Porting Rulex Software to the Raspberry Pi for Machine Learning Applications on the Edge

Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning applications have become a rudimentary procedure in vi...

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Published in:	Sensors (Basel, Switzerland) Vol. 21; no. 19; p. 6526
Main Authors:	Daher, Ali Walid, Rizik, Ali, Muselli, Marco, Chible, Hussein, Caviglia, Daniele D.
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 29.09.2021 MDPI
Subjects:	Algorithms Artificial intelligence Classification Climate change Datasets edge computing Embedded systems Energy consumption image classification Internet of Things Libraries Literature reviews Machine learning Neural networks Operating systems pre-processing Smartphones Software packages Universal Serial Bus User interface
ISSN:	1424-8220, 1424-8220
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Abstract	Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning applications have become a rudimentary procedure in virtually every industrial or preliminary system. Consequently, the Raspberry Pi is adopted, which is a low-cost computing platform that is profitably applied in the field of IoT. As for the software part, among the plethora of Machine Learning (ML) paradigms reported in the literature, we identified Rulex, as a good ML platform, suitable to be implemented on the Raspberry Pi. In this paper, we present the porting of the Rulex ML platform on the board to perform ML forecasts in an IoT setup. Specifically, we explain the porting Rulex’s libraries on Windows 32 Bits, Ubuntu 64 Bits, and Raspbian 32 Bits. Therefore, with the aim of carrying out an in-depth verification of the application possibilities, we propose to perform forecasts on five unrelated datasets from five different applications, having varying sizes in terms of the number of records, skewness, and dimensionality. These include a small Urban Classification dataset, three larger datasets concerning Human Activity detection, a Biomedical dataset related to mental state, and a Vehicle Activity Recognition dataset. The overall accuracies for the forecasts performed are: 84.13%, 99.29% (for SVM), 95.47% (for SVM), and 95.27% (For KNN) respectively. Finally, an image-based gender classification dataset is employed to perform image classification on the Edge. Moreover, a novel image pre-processing Algorithm was developed that converts images into Time-series by relying on statistical contour-based detection techniques. Even though the dataset contains inconsistent and random images, in terms of subjects and settings, Rulex achieves an overall accuracy of 96.47% while competing with the literature which is dominated by forward-facing and mugshot images. Additionally, power consumption for the Raspberry Pi in a Client/Server setup was compared with an HP laptop, where the board takes more time, but consumes less energy for the same ML task.
AbstractList	Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning applications have become a rudimentary procedure in virtually every industrial or preliminary system. Consequently, the Raspberry Pi is adopted, which is a low-cost computing platform that is profitably applied in the field of IoT. As for the software part, among the plethora of Machine Learning (ML) paradigms reported in the literature, we identified Rulex, as a good ML platform, suitable to be implemented on the Raspberry Pi. In this paper, we present the porting of the Rulex ML platform on the board to perform ML forecasts in an IoT setup. Specifically, we explain the porting Rulex’s libraries on Windows 32 Bits, Ubuntu 64 Bits, and Raspbian 32 Bits. Therefore, with the aim of carrying out an in-depth verification of the application possibilities, we propose to perform forecasts on five unrelated datasets from five different applications, having varying sizes in terms of the number of records, skewness, and dimensionality. These include a small Urban Classification dataset, three larger datasets concerning Human Activity detection, a Biomedical dataset related to mental state, and a Vehicle Activity Recognition dataset. The overall accuracies for the forecasts performed are: 84.13%, 99.29% (for SVM), 95.47% (for SVM), and 95.27% (For KNN) respectively. Finally, an image-based gender classification dataset is employed to perform image classification on the Edge. Moreover, a novel image pre-processing Algorithm was developed that converts images into Time-series by relying on statistical contour-based detection techniques. Even though the dataset contains inconsistent and random images, in terms of subjects and settings, Rulex achieves an overall accuracy of 96.47% while competing with the literature which is dominated by forward-facing and mugshot images. Additionally, power consumption for the Raspberry Pi in a Client/Server setup was compared with an HP laptop, where the board takes more time, but consumes less energy for the same ML task. Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning applications have become a rudimentary procedure in virtually every industrial or preliminary system. Consequently, the Raspberry Pi is adopted, which is a low-cost computing platform that is profitably applied in the field of IoT. As for the software part, among the plethora of Machine Learning (ML) paradigms reported in the literature, we identified Rulex, as a good ML platform, suitable to be implemented on the Raspberry Pi. In this paper, we present the porting of the Rulex ML platform on the board to perform ML forecasts in an IoT setup. Specifically, we explain the porting Rulex's libraries on Windows 32 Bits, Ubuntu 64 Bits, and Raspbian 32 Bits. Therefore, with the aim of carrying out an in-depth verification of the application possibilities, we propose to perform forecasts on five unrelated datasets from five different applications, having varying sizes in terms of the number of records, skewness, and dimensionality. These include a small Urban Classification dataset, three larger datasets concerning Human Activity detection, a Biomedical dataset related to mental