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.
Highlights
With the rapid development of the Internet, wireless networks, and sensor technologies, there is an emerging attention in leveraging massive amounts of data available in distributed data source such as wireless sensor networks (WSNs)
The sensors are divided into several clusters using the distributed clustering method, the sink transmits the compressed hash code set to the cluster head, and the similarity score is estimated in a distributed manner
(3) We propose an effective method to calculate the similarity score to reflect the degree of similarity between the sensed data with the query
Summary
With the rapid development of the Internet, wireless networks, and sensor technologies, there is an emerging attention in leveraging massive amounts of data available in distributed data source such as wireless sensor networks (WSNs). Considering the distributed nature of WSNs, how to acquire feasible solutions to the highdimensional similarity search in large-scale WSNs is still an open problem To this end, we propose a distributed similarity search approach for retrieving the similar highdimensional sensed data in WSNs. The contributions of this work are summarized as follows:. (1) We propose a distributed LSH-based model via computing the similarity score in the cluster head of WSN, instead of probing hash codes in multiple centralized hash tables. (2) We propose a distributed approximate similarity search (DASS) algorithm to retrieve the similar high-dimensional sensed data for query in WSNs. More explicitly, the sensors are divided into several clusters using the distributed clustering method, the sink transmits the compressed hash code set to the cluster head, and the similarity score is estimated in a distributed manner. The details of our algorithms are presented in section ‘‘DASS approach.’’ Performance evaluation results are given in section ‘‘Simulation.’’ Section ‘‘Conclusion’’ concludes the article
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