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Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller

The Internet of Things (IoT) era is mainly dependent on the word “Smart”, such as smart cities, smart homes, and smart cars. This aspect can be achieved through the merging of machine learning algorithms with IoT computing models. By adding the Artificial Intelligence (AI) algorithms to IoT, the res...

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Autores principales: Hussein, Mahmoud, Mohammed, Yehia Sayed, Galal, Ahmed I., Abd-Elrahman, Emad, Zorkany, Mohamed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316597/
https://www.ncbi.nlm.nih.gov/pubmed/35890787
http://dx.doi.org/10.3390/s22145106
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author Hussein, Mahmoud
Mohammed, Yehia Sayed
Galal, Ahmed I.
Abd-Elrahman, Emad
Zorkany, Mohamed
author_facet Hussein, Mahmoud
Mohammed, Yehia Sayed
Galal, Ahmed I.
Abd-Elrahman, Emad
Zorkany, Mohamed
author_sort Hussein, Mahmoud
collection PubMed
description The Internet of Things (IoT) era is mainly dependent on the word “Smart”, such as smart cities, smart homes, and smart cars. This aspect can be achieved through the merging of machine learning algorithms with IoT computing models. By adding the Artificial Intelligence (AI) algorithms to IoT, the result is the Cognitive IoT (CIoT). In the automotive industry, many researchers worked on self-diagnosis systems using deep learning, but most of them performed this process on the cloud due to the hardware limitations of the end-devices, and the devices obtain the decision via the cloud servers. Others worked with simple traditional algorithms of machine learning to solve these limitations of the processing capabilities of the end-devices. In this paper, a self-diagnosis smart device is introduced with fast responses and little overhead using the Multi-Layer Perceptron Neural Network (MLP-NN) as a deep learning technique. The MLP-NN learning stage is performed using a Tensorflow framework to generate an MLP model’s parameters. Then, the MLP-NN model is implemented using these model’s parameters on a low cost end-device such as ARM Cortex-M Series architecture. After implementing the MLP-NN model, the IoT implementation is built to publish the decision results. With the proposed implemented method for the smart device, the output decision based on sensors values can be taken by the IoT node itself without returning to the cloud. For comparison, another solution is proposed for the cloud-based architecture, where the MLP-NN model is implemented on Cloud. The results clarify a successful implemented MLP-NN model for little capabilities end-devices, where the smart device solution has a lower traffic and latency than the cloud-based solution.
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spelling pubmed-93165972022-07-27 Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller Hussein, Mahmoud Mohammed, Yehia Sayed Galal, Ahmed I. Abd-Elrahman, Emad Zorkany, Mohamed Sensors (Basel) Article The Internet of Things (IoT) era is mainly dependent on the word “Smart”, such as smart cities, smart homes, and smart cars. This aspect can be achieved through the merging of machine learning algorithms with IoT computing models. By adding the Artificial Intelligence (AI) algorithms to IoT, the result is the Cognitive IoT (CIoT). In the automotive industry, many researchers worked on self-diagnosis systems using deep learning, but most of them performed this process on the cloud due to the hardware limitations of the end-devices, and the devices obtain the decision via the cloud servers. Others worked with simple traditional algorithms of machine learning to solve these limitations of the processing capabilities of the end-devices. In this paper, a self-diagnosis smart device is introduced with fast responses and little overhead using the Multi-Layer Perceptron Neural Network (MLP-NN) as a deep learning technique. The MLP-NN learning stage is performed using a Tensorflow framework to generate an MLP model’s parameters. Then, the MLP-NN model is implemented using these model’s parameters on a low cost end-device such as ARM Cortex-M Series architecture. After implementing the MLP-NN model, the IoT implementation is built to publish the decision results. With the proposed implemented method for the smart device, the output decision based on sensors values can be taken by the IoT node itself without returning to the cloud. For comparison, another solution is proposed for the cloud-based architecture, where the MLP-NN model is implemented on Cloud. The results clarify a successful implemented MLP-NN model for little capabilities end-devices, where the smart device solution has a lower traffic and latency than the cloud-based solution. MDPI 2022-07-07 /pmc/articles/PMC9316597/ /pubmed/35890787 http://dx.doi.org/10.3390/s22145106 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hussein, Mahmoud
Mohammed, Yehia Sayed
Galal, Ahmed I.
Abd-Elrahman, Emad
Zorkany, Mohamed
Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller
title Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller
title_full Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller
title_fullStr Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller
title_full_unstemmed Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller
title_short Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller
title_sort smart cognitive iot devices using multi-layer perception neural network on limited microcontroller
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316597/
https://www.ncbi.nlm.nih.gov/pubmed/35890787
http://dx.doi.org/10.3390/s22145106
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