Abstract
| Original language | Undefined |
|---|---|
| Pages (from-to) | 58-69 |
| Number of pages | 12 |
| Journal | International Journal of Space-Based and Situated Computing |
| Volume | 2 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Feb 2012 |
Keywords
- situated computing
- EWI-21739
- Event Detection
- IR-80433
- Wireless Sensor Networks
- WSNs
- METIS-296048
- Disaster early warning systems
Cite this
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Use of wireless sensor networks for distributed event detection in disaster management applications. / Bahrepour, M.; Meratnia, Nirvana; Poel, Mannes; Taghikhaki, Zahra; Havinga, Paul J.M.
In: International Journal of Space-Based and Situated Computing, Vol. 2, No. 1, 02.2012, p. 58-69.Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Use of wireless sensor networks for distributed event detection in disaster management applications
AU - Bahrepour, M.
AU - Meratnia, Nirvana
AU - Poel, Mannes
AU - Taghikhaki, Zahra
AU - Havinga, Paul J.M.
N1 - eemcs-eprint-21739
PY - 2012/2
Y1 - 2012/2
N2 - Recently, wireless sensor networks (WSNs) have become mature enough to go beyond being simple fine-grained continuous monitoring platforms and have become one of the enabling technologies for early-warning disaster 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.
AB - Recently, wireless sensor networks (WSNs) have become mature enough to go beyond being simple fine-grained continuous monitoring platforms and have become one of the enabling technologies for early-warning disaster 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.
KW - situated computing
KW - EWI-21739
KW - Event Detection
KW - IR-80433
KW - Wireless Sensor Networks
KW - WSNs
KW - METIS-296048
KW - Disaster early warning systems
U2 - 10.1504/IJSSC.2012.045569
DO - 10.1504/IJSSC.2012.045569
M3 - Article
VL - 2
SP - 58
EP - 69
JO - International Journal of Space-Based and Situated Computing
JF - International Journal of Space-Based and Situated Computing
SN - 2044-4893
IS - 1
ER -