A graph-based sensor recommendation model in semantic sensor network
In the past few years, introducing ontology to describe the concepts and relationships between different entities in semantic sensor network enhances the interoperability between entities. Existing works mostly based on SPARQL retrieval ignore the user’s specific requirements of sensor attributes. T...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-05-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501477211049307 |
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| _version_ | 1850162494566301696 |
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| author | Yuanyi Chen Yihao Lin Peng Yu Yanyun Tao Zengwei Zheng |
| author_facet | Yuanyi Chen Yihao Lin Peng Yu Yanyun Tao Zengwei Zheng |
| author_sort | Yuanyi Chen |
| collection | DOAJ |
| description | In the past few years, introducing ontology to describe the concepts and relationships between different entities in semantic sensor network enhances the interoperability between entities. Existing works mostly based on SPARQL retrieval ignore the user’s specific requirements of sensor attributes. Therefore, the recommendation results cannot satisfy the user’s needs. In this article, we propose a graph-based sensor recommendation model. The model mainly includes two parts: (1) Filtering nodes in data graph. In addition to using the traditional graph matching algorithm, we propose a threshold pruning algorithm to narrow the matching scope and improve the matching efficiency. (2) Recommending top- k sensors. We use the improved fast non-dominated sorting algorithm to obtain the local optimal solutions of sensor data set, and we apply the simple additive weight algorithm to characterize and sort local optional solutions. Finally, we recommend the top- k sensors to the user. By comparison, the graph-based sensor recommendation algorithm meets user’s needs more than other algorithms, and experiments show that our model outperforms several baselines in terms of both response time and precision. |
| format | Article |
| id | doaj-art-fe0890706cbb45bb910a54ef98a1b3e2 |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2022-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-fe0890706cbb45bb910a54ef98a1b3e22025-08-20T02:22:33ZengWileyInternational Journal of Distributed Sensor Networks1550-14772022-05-011810.1177/15501477211049307A graph-based sensor recommendation model in semantic sensor networkYuanyi Chen0Yihao Lin1Peng Yu2Yanyun Tao3Zengwei Zheng4Hangzhou Key Laboratory for IoT Technology and Application, Zhejiang University City College, Hangzhou, ChinaDepartment of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaDepartment of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaDepartment of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaHangzhou Key Laboratory for IoT Technology and Application, Zhejiang University City College, Hangzhou, ChinaIn the past few years, introducing ontology to describe the concepts and relationships between different entities in semantic sensor network enhances the interoperability between entities. Existing works mostly based on SPARQL retrieval ignore the user’s specific requirements of sensor attributes. Therefore, the recommendation results cannot satisfy the user’s needs. In this article, we propose a graph-based sensor recommendation model. The model mainly includes two parts: (1) Filtering nodes in data graph. In addition to using the traditional graph matching algorithm, we propose a threshold pruning algorithm to narrow the matching scope and improve the matching efficiency. (2) Recommending top- k sensors. We use the improved fast non-dominated sorting algorithm to obtain the local optimal solutions of sensor data set, and we apply the simple additive weight algorithm to characterize and sort local optional solutions. Finally, we recommend the top- k sensors to the user. By comparison, the graph-based sensor recommendation algorithm meets user’s needs more than other algorithms, and experiments show that our model outperforms several baselines in terms of both response time and precision.https://doi.org/10.1177/15501477211049307 |
| spellingShingle | Yuanyi Chen Yihao Lin Peng Yu Yanyun Tao Zengwei Zheng A graph-based sensor recommendation model in semantic sensor network International Journal of Distributed Sensor Networks |
| title | A graph-based sensor recommendation model in semantic sensor network |
| title_full | A graph-based sensor recommendation model in semantic sensor network |
| title_fullStr | A graph-based sensor recommendation model in semantic sensor network |
| title_full_unstemmed | A graph-based sensor recommendation model in semantic sensor network |
| title_short | A graph-based sensor recommendation model in semantic sensor network |
| title_sort | graph based sensor recommendation model in semantic sensor network |
| url | https://doi.org/10.1177/15501477211049307 |
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