Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive Threshold
With the development of intelligent IoT applications, vast amounts of data are generated by various volume sensors. These sensor data need to be reduced at the sensor and then reconstructed later to save bandwidth and energy. As the reduced data increase, the reconstructed data become less accurate....
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/23/9427 |
| _version_ | 1797399557116526592 |
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| author | Handuo Zhang Jun Na Bin Zhang |
| author_facet | Handuo Zhang Jun Na Bin Zhang |
| author_sort | Handuo Zhang |
| collection | DOAJ |
| description | With the development of intelligent IoT applications, vast amounts of data are generated by various volume sensors. These sensor data need to be reduced at the sensor and then reconstructed later to save bandwidth and energy. As the reduced data increase, the reconstructed data become less accurate. Usually, the trade-off between reduction rate and reconstruction accuracy is controlled by the reduction threshold, which is calculated by experiments based on historical data. Considering the dynamic nature of IoT, a fixed threshold cannot balance the reduction rate with the reconstruction accuracy adaptively. Aiming to dynamically balance the reduction rate with the reconstruction accuracy, an autonomous IoT data reduction method based on an adaptive threshold is proposed. During data reduction, concept drift detection is performed to capture IoT dynamic changes and trigger threshold adjustment. During data reconstruction, a data trend is added to improve reconstruction accuracy. The effectiveness of the proposed method is demonstrated by comparing the proposed method with the basic Kalman filtering algorithm, LMS algorithm, and PIP algorithm on stationary and nonstationary datasets. Compared with not applying the adaptive threshold, on average, there is an 11.7% improvement in accuracy for the same reduction rate or a 17.3% improvement in reduction rate for the same accuracy. |
| first_indexed | 2024-03-09T01:42:53Z |
| format | Article |
| id | doaj.art-47f96bb90dbe484a9fb3ef5fbad44b04 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T01:42:53Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-47f96bb90dbe484a9fb3ef5fbad44b042023-12-08T15:25:57ZengMDPI AGSensors1424-82202023-11-012323942710.3390/s23239427Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive ThresholdHanduo Zhang0Jun Na1Bin Zhang2School of Computer Science and Engineering, Northeastern University, Shenyang 110167, ChinaSoftware College, Northeastern University, Shenyang 110167, ChinaSoftware College, Northeastern University, Shenyang 110167, ChinaWith the development of intelligent IoT applications, vast amounts of data are generated by various volume sensors. These sensor data need to be reduced at the sensor and then reconstructed later to save bandwidth and energy. As the reduced data increase, the reconstructed data become less accurate. Usually, the trade-off between reduction rate and reconstruction accuracy is controlled by the reduction threshold, which is calculated by experiments based on historical data. Considering the dynamic nature of IoT, a fixed threshold cannot balance the reduction rate with the reconstruction accuracy adaptively. Aiming to dynamically balance the reduction rate with the reconstruction accuracy, an autonomous IoT data reduction method based on an adaptive threshold is proposed. During data reduction, concept drift detection is performed to capture IoT dynamic changes and trigger threshold adjustment. During data reconstruction, a data trend is added to improve reconstruction accuracy. The effectiveness of the proposed method is demonstrated by comparing the proposed method with the basic Kalman filtering algorithm, LMS algorithm, and PIP algorithm on stationary and nonstationary datasets. Compared with not applying the adaptive threshold, on average, there is an 11.7% improvement in accuracy for the same reduction rate or a 17.3% improvement in reduction rate for the same accuracy.https://www.mdpi.com/1424-8220/23/23/9427data reductionInternet of ThingsKalman filteringconcept drift detection |
| spellingShingle | Handuo Zhang Jun Na Bin Zhang Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive Threshold Sensors data reduction Internet of Things Kalman filtering concept drift detection |
| title | Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive Threshold |
| title_full | Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive Threshold |
| title_fullStr | Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive Threshold |
| title_full_unstemmed | Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive Threshold |
| title_short | Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive Threshold |
| title_sort | autonomous internet of things iot data reduction based on adaptive threshold |
| topic | data reduction Internet of Things Kalman filtering concept drift detection |
| url | https://www.mdpi.com/1424-8220/23/23/9427 |
| work_keys_str_mv | AT handuozhang autonomousinternetofthingsiotdatareductionbasedonadaptivethreshold AT junna autonomousinternetofthingsiotdatareductionbasedonadaptivethreshold AT binzhang autonomousinternetofthingsiotdatareductionbasedonadaptivethreshold |