Applying Knowledge Inference on Event-Conjunction for Automatic Control in Smart Building
Smart building, one of IoT-based emerging applications is where energy-efficiency, human comfort, automation, security could be managed even better. However, at the current stage, a unified and practical framework for knowledge inference inside the smart building is still lacking. In this paper, we...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/3/935 |
| _version_ | 1797409196165038080 |
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| author | Hangli Ge Xiaohui Peng Noboru Koshizuka |
| author_facet | Hangli Ge Xiaohui Peng Noboru Koshizuka |
| author_sort | Hangli Ge |
| collection | DOAJ |
| description | Smart building, one of IoT-based emerging applications is where energy-efficiency, human comfort, automation, security could be managed even better. However, at the current stage, a unified and practical framework for knowledge inference inside the smart building is still lacking. In this paper, we present a practical proposal of knowledge extraction on event-conjunction for automatic control in smart buildings. The proposal consists of a unified API design, ontology model, inference engine for knowledge extraction. Two types of models: finite state machine(FSMs) and bayesian network (BN) have been used for capturing the state transition and sensor data fusion. In particular, to solve the problem that the size of time interval observations between two correlated events was too small to be approximated for estimation, we utilized the Markov Chain Monte Carlo (MCMC) sampling method to optimize the sampling on time intervals. The proposal has been put into use in a real smart building environment. 78-days data collection of the light states and elevator states has been conducted for evaluation. Several events have been inferred in the evaluation, such as room occupancy, elevator moving, as well as the event conjunction of both. The inference on the users’ waiting time of elevator-using revealed the potentials and effectiveness of the automatic control on the elevator. |
| first_indexed | 2024-03-09T04:09:51Z |
| format | Article |
| id | doaj.art-9e3a03b435134f75a61cb2324fd2503e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T04:09:51Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-9e3a03b435134f75a61cb2324fd2503e2023-12-03T14:01:35ZengMDPI AGApplied Sciences2076-34172021-01-0111393510.3390/app11030935Applying Knowledge Inference on Event-Conjunction for Automatic Control in Smart BuildingHangli Ge0Xiaohui Peng1Noboru Koshizuka2Interfaculty Initiative in Information Studies, The University of Tokyo, Tokyo 1130033, JapanChinese Academy of Sciences, Institute of Computing Technology, Beijing 100190, ChinaInterfaculty Initiative in Information Studies, The University of Tokyo, Tokyo 1130033, JapanSmart building, one of IoT-based emerging applications is where energy-efficiency, human comfort, automation, security could be managed even better. However, at the current stage, a unified and practical framework for knowledge inference inside the smart building is still lacking. In this paper, we present a practical proposal of knowledge extraction on event-conjunction for automatic control in smart buildings. The proposal consists of a unified API design, ontology model, inference engine for knowledge extraction. Two types of models: finite state machine(FSMs) and bayesian network (BN) have been used for capturing the state transition and sensor data fusion. In particular, to solve the problem that the size of time interval observations between two correlated events was too small to be approximated for estimation, we utilized the Markov Chain Monte Carlo (MCMC) sampling method to optimize the sampling on time intervals. The proposal has been put into use in a real smart building environment. 78-days data collection of the light states and elevator states has been conducted for evaluation. Several events have been inferred in the evaluation, such as room occupancy, elevator moving, as well as the event conjunction of both. The inference on the users’ waiting time of elevator-using revealed the potentials and effectiveness of the automatic control on the elevator.https://www.mdpi.com/2076-3417/11/3/935smart buildingInternet of Things (IoT)Markov Chain Monte Carlo (MCMC)ontologygraph model |
| spellingShingle | Hangli Ge Xiaohui Peng Noboru Koshizuka Applying Knowledge Inference on Event-Conjunction for Automatic Control in Smart Building Applied Sciences smart building Internet of Things (IoT) Markov Chain Monte Carlo (MCMC) ontology graph model |
| title | Applying Knowledge Inference on Event-Conjunction for Automatic Control in Smart Building |
| title_full | Applying Knowledge Inference on Event-Conjunction for Automatic Control in Smart Building |
| title_fullStr | Applying Knowledge Inference on Event-Conjunction for Automatic Control in Smart Building |
| title_full_unstemmed | Applying Knowledge Inference on Event-Conjunction for Automatic Control in Smart Building |
| title_short | Applying Knowledge Inference on Event-Conjunction for Automatic Control in Smart Building |
| title_sort | applying knowledge inference on event conjunction for automatic control in smart building |
| topic | smart building Internet of Things (IoT) Markov Chain Monte Carlo (MCMC) ontology graph model |
| url | https://www.mdpi.com/2076-3417/11/3/935 |
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