state, and a Vehicle Activity Recognition dataset. The overall accuracies for the forecasts performed are: 84.13%, 99.29% (for SVM), 95.47% (for SVM), and 95.27% (For KNN) respectively. Finally, an image-based gender classification dataset is employed to perform image classification on the Edge. Moreover, a novel image pre-processing Algorithm was developed that converts images into Time-series by relying on statistical contour-based detection techniques. Even though the dataset contains inconsistent and random images, in terms of subjects and settings, Rulex achieves an overall accuracy of 96.47% while competing with the literature which is dominated by forward-facing and mugshot images. Additionally, power consumption for the Raspberry Pi in a Client/Server setup was compared with an HP laptop, where the board takes more time, but consumes less energy for the same ML task.Edge Computing enables to perform measurement and cognitive decisions outside a central server by performing data storage, manipulation, and processing on the Internet of Things (IoT) node. Also, Artificial Intelligence (AI) and Machine Learning applications have become a rudimentary procedure in virtually every industrial or preliminary system. Consequently, the Raspberry Pi is adopted, which is a low-cost computing platform that is profitably applied in the field of IoT. As for the software part, among the plethora of Machine Learning (ML) paradigms reported in the literature, we identified Rulex, as a good ML platform, suitable to be implemented on the Raspberry Pi. In this paper, we present the porting of the Rulex ML platform on the board to perform ML forecasts in an IoT setup. Specifically, we explain the porting Rulex's libraries on Windows 32 Bits, Ubuntu 64 Bits, and Raspbian 32 Bits. Therefore, with the aim of carrying out an in-depth verification of the application possibilities, we propose to perform forecasts on five unrelated datasets from five different applications, having varying sizes in terms of the number of records, skewness, and dimensionality. These include a small Urban Classification dataset, three larger datasets concerning Human Activity detection, a Biomedical dataset related to mental state, and a Vehicle Activity Recognition dataset. The overall accuracies for the forecasts performed are: 84.13%, 99.29% (for SVM), 95.47% (for SVM), and 95.27% (For KNN) respectively. Finally, an image-based gender classification dataset is employed to perform image classification on the Edge. Moreover, a novel image pre-processing Algorithm was developed that converts images into Time-series by relying on statistical contour-based detection techniques. Even though the dataset contains inconsistent and random images, in terms of subjects and settings, Rulex achieves an overall accuracy of 96.47% while competing with the literature which is dominated by forward-facing and mugshot images. Additionally, power consumption for the Raspberry Pi in a Client/Server setup was compared with an HP laptop, where the board takes more time, but consumes less energy for the same ML task.
Author	Daher, Ali Walid Rizik, Ali Muselli, Marco Caviglia, Daniele D. Chible, Hussein
AuthorAffiliation	3 Consiglio Nazionale delle Ricerche, Institute of Electronics Computer and Telecommunication Engineering (IEIIT), 16149 Genoa, Italy; marco.muselli@ieiit.cnr.it 4 Rulex Innovation Labs, Rulex Inc., 16122 Genoa, Italy 1 COSMIC Lab, Department of Electrical, Electronic and Telecommunications Engineering and Naval Architecture (DITEN), University of Genoa, 16145 Genoa, Italy; ali.daher@edu.unige.it (A.W.D.); ali.rizik@edu.unige.it (A.R.) 2 MECRL Laboratory, Ph.D. School for Sciences and Technology, Lebanese University, Beirut 6573/14, Lebanon; hchible@ul.edu.lb
AuthorAffiliation_xml	– name: 2 MECRL Laboratory, Ph.D. School for Sciences and Technology, Lebanese University, Beirut 6573/14, Lebanon; hchible@ul.edu.lb – name: 3 Consiglio Nazionale delle Ricerche, Institute of Electronics Computer and Telecommunication Engineering (IEIIT), 16149 Genoa, Italy; marco.muselli@ieiit.cnr.it – name: 4 Rulex Innovation Labs, Rulex Inc., 16122 Genoa, Italy – name: 1 COSMIC Lab, Department of Electrical, Electronic and Telecommunications Engineering and Naval Architecture (DITEN), University of Genoa, 16145 Genoa, Italy; ali.daher@edu.unige.it (A.W.D.); ali.rizik@edu.unige.it (A.R.)
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Cites_doi	10.1145/2872518.2889385 10.1109/JSEN.2021.3095674 10.1007/11731177_4 10.1007/978-3-030-66729-0_33 10.1109/TIFS.2014.2359646 10.1109/ICSESS.2017.8342870 10.1109/TELFOR.2014.7034409 10.1109/ISNCC.2016.7746067 10.1145/3231053.3231062 10.1177/1460458216655188 10.1109/CVPRW.2015.7301352 10.1109/TKDE.2009.206 10.1109/PST.2016.7906930 10.14806/ej.18.B.549 10.3390/s21165395 10.1109/ICEngTechnol.2017.8308186 10.1109/ACCESS.2017.2778504 10.1109/JIOT.2018.2805263 10.3390/app10249113 10.1109/ICECS46596.2019.8965072 10.3390/s21092984 10.3390/s20216335 10.1109/MIPRO.2014.6859717 10.1088/1742-6596/396/5/052021 10.1145/3007748.3007777 10.1109/IS.2018.8710576
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Copyright	2021 by the authors. 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2021 by the authors. 2021
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 This paper is an extended version of our paper published in Daher, A.W.; Rizik, A.; Muselli, M.; Chible, H.; Caviglia, D.D. Porting Rulex Machine Learning Software to the Raspberry Pi as an Edge Computing Device. In Proceedings of the International Conference on Applications in Electronics Pervading Industry, Environment and Society, Online Event, 19–20 November 2020.
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SubjectTerms	Algorithms Artificial intelligence Classification Climate change Datasets edge computing Embedded systems Energy consumption image classification Internet of Things Libraries Literature reviews Machine learning Neural networks Operating systems pre-processing Smartphones Software packages Universal Serial Bus User interface
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