Event-based k-nearest neighbors query processing over distributed sensory data using fuzzy sets
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K-nearest neighbor (kNN) query is an effective way to extract information of interest from distributed sensing devices. Most of the existing kNN query processing approaches rely on using raw sensor readings, which is costly in terms of communication and time overhead. This paper investigates the event-based kNN query problem in distributed sensor systems and proposes a novel e-kNN query scheme using fuzzy sets. Our key technique is that linguistic e-kNN event information instead of raw sensory data is used for e-kNN information storage and in-networks kNN query processing, which is very beneficial to energy efficiency. In addition, event confidence-based grid storage method and e-kNN query processing algorithm are devised for e-kNN information storage and retrieval, respectively. Experimental results based on real-life data set further show that our e-kNN scheme outperforms the conventional methods in terms of communication cost and response time with accuracy guarantee.
KeywordsK-nearest neighbors (kNN) Event detection Fuzzy sets Energy efficiency Sensor network
We are grateful to the anonymous reviewers and the editor for their constructive suggestions for improving the quality of this paper. This work was funded by the National Natural Science Foundation of China (Nos. 61502421 and 61532021), as well as the Zhejiang Provincial Natural Science Foundation of China (Nos. LY15F020026 and LY15F020025).
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Conflicts of interest
The authors declare that they have no conflict of interest.
Human and animal rights
This article does not contain any studies with human participants or animals performed by any of the authors.
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