Title: Edge analytics on resource constrained devices

Authors: Sean Savitz; Charith Perera; Omer Rana

Addresses: School of Computer Science and Informatics, Cardiff University, UK ' School of Computer Science and Informatics, Cardiff University, UK ' School of Computer Science and Informatics, Cardiff University, UK

Abstract: Camera sensors can measure our environment at high precision, providing the basis for detecting more complex phenomena in comparison to other sensors, e.g., temperature or humidity. Using benchmarks, this work evaluates object classification on resource constrained devices, focusing on video feeds from IoT cameras. The models that have been used in this research include MobileNetV1, MobileNetV2 and faster R-CNN that can be combined with regression models for precise object localisation. We compare the models by using their accuracy for classifying objects and the demand they impose on the computational resources of a Raspberry Pi. We conclude that the faster R-CNN model that is configured with the InceptionV2 regression model has the highest accuracy. However, this is at the cost of additional computational resources. We found that the best model to use for object detection functionality on the Raspberry Pi is the MobileNetV2 model paired with the SSDLite regression model.

Keywords: internet of things; edge computing; edge analytics; resource constraint devices; camera sensing; deep learning; object detection.

DOI: 10.1504/IJCSE.2023.133674

International Journal of Computational Science and Engineering, 2023 Vol.26 No.5, pp.513 - 527

Received: 03 Nov 2020
Accepted: 02 Mar 2021
Published online: 29 Sep 2023 *

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