Uncertain Data Clustering-Based Distance Estimation in Wireless Sensor Networks
For communication distance estimations in Wireless Sensor Networks (WSNs), the RSSI (Received Signal Strength Indicator) value is usually assumed to have a linear relationship with the logarithm of the communication distance. However, this is not always true in reality because there are always uncer...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2014-04-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/14/4/6584 |
| _version_ | 1828138737170644992 |
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| author | Qinghua Luo Yu Peng Xiyuan Peng Abdulmotaleb El Saddik |
| author_facet | Qinghua Luo Yu Peng Xiyuan Peng Abdulmotaleb El Saddik |
| author_sort | Qinghua Luo |
| collection | DOAJ |
| description | For communication distance estimations in Wireless Sensor Networks (WSNs), the RSSI (Received Signal Strength Indicator) value is usually assumed to have a linear relationship with the logarithm of the communication distance. However, this is not always true in reality because there are always uncertainties in RSSI readings due to obstacles, wireless interferences, etc. In this paper, we specifically propose a novel RSSI-based communication distance estimation method based on the idea of interval data clustering. We first use interval data, combined with statistical information of RSSI values, to interpret the distribution characteristics of RSSI. We then use interval data hard clustering and soft clustering to overcome different levels of RSSI uncertainties, respectively. We have used real RSSI measurements to evaluate our communication distance estimation method in three representative wireless environments. Extensive experimental results show that our communication distance estimation method can effectively achieve promising estimation accuracy with high efficiency when compared to other state-of-art approaches. |
| first_indexed | 2024-04-11T18:41:09Z |
| format | Article |
| id | doaj.art-c43bf023ab0f4c9481f62946757cb119 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T18:41:09Z |
| publishDate | 2014-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c43bf023ab0f4c9481f62946757cb1192022-12-22T04:08:58ZengMDPI AGSensors1424-82202014-04-011446584660510.3390/s140406584s140406584Uncertain Data Clustering-Based Distance Estimation in Wireless Sensor NetworksQinghua Luo0Yu Peng1Xiyuan Peng2Abdulmotaleb El Saddik3School of Information and Electrical Engineering, Harbin Institute of Technology at WeiHai, No.2 WenHua west road, Weihai 264209, ChinaAutomatic Test and Control Institute, Harbin Institute of Technology, Harbin 150080, ChinaAutomatic Test and Control Institute, Harbin Institute of Technology, Harbin 150080, ChinaMultimedia Communications Research Laboratory (MCRLab), University of Ottawa, Ottawa, ON K1N 6N5, CanadaFor communication distance estimations in Wireless Sensor Networks (WSNs), the RSSI (Received Signal Strength Indicator) value is usually assumed to have a linear relationship with the logarithm of the communication distance. However, this is not always true in reality because there are always uncertainties in RSSI readings due to obstacles, wireless interferences, etc. In this paper, we specifically propose a novel RSSI-based communication distance estimation method based on the idea of interval data clustering. We first use interval data, combined with statistical information of RSSI values, to interpret the distribution characteristics of RSSI. We then use interval data hard clustering and soft clustering to overcome different levels of RSSI uncertainties, respectively. We have used real RSSI measurements to evaluate our communication distance estimation method in three representative wireless environments. Extensive experimental results show that our communication distance estimation method can effectively achieve promising estimation accuracy with high efficiency when compared to other state-of-art approaches.http://www.mdpi.com/1424-8220/14/4/6584wireless sensor networkdistance estimationRSSIuncertain datadata clustering algorithm |
| spellingShingle | Qinghua Luo Yu Peng Xiyuan Peng Abdulmotaleb El Saddik Uncertain Data Clustering-Based Distance Estimation in Wireless Sensor Networks Sensors wireless sensor network distance estimation RSSI uncertain data data clustering algorithm |
| title | Uncertain Data Clustering-Based Distance Estimation in Wireless Sensor Networks |
| title_full | Uncertain Data Clustering-Based Distance Estimation in Wireless Sensor Networks |
| title_fullStr | Uncertain Data Clustering-Based Distance Estimation in Wireless Sensor Networks |
| title_full_unstemmed | Uncertain Data Clustering-Based Distance Estimation in Wireless Sensor Networks |
| title_short | Uncertain Data Clustering-Based Distance Estimation in Wireless Sensor Networks |
| title_sort | uncertain data clustering based distance estimation in wireless sensor networks |
| topic | wireless sensor network distance estimation RSSI uncertain data data clustering algorithm |
| url | http://www.mdpi.com/1424-8220/14/4/6584 |
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