Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model
In this paper, based on the information entropy and spatio-temporal correlation of sensing nodes in the Internet of Things (IoT), a Spatio-temporal Scope Information Model (SSIM) is proposed to quantify the scope of the valuable information of sensor data. Specifically, the valuable information of s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/12/5437 |
| _version_ | 1797592740965384192 |
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| author | Yang Liu Chen Dong Xiaoqi Qin Xiaodong Xu |
| author_facet | Yang Liu Chen Dong Xiaoqi Qin Xiaodong Xu |
| author_sort | Yang Liu |
| collection | DOAJ |
| description | In this paper, based on the information entropy and spatio-temporal correlation of sensing nodes in the Internet of Things (IoT), a Spatio-temporal Scope Information Model (SSIM) is proposed to quantify the scope of the valuable information of sensor data. Specifically, the valuable information of sensor data decays with space and time, which can be used to guide the system to make efficient sensor activation scheduling decisions for regional sensing accuracy. A simple sensing and monitoring system with three sensor nodes is investigated in this paper, and a single-step scheduling decision mechanism is proposed for the optimization problem of maximizing valuable information acquisition and efficient sensor activation scheduling in the sensed region. Regarding the above mechanism, the scheduling results and approximate numerical bounds on the node layout between different scheduling results are obtained through theoretical analyses, which are consistent with simulation. In addition, a long-term decision mechanism is also proposed for the aforementioned optimization issues, where the scheduling results with different node layouts are derived by modeling as a Markov decision process and utilizing the Q-learning algorithm. Concerning the above two mechanisms, the performance of both is verified by conducting experiments using the relative humidity dataset; furthermore, the differences in performance and limitations of the model are discussed and summarized. |
| first_indexed | 2024-03-11T01:57:26Z |
| format | Article |
| id | doaj.art-d937f14337b4452ba1bef73901b7ebb8 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:57:26Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d937f14337b4452ba1bef73901b7ebb82023-11-18T12:31:00ZengMDPI AGSensors1424-82202023-06-012312543710.3390/s23125437Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information ModelYang Liu0Chen Dong1Xiaoqi Qin2Xiaodong Xu3State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaIn this paper, based on the information entropy and spatio-temporal correlation of sensing nodes in the Internet of Things (IoT), a Spatio-temporal Scope Information Model (SSIM) is proposed to quantify the scope of the valuable information of sensor data. Specifically, the valuable information of sensor data decays with space and time, which can be used to guide the system to make efficient sensor activation scheduling decisions for regional sensing accuracy. A simple sensing and monitoring system with three sensor nodes is investigated in this paper, and a single-step scheduling decision mechanism is proposed for the optimization problem of maximizing valuable information acquisition and efficient sensor activation scheduling in the sensed region. Regarding the above mechanism, the scheduling results and approximate numerical bounds on the node layout between different scheduling results are obtained through theoretical analyses, which are consistent with simulation. In addition, a long-term decision mechanism is also proposed for the aforementioned optimization issues, where the scheduling results with different node layouts are derived by modeling as a Markov decision process and utilizing the Q-learning algorithm. Concerning the above two mechanisms, the performance of both is verified by conducting experiments using the relative humidity dataset; furthermore, the differences in performance and limitations of the model are discussed and summarized.https://www.mdpi.com/1424-8220/23/12/5437internet of thingsspatio-temporal scope information modelspatio-temporal correlationsensor scheduling |
| spellingShingle | Yang Liu Chen Dong Xiaoqi Qin Xiaodong Xu Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model Sensors internet of things spatio-temporal scope information model spatio-temporal correlation sensor scheduling |
| title | Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model |
| title_full | Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model |
| title_fullStr | Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model |
| title_full_unstemmed | Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model |
| title_short | Efficient Sensor Scheduling Strategy Based on Spatio-Temporal Scope Information Model |
| title_sort | efficient sensor scheduling strategy based on spatio temporal scope information model |
| topic | internet of things spatio-temporal scope information model spatio-temporal correlation sensor scheduling |
| url | https://www.mdpi.com/1424-8220/23/12/5437 |
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