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Keywords: wireless sensor network, event detection, pattern recognition, grid computing, event tracking, distributed hierarchical graph neuron.

Summary

HREF="#muhamad:PripuzicBV10">3], in terms of their capability and thus active detection mechanisms are required to provide early warnings for the occurrence of forest fires. With the emergence of lightweight and distributed networks such as wireless sensor networks and mobile ad hoc networks, such active and continuous event detection mechanisms for forest fires can possibly be deployed across a range of geographical areas.

A wireless sensor network (WSN) in particular, offers tremendous potential for technological development. In reality, existing WSN deployment has only been demonstrated for humble applications such as meter readings in buildings and basic forms of ecological monitoring. Reaping the full potential of this technology for real-time detection and monitoring requires a second level of algorithms, which at present is missing. Current techniques solve the more immediate problem of conveying sensory data to a central entity known as the base station. This approach if scaled up for widespread use will cause bottlenecks and cast suspicions of big brother analysis. Hence, there is a need for WSN networks that acquire the abilities to process their sensory data and to generate highly condensed and sophisticated outputs internally. These abilities will alleviate the bottleneck problem, with on-site computations, and address the privacy concern by adopting a completely decentralised approach.

In this paper, we present an on-going study of the implementation of distributed pattern recognition schemes for forest fire detection using WSN. We intend to demonstrate a capability of distributed pattern recognition scheme known as distributed hierarchical graph neuron (DHGN) [4] for event detection. DHGN is a form of neural networks, which adopts clustered and hierarchical graph-based representation of input patterns for use within fully decentralised networks. In this paper we will also demonstrate the capability of DHGNs in performing spatio-temporal recognition using WSN-Grid integrated scheme.

Based upon the results from a series of simulations involving forest fire data, DHGN has demonstrated a significant level of accuracy in detecting occurrences of events, specifically in the area of forest-fire detection.

References

1: J. Fleming, R.G. Robertson, "Fire Management Tech Tips", Technical report, San Dimas CA, USA, 2003.
2: B.n.C. Arrue, A. Ollero, J.R.M. de Dios, "An Intelligent System for False Alarm Reduction in Infrared Forest-Fire Detection", IEEE Intelligent Systems, 15(3), 64-73, 2000. doi:10.1109/5254.846287
3: K. Pripuzic, H. Belani, M. Vukovic, "Early Forest Fire Detection with Sensor Networks: Sliding Window Skylines Approach", Springer, Berlin/Heidelberg, 2010. doi:10.1007/978-3-540-85563-7_91
4: A.I. Khan, A. Muhamad Amin, "One Shot Associative Memory Method for Distorted Pattern Recognition", in "AI 2007: Advances in Artificial Intelligence", Springer, Berlin/Heidelberg, 705-709, 2007. doi:10.1007/978-3-540-76928-6_79

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Front Page Browse CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Proceedings ISSN 1759-3433 CCP: 95 PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, GRID AND CLOUD COMPUTING FOR ENGINEERING Edited by: Paper 50 Spatio-Temporal Forest Fire Detection using a Distributed Hierarchical Graph Neuron within an Integrated Wireless Sensor Network-Grid Environment A.H. Muhamad Amin¹ and A.I. Khan² ¹Computer & Information Sciences Department, Universiti Teknologi PETRONAS, Perak Darul Ridzuan, Malaysia ²Clayton School of Information Technology, Monash University, Clayton, Australia doi:10.4203/ccp.95.50 purchase the full-text of this paper Full Bibliographic Reference for this paper A.H. Muhamad Amin, A.I. Khan, "Spatio-Temporal Forest Fire Detection using a Distributed Hierarchical Graph Neuron within an Integrated Wireless Sensor Network-Grid Environment", in , (Editors), "Proceedings of the Second International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 50, 2011. doi:10.4203/ccp.95.50 Keywords: wireless sensor network, event detection, pattern recognition, grid computing, event tracking, distributed hierarchical graph neuron. Summary HREF="#muhamad:PripuzicBV10">3], in terms of their capability and thus active detection mechanisms are required to provide early warnings for the occurrence of forest fires. With the emergence of lightweight and distributed networks such as wireless sensor networks and mobile ad hoc networks, such active and continuous event detection mechanisms for forest fires can possibly be deployed across a range of geographical areas. A wireless sensor network (WSN) in particular, offers tremendous potential for technological development. In reality, existing WSN deployment has only been demonstrated for humble applications such as meter readings in buildings and basic forms of ecological monitoring. Reaping the full potential of this technology for real-time detection and monitoring requires a second level of algorithms, which at present is missing. Current techniques solve the more immediate problem of conveying sensory data to a central entity known as the base station. This approach if scaled up for widespread use will cause bottlenecks and cast suspicions of big brother analysis. Hence, there is a need for WSN networks that acquire the abilities to process their sensory data and to generate highly condensed and sophisticated outputs internally. These abilities will alleviate the bottleneck problem, with on-site computations, and address the privacy concern by adopting a completely decentralised approach. In this paper, we present an on-going study of the implementation of distributed pattern recognition schemes for forest fire detection using WSN. We intend to demonstrate a capability of distributed pattern recognition scheme known as distributed hierarchical graph neuron (DHGN) [4] for event detection. DHGN is a form of neural networks, which adopts clustered and hierarchical graph-based representation of input patterns for use within fully decentralised networks. In this paper we will also demonstrate the capability of DHGNs in performing spatio-temporal recognition using WSN-Grid integrated scheme. Based upon the results from a series of simulations involving forest fire data, DHGN has demonstrated a significant level of accuracy in detecting occurrences of events, specifically in the area of forest-fire detection. References 1 J. Fleming, R.G. Robertson, "Fire Management Tech Tips", Technical report, San Dimas CA, USA, 2003. 2 B.n.C. Arrue, A. Ollero, J.R.M. de Dios, "An Intelligent System for False Alarm Reduction in Infrared Forest-Fire Detection", IEEE Intelligent Systems, 15(3), 64-73, 2000. doi:10.1109/5254.846287 3 K. Pripuzic, H. Belani, M. Vukovic, "Early Forest Fire Detection with Sensor Networks: Sliding Window Skylines Approach", Springer, Berlin/Heidelberg, 2010. doi:10.1007/978-3-540-85563-7_91 4 A.I. Khan, A. Muhamad Amin, "One Shot Associative Memory Method for Distorted Pattern Recognition", in "AI 2007: Advances in Artificial Intelligence", Springer, Berlin/Heidelberg, 705-709, 2007. doi:10.1007/978-3-540-76928-6_79 purchase the full-text of this paper (price £20) go to the previous paper go to the next paper return to the table of contents return to the book description purchase this book (price £85 +P&P)
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