Data Fusion Using Improved Support Degree Function in Aquaculture Wireless Sensor Networks
For monitoring the aquaculture parameters in pond with wireless sensor networks (WSN), high accuracy of fault detection and high precision of error correction are essential. However, collecting accurate data from WSN to server or cloud is a bottleneck because of the data faults of WSN, especially in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/18/11/3851 |
| _version_ | 1828112710840090624 |
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| author | Pei Shi Guanghui Li Yongming Yuan Liang Kuang |
| author_facet | Pei Shi Guanghui Li Yongming Yuan Liang Kuang |
| author_sort | Pei Shi |
| collection | DOAJ |
| description | For monitoring the aquaculture parameters in pond with wireless sensor networks (WSN), high accuracy of fault detection and high precision of error correction are essential. However, collecting accurate data from WSN to server or cloud is a bottleneck because of the data faults of WSN, especially in aquaculture applications, limits their further development. When the data fault occurs, data fusion mechanism can help to obtain corrected data to replace abnormal one. In this paper, we propose a data fusion method using a novel function that is Dynamic Time Warping time series strategy improved support degree (DTWS-ISD) for enhancing data quality, which employs a Dynamic Time Warping (DTW) time series segmentation strategy to the improved support degree (ISD) function. We use the DTW distance to replace Euclidean distance, which can explore the continuity and fuzziness of data streams, and the time series segmentation strategy is adopted to reduce the computation dimension of DTW algorithm. Unlike Gauss support function, ISD function obtains mutual support degree of sensors without the exponent calculation. Several experiments were finished to evaluate the accuracy and efficiency of DTWS-ISD with different performance metrics. The experimental results demonstrated that DTWS-ISD achieved better fusion precision than three existing functions in a real-world WSN water quality monitoring application. |
| first_indexed | 2024-04-11T11:53:40Z |
| format | Article |
| id | doaj.art-f4b4fd06ab784fe0ab87ae05886e63cb |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T11:53:40Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f4b4fd06ab784fe0ab87ae05886e63cb2022-12-22T04:25:14ZengMDPI AGSensors1424-82202018-11-011811385110.3390/s18113851s18113851Data Fusion Using Improved Support Degree Function in Aquaculture Wireless Sensor NetworksPei Shi0Guanghui Li1Yongming Yuan2Liang Kuang3School of IoT Engineering, Jiangnan University, Wuxi 214122, ChinaSchool of IoT Engineering, Jiangnan University, Wuxi 214122, ChinaFreshwater Fisheries Research Center, Chinese Academy of Fishery Sciences, Wuxi 214081, ChinaSchool of IoT Engineering, Jiangsu Vocational College of Information Technology, Wuxi 214153, ChinaFor monitoring the aquaculture parameters in pond with wireless sensor networks (WSN), high accuracy of fault detection and high precision of error correction are essential. However, collecting accurate data from WSN to server or cloud is a bottleneck because of the data faults of WSN, especially in aquaculture applications, limits their further development. When the data fault occurs, data fusion mechanism can help to obtain corrected data to replace abnormal one. In this paper, we propose a data fusion method using a novel function that is Dynamic Time Warping time series strategy improved support degree (DTWS-ISD) for enhancing data quality, which employs a Dynamic Time Warping (DTW) time series segmentation strategy to the improved support degree (ISD) function. We use the DTW distance to replace Euclidean distance, which can explore the continuity and fuzziness of data streams, and the time series segmentation strategy is adopted to reduce the computation dimension of DTW algorithm. Unlike Gauss support function, ISD function obtains mutual support degree of sensors without the exponent calculation. Several experiments were finished to evaluate the accuracy and efficiency of DTWS-ISD with different performance metrics. The experimental results demonstrated that DTWS-ISD achieved better fusion precision than three existing functions in a real-world WSN water quality monitoring application.https://www.mdpi.com/1424-8220/18/11/3851wireless sensor networksdata fusionsupport degree functiondynamic time warpingsensor-cloudwater quality monitoring |
| spellingShingle | Pei Shi Guanghui Li Yongming Yuan Liang Kuang Data Fusion Using Improved Support Degree Function in Aquaculture Wireless Sensor Networks Sensors wireless sensor networks data fusion support degree function dynamic time warping sensor-cloud water quality monitoring |
| title | Data Fusion Using Improved Support Degree Function in Aquaculture Wireless Sensor Networks |
| title_full | Data Fusion Using Improved Support Degree Function in Aquaculture Wireless Sensor Networks |
| title_fullStr | Data Fusion Using Improved Support Degree Function in Aquaculture Wireless Sensor Networks |
| title_full_unstemmed | Data Fusion Using Improved Support Degree Function in Aquaculture Wireless Sensor Networks |
| title_short | Data Fusion Using Improved Support Degree Function in Aquaculture Wireless Sensor Networks |
| title_sort | data fusion using improved support degree function in aquaculture wireless sensor networks |
| topic | wireless sensor networks data fusion support degree function dynamic time warping sensor-cloud water quality monitoring |
| url | https://www.mdpi.com/1424-8220/18/11/3851 |
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