Design of Smart Home Implementation Within IoT Natural Language Interface
To process continuous sensor data in Internet of Things (IoT) environments, this study optimizes queries using multiple MJoin operators. To achieve efficient storage management, it classifies and reduces data using a support vector machine (SVM) classification algorithm. A global shared query execut...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9086607/ |
| _version_ | 1818853387244929024 |
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| author | Tae-Yeun Kim Sang-Hyun Bae Young-Eun An |
| author_facet | Tae-Yeun Kim Sang-Hyun Bae Young-Eun An |
| author_sort | Tae-Yeun Kim |
| collection | DOAJ |
| description | To process continuous sensor data in Internet of Things (IoT) environments, this study optimizes queries using multiple MJoin operators. To achieve efficient storage management, it classifies and reduces data using a support vector machine (SVM) classification algorithm. A global shared query execution technique was used to optimize multiple MJoin queries. By comparing each kernel function of the SVM classification algorithm, the system's performance was evaluated through experiments according to the selected optimal kernel function and changes in sliding window size. Furthermore, to implement a smart home system that can actively respond to users, classified and reduced sensor data were utilized to enable the intelligent control of devices inside the home. The sensor data (e.g., temperature, humidity, gas) used to recognize the current conditions of an IoT-based smart home system and corresponding date data were classified into decision trees, and the system was designed using five sensors to intelligently control priorities such as ventilation, temperature, and fire and intrusion detection. The experiments demonstrated that the multiple MJoin technique yields high improvements in performance with relatively few searches. In this study, the sigmoid was selected as the optimal kernel function for the SVM classification algorithm. According to the SVM classification algorithm results, based on changes in the sliding window size, the average error rate was 2.42%, the reduction result was 17.58%, and the classification accuracy was 85.94%. According to the comparison of the classification performance of SVM and other algorithms, the SVM classification algorithm exhibited a minimum 9% better classification performance. Thus, compared to existing home systems, this algorithm is expected to increase system efficiency and convenience by enabling the configuration of a more intelligent environment according to the user's characteristics or requirements. |
| first_indexed | 2024-12-19T07:36:00Z |
| format | Article |
| id | doaj.art-eac55fd64d4e4e8b95e16f8ccd5150e4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:36:00Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-eac55fd64d4e4e8b95e16f8ccd5150e42022-12-21T20:30:35ZengIEEEIEEE Access2169-35362020-01-018849298494910.1109/ACCESS.2020.29925129086607Design of Smart Home Implementation Within IoT Natural Language InterfaceTae-Yeun Kim0https://orcid.org/0000-0002-1644-1869Sang-Hyun Bae1https://orcid.org/0000-0002-3859-8365Young-Eun An2https://orcid.org/0000-0003-3072-3800SW Convergence Education Institute, Chosun University, Gwangju, South KoreaDepartment of Computer Science and Statistics, Chosun University, Gwangju, South KoreaCollege of General Education, Chosun University, Gwangju, South KoreaTo process continuous sensor data in Internet of Things (IoT) environments, this study optimizes queries using multiple MJoin operators. To achieve efficient storage management, it classifies and reduces data using a support vector machine (SVM) classification algorithm. A global shared query execution technique was used to optimize multiple MJoin queries. By comparing each kernel function of the SVM classification algorithm, the system's performance was evaluated through experiments according to the selected optimal kernel function and changes in sliding window size. Furthermore, to implement a smart home system that can actively respond to users, classified and reduced sensor data were utilized to enable the intelligent control of devices inside the home. The sensor data (e.g., temperature, humidity, gas) used to recognize the current conditions of an IoT-based smart home system and corresponding date data were classified into decision trees, and the system was designed using five sensors to intelligently control priorities such as ventilation, temperature, and fire and intrusion detection. The experiments demonstrated that the multiple MJoin technique yields high improvements in performance with relatively few searches. In this study, the sigmoid was selected as the optimal kernel function for the SVM classification algorithm. According to the SVM classification algorithm results, based on changes in the sliding window size, the average error rate was 2.42%, the reduction result was 17.58%, and the classification accuracy was 85.94%. According to the comparison of the classification performance of SVM and other algorithms, the SVM classification algorithm exhibited a minimum 9% better classification performance. Thus, compared to existing home systems, this algorithm is expected to increase system efficiency and convenience by enabling the configuration of a more intelligent environment according to the user's characteristics or requirements.https://ieeexplore.ieee.org/document/9086607/ApplicationInternet of Things (IoT)sensor datasmart home systemSVM algorithm |
| spellingShingle | Tae-Yeun Kim Sang-Hyun Bae Young-Eun An Design of Smart Home Implementation Within IoT Natural Language Interface IEEE Access Application Internet of Things (IoT) sensor data smart home system SVM algorithm |
| title | Design of Smart Home Implementation Within IoT Natural Language Interface |
| title_full | Design of Smart Home Implementation Within IoT Natural Language Interface |
| title_fullStr | Design of Smart Home Implementation Within IoT Natural Language Interface |
| title_full_unstemmed | Design of Smart Home Implementation Within IoT Natural Language Interface |
| title_short | Design of Smart Home Implementation Within IoT Natural Language Interface |
| title_sort | design of smart home implementation within iot natural language interface |
| topic | Application Internet of Things (IoT) sensor data smart home system SVM algorithm |
| url | https://ieeexplore.ieee.org/document/9086607/ |
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