 August 2021
(This article belongs to the Special Issue Embedded IoT: System Design and Applications)
Image processing systems exploit image information for a purpose determined by the application at hand. The implementation of image processing systems in an Internet of Things (IoT) context is a challenge due to the amount of data in an image processing system, which affects the three main node constraints: memory, latency and energy. One method to address these challenges is the partitioning of tasks between the IoT node and a server. In this work, we present an in-depth analysis of how the input image size and its content within the conventional image processing systems affect the decision on where tasks should be implemented, with respect to node energy and latency. We focus on explaining how the characteristics of the image are transferred through the system until finally influencing partition decisions. Our results show that the image size affects significantly the efficiency of the node offloading configurations. This is mainly due to the dominant cost of communication over processing as the image size increases. Furthermore, we observed that image content has limited effects in the node offloading analysis. View Full-Text
Keywords: camera node optimization; intelligence partitioning; input changes; inter-task data amount; IoT; WVSN camera node optimization; intelligence partitioning; input changes; inter-task data amount; IoT; WVSN
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MDPI and ACS Style

Leal, I.S.; Shallari, I.; Krug, S.; Jantsch, A.; O’Nils, M. Impact of Input Data on Intelligence Partitioning Decisions for IoT Smart Camera Nodes. Electronics 2021, 10, 1898. https://doi.org/10.3390/electronics10161898

AMA Style

Leal IS, Shallari I, Krug S, Jantsch A, O’Nils M. Impact of Input Data on Intelligence Partitioning Decisions for IoT Smart Camera Nodes. Electronics. 2021; 10(16):1898. https://doi.org/10.3390/electronics10161898

Chicago/Turabian Style

Leal, Isaac Sánchez, Irida Shallari, Silvia Krug, Axel Jantsch, and Mattias O’Nils. 2021. "Impact of Input Data on Intelligence Partitioning Decisions for IoT Smart Camera Nodes" Electronics 10, no. 16: 1898. https://doi.org/10.3390/electronics10161898

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