A Real-Time Data Monitoring Framework for Predictive Maintenance Based on the Internet of Things
The Internet of Things (IoT) is a platform that manages daily life tasks to establish an interaction between things and humans. One of its applications, the smart office that uses the Internet to monitor electrical appliances and sensor data using an automation system, is presented in this study. So...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2023/9991029 |
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| author | Mudita Uppal Deepali Gupta Nitin Goyal Agbotiname Lucky Imoize Arun Kumar Stephen Ojo Subhendu Kumar Pani Yongsung Kim Jaeun Choi |
| author_facet | Mudita Uppal Deepali Gupta Nitin Goyal Agbotiname Lucky Imoize Arun Kumar Stephen Ojo Subhendu Kumar Pani Yongsung Kim Jaeun Choi |
| author_sort | Mudita Uppal |
| collection | DOAJ |
| description | The Internet of Things (IoT) is a platform that manages daily life tasks to establish an interaction between things and humans. One of its applications, the smart office that uses the Internet to monitor electrical appliances and sensor data using an automation system, is presented in this study. Some of the limitations of the existing office automation system are an unfriendly user interface, lack of IoT technology, high cost, or restricted range of wireless transmission. Therefore, this paper presents the design and fabrication of an IoT-based office automation system with a user-friendly smartphone interface. Also, real-time data monitoring is conducted for the predictive maintenance of sensor nodes. This model uses an Arduino Mega 2560 Rev3 microcontroller connected to different appliances and sensors. The data collected from different sensors and appliances are sent to the cloud and accessible to the user on their smartphone despite their location. A sensor fault prediction model based on a machine learning algorithm is proposed in this paper, where the k-nearest neighbors model achieved better performance with 99.63% accuracy, 99.59% F1-score, and 99.67% recall. The performance of both models, i.e., k-nearest neighbors and naive Bayes, was evaluated using different performance metrics such as precision, recall, F1-score, and accuracy. It is a reliable, continuous, and stable automation system that provides safety and convenience to smart office employees and improves their work efficiency while saving resources. |
| format | Article |
| id | doaj-art-63dacc1522d94debb41562a3125a8edd |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-63dacc1522d94debb41562a3125a8edd2025-02-03T01:30:44ZengWileyComplexity1099-05262023-01-01202310.1155/2023/9991029A Real-Time Data Monitoring Framework for Predictive Maintenance Based on the Internet of ThingsMudita Uppal0Deepali Gupta1Nitin Goyal2Agbotiname Lucky Imoize3Arun Kumar4Stephen Ojo5Subhendu Kumar Pani6Yongsung Kim7Jaeun Choi8Chitkara University Institute of Engineering and TechnologyChitkara University Institute of Engineering and TechnologyDepartment of Computer Science and EngineeringDepartment of Electrical and Electronics EngineeringGalgotias College of Engineering and TechnologyDepartment of Electrical and Computer EngineeringKrupajal Engineering CollegeDepartment of Technology EducationCollege of BusinessThe Internet of Things (IoT) is a platform that manages daily life tasks to establish an interaction between things and humans. One of its applications, the smart office that uses the Internet to monitor electrical appliances and sensor data using an automation system, is presented in this study. Some of the limitations of the existing office automation system are an unfriendly user interface, lack of IoT technology, high cost, or restricted range of wireless transmission. Therefore, this paper presents the design and fabrication of an IoT-based office automation system with a user-friendly smartphone interface. Also, real-time data monitoring is conducted for the predictive maintenance of sensor nodes. This model uses an Arduino Mega 2560 Rev3 microcontroller connected to different appliances and sensors. The data collected from different sensors and appliances are sent to the cloud and accessible to the user on their smartphone despite their location. A sensor fault prediction model based on a machine learning algorithm is proposed in this paper, where the k-nearest neighbors model achieved better performance with 99.63% accuracy, 99.59% F1-score, and 99.67% recall. The performance of both models, i.e., k-nearest neighbors and naive Bayes, was evaluated using different performance metrics such as precision, recall, F1-score, and accuracy. It is a reliable, continuous, and stable automation system that provides safety and convenience to smart office employees and improves their work efficiency while saving resources.http://dx.doi.org/10.1155/2023/9991029 |
| spellingShingle | Mudita Uppal Deepali Gupta Nitin Goyal Agbotiname Lucky Imoize Arun Kumar Stephen Ojo Subhendu Kumar Pani Yongsung Kim Jaeun Choi A Real-Time Data Monitoring Framework for Predictive Maintenance Based on the Internet of Things Complexity |
| title | A Real-Time Data Monitoring Framework for Predictive Maintenance Based on the Internet of Things |
| title_full | A Real-Time Data Monitoring Framework for Predictive Maintenance Based on the Internet of Things |
| title_fullStr | A Real-Time Data Monitoring Framework for Predictive Maintenance Based on the Internet of Things |
| title_full_unstemmed | A Real-Time Data Monitoring Framework for Predictive Maintenance Based on the Internet of Things |
| title_short | A Real-Time Data Monitoring Framework for Predictive Maintenance Based on the Internet of Things |
| title_sort | real time data monitoring framework for predictive maintenance based on the internet of things |
| url | http://dx.doi.org/10.1155/2023/9991029 |
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