Distributed Processing of Approximate Range Queries in Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), the large amount of high-dimensional sensed data play a key role in many emerging WSN applications. Existing approaches to similarity search rely on the assumption that sensed data are centralized to deal with, or sensed data are simple enough to be stored in a relational database. In this article, we propose a distributed approximate similarity search (DASS) algorithm to retrieve similar sensed data for range queries by comparing the similarity score of each sensed data with a specified threshold, while taking into account search performance and energy efficiency. The results show that the proposed method provides significant performance gains in terms of precision and energy efficiency compared to existing algorithms.