Multi-Dimensional Joint Prediction Model for IoT Sensor Data Search
In recent years, with the rapid deployment of various Internet of Things (IoT) devices, it becomes a crucial and practical challenge to enable real-time search for objects, data, and services in the Internet of Everything. The IoT data prediction model can not only provide solutions for the real-tim...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8756194/ |
| _version_ | 1818412509775790080 |
|---|---|
| author | Cong Zhang Yuanan Liu Fan Wu Wenhao Fan Jielong Tang Haosong Liu |
| author_facet | Cong Zhang Yuanan Liu Fan Wu Wenhao Fan Jielong Tang Haosong Liu |
| author_sort | Cong Zhang |
| collection | DOAJ |
| description | In recent years, with the rapid deployment of various Internet of Things (IoT) devices, it becomes a crucial and practical challenge to enable real-time search for objects, data, and services in the Internet of Everything. The IoT data prediction model can not only provide solutions for the real-time acquisition of the IoT sensor data but also provide more meaningful applications than the traditional IoT event detection model. In this paper, we use the complex time series formed by various types of sensors to establish a multi-dimensional feature selection model and a dynamic sensor-data prediction model. Compared with the traditional data prediction model, our model improves the accuracy and stability of the long-term prediction results of the IoT sensor data. Finally, we evaluate our prediction model using the Intel Berkeley Research Lab sensor data with an accuracy of over 98% and 92% accuracy on the Chicago Park District weather&water data. |
| first_indexed | 2024-12-14T10:48:27Z |
| format | Article |
| id | doaj.art-93aa3ab83d6a4e5c9336e27e088ad579 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T10:48:27Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-93aa3ab83d6a4e5c9336e27e088ad5792022-12-21T23:05:21ZengIEEEIEEE Access2169-35362019-01-017908639087310.1109/ACCESS.2019.29272398756194Multi-Dimensional Joint Prediction Model for IoT Sensor Data SearchCong Zhang0https://orcid.org/0000-0001-6387-503XYuanan Liu1Fan Wu2https://orcid.org/0000-0002-1286-7141Wenhao Fan3https://orcid.org/0000-0001-5288-8708Jielong Tang4Haosong Liu5School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, ChinaInternational School, Beijing University of Posts and Telecommunications, Beijing, ChinaInternational School, Beijing University of Posts and Telecommunications, Beijing, ChinaIn recent years, with the rapid deployment of various Internet of Things (IoT) devices, it becomes a crucial and practical challenge to enable real-time search for objects, data, and services in the Internet of Everything. The IoT data prediction model can not only provide solutions for the real-time acquisition of the IoT sensor data but also provide more meaningful applications than the traditional IoT event detection model. In this paper, we use the complex time series formed by various types of sensors to establish a multi-dimensional feature selection model and a dynamic sensor-data prediction model. Compared with the traditional data prediction model, our model improves the accuracy and stability of the long-term prediction results of the IoT sensor data. Finally, we evaluate our prediction model using the Intel Berkeley Research Lab sensor data with an accuracy of over 98% and 92% accuracy on the Chicago Park District weather&water data.https://ieeexplore.ieee.org/document/8756194/IoT sensor data predictioncomplex time seriesmulti-dimensional feature selection |
| spellingShingle | Cong Zhang Yuanan Liu Fan Wu Wenhao Fan Jielong Tang Haosong Liu Multi-Dimensional Joint Prediction Model for IoT Sensor Data Search IEEE Access IoT sensor data prediction complex time series multi-dimensional feature selection |
| title | Multi-Dimensional Joint Prediction Model for IoT Sensor Data Search |
| title_full | Multi-Dimensional Joint Prediction Model for IoT Sensor Data Search |
| title_fullStr | Multi-Dimensional Joint Prediction Model for IoT Sensor Data Search |
| title_full_unstemmed | Multi-Dimensional Joint Prediction Model for IoT Sensor Data Search |
| title_short | Multi-Dimensional Joint Prediction Model for IoT Sensor Data Search |
| title_sort | multi dimensional joint prediction model for iot sensor data search |
| topic | IoT sensor data prediction complex time series multi-dimensional feature selection |
| url | https://ieeexplore.ieee.org/document/8756194/ |
| work_keys_str_mv | AT congzhang multidimensionaljointpredictionmodelforiotsensordatasearch AT yuananliu multidimensionaljointpredictionmodelforiotsensordatasearch AT fanwu multidimensionaljointpredictionmodelforiotsensordatasearch AT wenhaofan multidimensionaljointpredictionmodelforiotsensordatasearch AT jielongtang multidimensionaljointpredictionmodelforiotsensordatasearch AT haosongliu multidimensionaljointpredictionmodelforiotsensordatasearch |