Distributed high-dimensional similarity search approach for large-scale wireless sensor networks

Haifeng Hu, Jiefang He, Jianshen Wu, Kun Wang and Wei Zhuang

International Journal of Distributed Sensor Networks, 2017, vol. 13, issue 3, 1550147717697715

Abstract: 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.

Keywords: Wireless sensor networks; distributed approximate similarity search; locality sensitive hashing (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:13:y:2017:i:3:p:1550147717697715

DOI: 10.1177/1550147717697715

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