{"title":"IoT-HGDS: Internet of Things integrated machine learning based hazardous gases detection system for smart kitchen","authors":"Kanak Kumar , Anshul Verma , Pradeepika Verma","doi":"10.1016/j.iot.2024.101396","DOIUrl":null,"url":null,"abstract":"<div><div>This paper proposes an Internet of Things (IoT) and Machine Learning (ML) integrated Hazardous Gas Detection System (IoT-HGDS) for smart kitchens. The design incorporates six tin-oxide-based metal–oxide–semiconductor (MOS) gas sensor arrays and one DHT22 (temperature & humidity sensor). This IoT-HGDS can detect different hazardous gases, Volatile Organic Compounds (VOCs), and odors responses released from the kitchen’s materials and transmit them to a Remote Data Processing Centre (RDPC) through Amazon-Web Services (AWS) in real time. In this experiment, we collected <span><math><mrow><mn>150</mn><mo>×</mo><mn>9</mn><mo>=</mo><mn>1350</mn></mrow></math></span> samples from 9 kitchen materials like ghee, milk, liquid petroleum gas (LPG), bread, mustard oil, compressed natural gas (CNG), pigeon peas, refined oil, and kerosene. The Standardized Independent Component Analysis (SICA) pre-processing technique has been used to clean data, standardize the features, and remove outliers. ML approaches like Logistic Regression (LR), Adaptive Boosting (AdaBoost) and Regularized Discriminant Analysis (RDA) have been applied for accurate identification of gases/VOCs class and provide immediate alerts to improve kitchen safety. The SICA-RDA classifier outperformed (highest accuracy at 97.78 %) as compared to LR and AdaBoost in terms of performance and balanced precision, recall, and F1-Score. LR has the lowest performance in all metrics. LPG has the lowest Mean Squared Error (MSE) of <span><math><mrow><mn>6</mn><mo>.</mo><mn>62</mn><mo>×</mo><mn>10</mn><mo>−</mo><mn>7</mn></mrow></math></span>, while CNG has the highest MSE of <span><math><mrow><mn>3</mn><mo>.</mo><mn>60</mn><mo>×</mo><mn>10</mn><mo>−</mo><mn>4</mn></mrow></math></span>. This system can intelligently preserve gases, ensure safety precautions, and prevent accidents in the kitchens.</div></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet of Things","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2542660524003378","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
This paper proposes an Internet of Things (IoT) and Machine Learning (ML) integrated Hazardous Gas Detection System (IoT-HGDS) for smart kitchens. The design incorporates six tin-oxide-based metal–oxide–semiconductor (MOS) gas sensor arrays and one DHT22 (temperature & humidity sensor). This IoT-HGDS can detect different hazardous gases, Volatile Organic Compounds (VOCs), and odors responses released from the kitchen’s materials and transmit them to a Remote Data Processing Centre (RDPC) through Amazon-Web Services (AWS) in real time. In this experiment, we collected samples from 9 kitchen materials like ghee, milk, liquid petroleum gas (LPG), bread, mustard oil, compressed natural gas (CNG), pigeon peas, refined oil, and kerosene. The Standardized Independent Component Analysis (SICA) pre-processing technique has been used to clean data, standardize the features, and remove outliers. ML approaches like Logistic Regression (LR), Adaptive Boosting (AdaBoost) and Regularized Discriminant Analysis (RDA) have been applied for accurate identification of gases/VOCs class and provide immediate alerts to improve kitchen safety. The SICA-RDA classifier outperformed (highest accuracy at 97.78 %) as compared to LR and AdaBoost in terms of performance and balanced precision, recall, and F1-Score. LR has the lowest performance in all metrics. LPG has the lowest Mean Squared Error (MSE) of , while CNG has the highest MSE of . This system can intelligently preserve gases, ensure safety precautions, and prevent accidents in the kitchens.
期刊介绍:
Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT.
The journal will place a high priority on timely publication, and provide a home for high quality.
Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.