Next Article in Journal
Thermal and Optical Properties of Porous Nanomesh Structures for Sensitive Terahertz Bolometric Detection
Previous Article in Journal
Introducing the CYSAS-S3 Dataset for Operationalizing a Mission-Oriented Cyber Situational Awareness
 
 
Article

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

1
National Telecommunication Institute (NTI), 5 Mahmoud El Miligui Street, 6th District-Nasr City, Cairo 11768, Egypt
2
Faculty of Engineering, Minia University, Minia 61519, Egypt
*
Author to whom correspondence should be addressed.
Academic Editor: Omprakash Kaiwartya
Sensors 2022, 22(14), 5106; https://doi.org/10.3390/s22145106
Received: 2 June 2022 / Revised: 1 July 2022 / Accepted: 5 July 2022 / Published: 7 July 2022
(This article belongs to the Section Internet of Things)
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. View Full-Text
Keywords: cognitive IoT; smart nodes; critical systems; neural networks; microcontrollers cognitive IoT; smart nodes; critical systems; neural networks; microcontrollers
Show Figures

Figure 1

MDPI and ACS Style

Hussein, M.; Mohammed, Y.S.; Galal, A.I.; Abd-Elrahman, E.; Zorkany, M. Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller. Sensors 2022, 22, 5106. https://doi.org/10.3390/s22145106

AMA Style

Hussein M, Mohammed YS, Galal AI, Abd-Elrahman E, Zorkany M. Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller. Sensors. 2022; 22(14):5106. https://doi.org/10.3390/s22145106

Chicago/Turabian Style

Hussein, Mahmoud, Yehia Sayed Mohammed, Ahmed I. Galal, Emad Abd-Elrahman, and Mohamed Zorkany. 2022. "Smart Cognitive IoT Devices Using Multi-Layer Perception Neural Network on Limited Microcontroller" Sensors 22, no. 14: 5106. https://doi.org/10.3390/s22145106

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop