Similarity-aware data aggregation using fuzzy c-means approach for wireless sensor networks
Abstract For resource-constrained IoT systems, data collection is one of the fundamental operations to reduce the energy dissipation of sensor nodes and improve the network lifetime. However, an anomaly or deviation will exert a great influence on the quality of data collected, especially for a data...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2019-03-01
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| Series: | EURASIP Journal on Wireless Communications and Networking |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13638-019-1374-8 |
| _version_ | 1818085700374888448 |
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| author | Runze Wan Naixue Xiong Qinghui Hu Haijun Wang Jun Shang |
| author_facet | Runze Wan Naixue Xiong Qinghui Hu Haijun Wang Jun Shang |
| author_sort | Runze Wan |
| collection | DOAJ |
| description | Abstract For resource-constrained IoT systems, data collection is one of the fundamental operations to reduce the energy dissipation of sensor nodes and improve the network lifetime. However, an anomaly or deviation will exert a great influence on the quality of data collected, especially for a data aggregation scheme. By taking into account data-aware clustering and detection of anomalous events, a similarity-aware data aggregation using a fuzzy c-means approach for wireless sensor networks is proposed. Firstly, by using a fuzzy c-means approach, the clustering process can be performed to organize sensors into clusters based on data similarity. Next, an effective support degree function is defined for further outlier diagnosis. Afterwards, the appropriate weight of valid data can be obtained by taking advantage of the probability distribution characteristics of normal samples within a certain period. Finally, the aggregation result in the cluster can be estimated. Practical database-based simulations have confirmed that the proposed data aggregation method can achieve better performance than traditional methods in terms of data outlier detection accuracy and relative recovery error. |
| first_indexed | 2024-12-10T20:13:57Z |
| format | Article |
| id | doaj.art-6f19eaca568f4690bc42d07e1921f7fa |
| institution | Directory Open Access Journal |
| issn | 1687-1499 |
| language | English |
| last_indexed | 2024-12-10T20:13:57Z |
| publishDate | 2019-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Wireless Communications and Networking |
| spelling | doaj.art-6f19eaca568f4690bc42d07e1921f7fa2022-12-22T01:35:14ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992019-03-012019111110.1186/s13638-019-1374-8Similarity-aware data aggregation using fuzzy c-means approach for wireless sensor networksRunze Wan0Naixue Xiong1Qinghui Hu2Haijun Wang3Jun Shang4Hubei Co-Innovation Center of Information Technology Service for Elementary Education, Hubei University of EducationDepartment of Mathematics and Computer Science, Northeastern State UniversitySchool of Computer Science & Engineering, Guilin University of Aerospace TechnologyHubei Co-Innovation Center of Information Technology Service for Elementary Education, Hubei University of EducationHubei Co-Innovation Center of Information Technology Service for Elementary Education, Hubei University of EducationAbstract For resource-constrained IoT systems, data collection is one of the fundamental operations to reduce the energy dissipation of sensor nodes and improve the network lifetime. However, an anomaly or deviation will exert a great influence on the quality of data collected, especially for a data aggregation scheme. By taking into account data-aware clustering and detection of anomalous events, a similarity-aware data aggregation using a fuzzy c-means approach for wireless sensor networks is proposed. Firstly, by using a fuzzy c-means approach, the clustering process can be performed to organize sensors into clusters based on data similarity. Next, an effective support degree function is defined for further outlier diagnosis. Afterwards, the appropriate weight of valid data can be obtained by taking advantage of the probability distribution characteristics of normal samples within a certain period. Finally, the aggregation result in the cluster can be estimated. Practical database-based simulations have confirmed that the proposed data aggregation method can achieve better performance than traditional methods in terms of data outlier detection accuracy and relative recovery error.http://link.springer.com/article/10.1186/s13638-019-1374-8Fuzzy c-meansData similarityAggregationWireless sensor networks |
| spellingShingle | Runze Wan Naixue Xiong Qinghui Hu Haijun Wang Jun Shang Similarity-aware data aggregation using fuzzy c-means approach for wireless sensor networks EURASIP Journal on Wireless Communications and Networking Fuzzy c-means Data similarity Aggregation Wireless sensor networks |
| title | Similarity-aware data aggregation using fuzzy c-means approach for wireless sensor networks |
| title_full | Similarity-aware data aggregation using fuzzy c-means approach for wireless sensor networks |
| title_fullStr | Similarity-aware data aggregation using fuzzy c-means approach for wireless sensor networks |
| title_full_unstemmed | Similarity-aware data aggregation using fuzzy c-means approach for wireless sensor networks |
| title_short | Similarity-aware data aggregation using fuzzy c-means approach for wireless sensor networks |
| title_sort | similarity aware data aggregation using fuzzy c means approach for wireless sensor networks |
| topic | Fuzzy c-means Data similarity Aggregation Wireless sensor networks |
| url | http://link.springer.com/article/10.1186/s13638-019-1374-8 |
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