Abstract
To provide Artificial Intelligence (AI) services such as object detection, Internet of Things (IoT) sensor devices should be able to send a large amount of data such as images and videos. However, this inevitably causes IoT networks to be severely overloaded. In this paper, therefore, we propose a novel oneM2M-compliant Artificial Intelligence of Things (AIoT) system for reducing overall data traffic and offering object detection. It consists of some IoT sensor devices with random sampling functions controlled by a compressed sensing (CS) rate, an IoT edge gateway with CS recovery and domain transform functions related to compressed sensing, and a YOLOv5 deep learning function for object detection, and an IoT server. By analyzing the effects of compressed sensing on data traffic reduction in terms of data rate per IoT sensor device, we showed that the proposed AIoT system can reduce the overall data traffic by changing compressed sensing rates of random sampling functions in IoT sensor devices. In addition, we analyzed the effects of the compressed sensing on YOLOv5 object detection in terms of performance metrics such as recall, precision, mAP50, and mAP, and found that recall slightly decreases but precision remains almost constant even though the compressed sensing rate decreases and that mAP50 and mAP are gradually degraded according to the decreased compressed sensing rate. Consequently, if proper compressed sensing rates are chosen, the proposed AIoT system will reduce the overall data traffic without significant performance degradation of YOLOv5.
Highlights
Internet of Things (IoT) is a dynamic global network infrastructure with self-configuration capabilities based on standards and interoperable communication protocols, in which the physical and virtual things have their own identities and properties and are integrated into IoT networks through various wired and wireless interfaces [1]
We describe experimental results to evaluate the performance of the proposed Artificial Intelligence of Things (AIoT) system model in terms of the effects of the compressed sensing on object detection
Instead of real-time images from a C270 high definition (HD) webcam attached on the Raspberry Pi 4, 128 images of the COCO dataset were fed into the IoT sensor device for objective performance evaluation
Summary
Internet of Things (IoT) is a dynamic global network infrastructure with self-configuration capabilities based on standards and interoperable communication protocols, in which the physical and virtual things have their own identities and properties and are integrated into IoT networks through various wired and wireless interfaces [1]. Jung et al [3] proposed an oneM2M-compliant AIoT monitoring system where an AIoT edge device extracted video frame images from a CCTV camera in a pig house, detected multiple pigs in the images by a faster region-based convolutional neural network model, and tracked them by an object center-point tracking algorithm. They did not consider the data traffic problem. In this paper, we propose an AIoT system model using compressed sensing to solve the data traffic problem that occurs when transmitting a large amount of data through the IoT network for an AI service such as YOLOv5 object detection.
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