Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15234
Title: A statistics-based sensor selection scheme for continuous probabilistic queries in sensor networks
Authors: Han, S
Chan, E
Cheng, R
Lam, KY
Keywords: Continuous probabilistic queries
Data uncertainty
Sensor networks
Sensor selection
Issue Date: 2007
Publisher: Springer
Source: Real-time systems, 2007, v. 35, no. 1, p. 33-58 How to cite?
Journal: Real-Time Systems 
Abstract: A common approach to improve the reliability of query results based on error-prone sensors is to introduce redundant sensors. However, using multiple sensors to generate the value for a data item can be expensive, especially in wireless environments where continuous queries are executed. Moreover, some sensors may not be working properly and their readings need to be discarded. In this paper, we propose a statistical approach to decide which sensor nodes to be used to answer a query. In particular, we propose to solve the problem with the aid of continuous probabilistic query (CPQ), which is originally used to manage uncertain data and is associated with a probabilistic guarantee on the query result. Based on the historical data values from the sensor nodes, the query type, and the requirement on the query, we present methods to select an appropriate set of sensors and provide reliable answers for several common aggregate queries. Our statistics-based sensor node selection algorithm is demonstrated in a number of simulation experiments, which shows that a small number of sensor nodes can provide accurate and robust query results.
URI: http://hdl.handle.net/10397/15234
DOI: 10.1007/s11241-006-9000-3
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

9
Last Week
0
Last month
0
Citations as of Aug 21, 2017

WEB OF SCIENCETM
Citations

10
Last Week
0
Last month
Citations as of Aug 4, 2017

Page view(s)

30
Last Week
0
Last month
Checked on Aug 20, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.