Abstract
The similarity search problem has received considerable attention in database research community. In sensor network applications, this problem is even more important due to the imprecision of the sensor hardware, and variation of environmental parameters. Traditional similarity search mechanisms are both improper and inefficient for these highly energy-constrained sensors. A difficulty is that it is hard to predict which sensor has the most similar (or closest) data item such that many or even all sensors need to send their data to the query node for further comparison. In this paper, we propose a similarity search algorithm (SSA), which is a novel framework based on the concept of Hilbert curve over a data-centric storage structure, for efficiently processing similarity search queries in sensor networks. SSA successfully avoids the need of collecting data from all sensors in the network in searching for the most similar data item. The performance study reveals that this mechanism is highly efficient and significantly outperforms previous approaches in processing similarity search queries.
Original language | English |
---|---|
Pages (from-to) | 284-307 |
Number of pages | 24 |
Journal | Information sciences |
Volume | 181 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2011 Jan 15 |
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Software
- Control and Systems Engineering
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence