期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2022
卷号：18
期号：5
页码：1-18
DOI：10.1177/15501477211049307
语种：English
出版社：Hindawi Publishing Corporation
摘要：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.
