Abstract
A Decentralised Adaptive Clustering (DAC) algorithm for self-monitoring impact sensing networks is presented within the context of CSIRO-NASA Ageless Aero-space Vehicle project. DAC algorithm is contrasted with a Fixed-order Centralised Adaptive Clustering (FCAC) algorithm, developed to evaluate the comparative performance. A number of simulation experiments is described, with a focus on the scalability and convergence rate of the clustering algorithm. Results show that DAC algorithm scales well with increasing network and data sizes and is robust to dynamics of the sensor-data flux.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abbott, D., Doyle, B., Dunlop, J., Farmer, T., Hedley, M., Herrmann, J., James, G., Johnson, M., Joshi, B., Poulton, G., Price, D., Prokopenko, M., Reda, T., Rees, D., Scott, A., Valencia, P., Ward, D., Winter, J.: Development and Evaluation of Sensor Concepts for Ageless Aerospace Vehicles. Development of Concepts for an Intelligent Sensing System. NASA technical report NASA/CR-2002-211773, Langley Research Center, Hampton, Virginia (2002)
Ester, M., Kriegel, H., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Second International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)
Foreman, M., Prokopenko, M., Wang, P.: Phase Transitions in Self-organising Sensor Networks. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 781–791. Springer, Heidelberg (2003)
Judd, D., McKinley, P., Jain, A.: Large-Scale Parallel Data Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 871–876 (1998)
Mallows, C.L.: Some comments on Cp. Technometrics 15, 661–675 (1973)
Ogston, E., Overeinder, B., Van Steen, M., Brazier, F.: A Method for Decentralized Clustering in Large Multi-Agent Systems. In: Proceedings of the Second International Joint Conference on Autonomous Agent and Multi Agent Systems, pp. 798–796 (2003)
Ogston, E., Overeinder, B., Van Steen, M., Brazier, F.: Group Formation Among Peerto- Peer Agents: Learning Group Characteristics. In: Moro, G., Sartori, C., Singh, M.P. (eds.) AP2PC 2003. LNCS (LNAI), vol. 2872, pp. 59–70. Springer, Heidelberg (2004)
Price, D., Scott, A., Edwards, G., Batten, A., Farmer, A., Hedley, M., Johnson, M., Lewis, C., Poulton, G., Prokopenko, M., Valencia, P., Wang, P.: An Integrated Health Monitoring System for an Ageless Aerospace Vehicle. In: Proceedings of 4th International Workshop on Structural Health Monitoring, Stanford (September 2003)
Wang, P., Valencia, P., Prokopenko, M., Price, D., Poulton, G.: Self-reconfigurable sensor networks in ageless aerospace vehicles. In: Proceedings of the Eleventh International Conference on Advanced Robotics, Coimbra, pp. 1098–1103 (2003)
Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH:A New Data Clustering Algorithm and Its Applications. Data Mining and Knowledge Discovery 1(2), 141–182 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
rajah, P.M., Prokopenko, M., Wang, P., Price, D. (2005). Towards Adaptive Clustering in Self-monitoring Multi-agent Networks. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552451_109
Download citation
DOI: https://doi.org/10.1007/11552451_109
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28895-4
Online ISBN: 978-3-540-31986-3
eBook Packages: Computer ScienceComputer Science (R0)