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Computational Science, Engineering & Technology Series
ISSN 1759-3158
Edited by: B.H.V. Topping, P. Iványi
Chapter 18

Integrating Sensory Data within a Structural Analysis Grid

A.I. Khan and A.H. Muhamad Amin

Clayton School of Information Technology, Monash University, Clayton, Australia

Full Bibliographic Reference for this chapter
A.I. Khan, A.H. Muhamad Amin, "Integrating Sensory Data within a Structural Analysis Grid", in B.H.V. Topping, P. Iványi, (Editors), "Parallel, Distributed and Grid Computing for Engineering", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 18, pp 389-412, 2009. doi:10.4203/csets.21.18
Keywords: structural health monitoring, wireless sensor network, pattern recognition, computational grid, structural engineering.

Highly-invested and sophisticated structures such as aerospace vehicles, offshore installations, maritime vessels, and critical infrastructure frequently require up-to-date information regarding their structural status. Maintenance costs for such structures are usually high and involve major technical operations to be conducted. In this paper, we propose a novel holistic integrated grid-sensor network framework for structural engineering lifecycle that incorporates an end-to-end structural analysis, design, and monitoring. This framework occupies the integration between wireless sensor network (WSN) technology and computational grid for rapid structural data acquisition and processing.

In this framework, the sensory data collected from real-time monitoring of structures can be used for less conservative analysis and design, while the information generated during analysis can be used towards real time interpretation of sensory data. The integrated grid-sensor network framework is a combination of commodity-grid based system, for large-scale structural data analysis, and sensor networks. The proposed framework utilises an associative memory algorithm known as Graph Neuron (GN) [1-5], which is an in-network processing scheme for resource constrained sensor networks, for real time monitoring. The grid incorporates a hierarchical version of the Graph Neuron known as Distributed Hierarchical Graph Neuron (DHGN) [6-9] to rapidly correlate sensory patterns of interest with all available analytical data. Within this framework, data generated through the analysis would flow through the grid and into the sensor network, thereby allowing intelligent filtering of sensory outputs in real time. The filtered event pattern data from this process would then flow into the grid, where the grid-enabled pattern recognising scheme would check for repeated and critical patterns by comparing the sensory outputs with the available analytical data. Many of the iterative processes within the analysis, design, and monitoring stages can be automated by defining work flows within the framework. The resultant grid-sensor network framework would enable the use of sensory data within structural design and it would also improve sensory data interpretation.

A.I. Khan, "A Peer-to-Peer Associative Memory Network for Intelligent Information Systems", presented at The Proceedings of The Thirteenth Australasian Conference on Information Systems, Melbourne, Australia, 2002.
Z.A. Baig, M. Baqer, A.I. Khan, "A Graph Neuron-based Distributed Denial of Service (DDoS) Attack Recognition Scheme for Wireless Sensor Networks", presented at International Conference of Pattern Recognition (IPCR'06), Hong Kong, 2006.
M. Baqer, A.I. Khan, Z.A. Baig, "Implementing a graph neuron array for pattern recognition within unstructured wireless sensor networks", presented at Proceedings of EUC Workshops, 2005.
A.I. Khan, M. Isreb, R.S. Spindler, "A Parallel Distributed Application of the Wireless Sensor Network", presented at Proceedings of the Seventh International Conference on Computing and Grid in Asia Pacific Region, Tokyo, Japan, 2004.
A.I. Khan, P. Mihailescu, "Parallel Pattern Recognition Computations within a Wireless Sensor Network", presented at Proceedings of the 17th International Conference on Pattern Recognition (ICPR'04), Cambridge, United Kingdom, 2004.
A.I. Khan, A.H. Muhamad Amin, "One Shot Associative Memory Method for Distorted Pattern Recognition", in AI 2007: Advances in Artificial Intelligence, 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Australia, December 2-6, 2007, Proceedings, vol. 4830, M.A. Orgun, J. Thornton, (Editors), Springer, 705-709, 2007.
A.I. Khan, A.H. Muhamad Amin, "An On-line Scheme for Threat Detection Within Mobile Ad Hoc Networks", in Mobile Intelligence: Mobile Computing and Computational Intelligence, L.T. Yang, A.B. Waluyo, J. Ma, L. Tan, B. Srinivasan, (Editors), John Wiley & Sons, 2008.
A.H. Muhamad Amin, A.I. Khan, "Commodity-Grid Based Distributed Pattern Recognition Framework", presented at Sixth Australasian Symposium on Grid Computing and e-Research (AusGrid 2008), Wollongong, NSW, Australia, 2008.
A.H. Muhamad Amin, R.A. Raja Mahmood, A.I. Khan, "Analysis of Pattern Recognition Algorithms Using Associative Memory Approach: A Comparative Study between the Hopfield Network and Distributed Hierarchical Graph Neuron (DHGN)", presented at Computer and Information Technology Workshops, 2008. CIT Workshops 2008. IEEE 8th International Conference on, 2008.

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