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International Journal of Distributed Sensor Networks
Volume 2012 (2012), Article ID 863545, 16 pages
Research Article

A Very Fast Decision Tree Algorithm for Real-Time Data Mining of Imperfect Data Streams in a Distributed Wireless Sensor Network

1Department of Computer and Information Science, University of Macau, Taipa, Macau
2Department of Electronic Engineering, Beijing University of Technology, Beijing 100022, China
3School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia

Received 6 October 2012; Accepted 22 October 2012

Academic Editor: Sabah Mohammed

Copyright © 2012 Hang Yang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Wireless sensor networks (WSNs) are a rapidly emerging technology with a great potential in many ubiquitous applications. Although these sensors can be inexpensive, they are often relatively unreliable when deployed in harsh environments characterized by a vast amount of noisy and uncertain data, such as urban traffic control, earthquake zones, and battlefields. The data gathered by distributed sensors—which serve as the eyes and ears of the system—are delivered to a decision center or a gateway sensor node that interprets situational information from the data streams. Although many other machine learning techniques have been extensively studied, real-time data mining of high-speed and nonstationary data streams represents one of the most promising WSN solutions. This paper proposes a novel stream mining algorithm with a programmable mechanism for handling missing data. Experimental results from both synthetic and real-life data show that the new model is superior to standard algorithms.