Distributed high-dimensional similarity search approach for large-scale wireless sensor networks
Similarity search in high-dimensional space has become increasingly important in many wireless sensor network applications. However, existing approaches to similarity search is based on the premise that sensed data are centralized to deal with, or sensed data are simple enough to be stored in a rela...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-03-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147717697715 |
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| _version_ | 1832553132903628800 |
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| author | Haifeng Hu Jiefang He Jianshen Wu Kun Wang Wei Zhuang |
| author_facet | Haifeng Hu Jiefang He Jianshen Wu Kun Wang Wei Zhuang |
| author_sort | Haifeng Hu |
| collection | DOAJ |
| description | Similarity search in high-dimensional space has become increasingly important in many wireless sensor network applications. However, existing approaches to similarity search is based on the premise that sensed data are centralized to deal with, or sensed data are simple enough to be stored in a relational database. Different from the previous work, we propose a distributed approximate similarity search algorithm to retrieve similar high-dimensional sensed data for query in wireless sensor networks. First, the sensors are divided into several clusters using the distributed clustering method. Furthermore, the sink transmits the compressed hash code set to the cluster heads. Finally, the estimated similarity score is compared with a specified threshold to filter out irrelevant sensed data. Therefore, the higher search precision and energy efficiency can be achieved. Extensive simulation results show that the proposed algorithms provide significant performance gains in terms of precision and energy efficiency compared with the existing algorithms. |
| format | Article |
| id | doaj-art-7e501dbba692495ca5fffa95e62f8e70 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2017-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-7e501dbba692495ca5fffa95e62f8e702025-02-03T05:55:24ZengWileyInternational Journal of Distributed Sensor Networks1550-14772017-03-011310.1177/1550147717697715Distributed high-dimensional similarity search approach for large-scale wireless sensor networksHaifeng Hu0Jiefang He1Jianshen Wu2Kun Wang3Wei Zhuang4PAPD, CICAEET, Nanjing University of Information Science & Technology, Nanjing, ChinaDepartment of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, ChinaDepartment of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, ChinaDepartment of Telecommunication and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, ChinaPAPD, CICAEET, Nanjing University of Information Science & Technology, Nanjing, ChinaSimilarity search in high-dimensional space has become increasingly important in many wireless sensor network applications. However, existing approaches to similarity search is based on the premise that sensed data are centralized to deal with, or sensed data are simple enough to be stored in a relational database. Different from the previous work, we propose a distributed approximate similarity search algorithm to retrieve similar high-dimensional sensed data for query in wireless sensor networks. First, the sensors are divided into several clusters using the distributed clustering method. Furthermore, the sink transmits the compressed hash code set to the cluster heads. Finally, the estimated similarity score is compared with a specified threshold to filter out irrelevant sensed data. Therefore, the higher search precision and energy efficiency can be achieved. Extensive simulation results show that the proposed algorithms provide significant performance gains in terms of precision and energy efficiency compared with the existing algorithms.https://doi.org/10.1177/1550147717697715 |
| spellingShingle | Haifeng Hu Jiefang He Jianshen Wu Kun Wang Wei Zhuang Distributed high-dimensional similarity search approach for large-scale wireless sensor networks International Journal of Distributed Sensor Networks |
| title | Distributed high-dimensional similarity search approach for large-scale wireless sensor networks |
| title_full | Distributed high-dimensional similarity search approach for large-scale wireless sensor networks |
| title_fullStr | Distributed high-dimensional similarity search approach for large-scale wireless sensor networks |
| title_full_unstemmed | Distributed high-dimensional similarity search approach for large-scale wireless sensor networks |
| title_short | Distributed high-dimensional similarity search approach for large-scale wireless sensor networks |
| title_sort | distributed high dimensional similarity search approach for large scale wireless sensor networks |
| url | https://doi.org/10.1177/1550147717697715 |
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