A statistics-based sensor selection scheme for continuous probabilistic queries in sensor networks

  • Authors:

  • Song Han;Edward Chan;Reynold Cheng;Kam-Yiu Lam

  • Affiliations:

  • Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong;Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong;Department of Computing, Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong;Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong

  • Venue:
  • Real-Time Systems
  • Year:
  • 2007

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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.