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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Bahrepour, M. Meratnia, N. Poel, M. Taghikhaki, Z. Havinga, P.J.M. |
| Copyright Year | 2010 |
| Abstract | Recently, wireless sensor networks (WSNs) have become mature enough to go beyond being simple fine-grained continuous monitoring platforms and become one of the enabling technologies for disaster early-warning systems. Event detection functionality of WSNs can be of great help and importance for (near) real-time detection of, for example, meteorological natural hazards and wild and residential fires. From the data-mining perspective, many real world events exhibit specific patterns, which can be detected by applying machine learning (ML) techniques. In this paper, we introduce ML techniques for distributed event detection in WSNs and evaluate their performance and applicability for early detection of disasters, specifically residential fires. To this end, we present a distributed event detection approach incorporating a novel reputation-based voting and the decision tree and evaluate its performance in terms of detection accuracy and time complexity. |
| Starting Page | 507 |
| Ending Page | 512 |
| File Size | 536130 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424488285 |
| DOI | 10.1109/INCOS.2010.24 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-11-24 |
| Publisher Place | Greece |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Wireless sensor networks Event detection Fires Complexity theory Classification tree analysis Disaster early warning systems |
| Content Type | Text |
| Resource Type | Article |
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