Abstract
We present the design of a Wireless Sensor Networks (WSN) level event prediction framework to monitor the network and its operational environment to support proactive self* actions. For example, by monitoring and subsequently predicting trends on network load or sensor nodes energy levels, the WSN can proactively initiate self-reconfiguration. We propose a Map based Predictive Monitoring (MPM) approach where a selected WSN attribute is first profiled as WSN maps, and based on the maps history, predicts future maps using time series modeling. The ”attribute” maps are created using a gridding technique and predicted maps are used to detect events using our regioning algorithm. The proposed approach is also a general framework to cover multiple application domains. For proof of concept, we show MPM’s enhanced ability to also accurately ”predict” the network partitioning, accommodating parameters such as shape and location of the partition with a very high accuracy and efficiency.
Research supported in part by HEC, MUET, EC INSPIRE, EC CoMiFiN, and DFG GRK 1362 (TUD GKMM).
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
Yick, J., et al.: Wireless sensor network survey. Computer Networks 52(12), 2292–2330 (2008)
Yu, L., et al.: Real-time forest fire detection with wireless sensor networks. In: WCNM, vol. 2, pp. 1214–1217 (2005)
Shrivastava, N., et al.: Detecting cuts in sensor networks. In: IPSN, p. 28 (2005)
Rost, S., Balakrishnan, H.: Memento: A Health Monitoring System for Wireless Sensor Networks. In: IEEE SECON, pp. 575–584 (2006)
Shih, K.P., et al.: PALM: A Partition Avoidance Lazy Movement Protocol for Mobile Sensor Networks. In: Proceedings of the IEEE WCNC, pp. 2484–2489 (2007)
Wang, X., et al.: Contour map matching for event detection in sensor networks. In: SIGMOD, pp. 145–156 (2006)
Achir, M., Ouvry, L.: Power consumption prediction in wireless sensor networks. In: 16th ITCS (2004)
Tulone, D., Madden, S.: PAQ: Time series forecasting for approximate query answering in sensor networks. In: Römer, K., Karl, H., Mattern, F. (eds.) EWSN 2006. LNCS, vol. 3868, pp. 21–37. Springer, Heidelberg (2006)
Zhao, J., et al.: Residual energy scan for monitoring sensor networks. In: WCNC, pp. 356–362 (2002)
Banerjee, T., et al.: Fault tolerant multiple event detection in a wireless sensor network. Journal of Parallel and Distributed Computing 68(9), 1222–1234 (2008)
Landsiedel, O., et al.: Accurate prediction of power consumption in sensor networks. In: EmNets, pp. 37–44 (2005)
Mini, A.F., et al.: A probabilistic approach to predict the energy consumption in wireless sensor networks. In: IV Workshop de Comunicao sem Fio e Computao Mvel, So Paulo, pp. 23–25 (2002)
Wang, X., et al.: Robust forecasting for energy efficiency of wireless multimedia sensor networks. Sensors 7(11), 2779–2807 (2007)
Khelil, A., et al.: MWM: A map-based world model for event-driven wireless sensor networks. Autonomics, 1–10 (2008)
He, T., et al.: Range-free localization and its impact on large scale sensor networks. Transaction on Embedded Computing Systems 4(4), 877–906 (2005)
Aurenhammer, F.: Voronoi diagrams - a survey of a fundamental geometric data structure. ACM Computing Surveys 23(3), 345–405 (1991)
Montgomery, D.C., et al.: Introduction to Time Series Analysis and Forecasting. John Wiley and Sons, New Jersey (2008)
Ljung, L.: System Identification: Theory for the User, 2nd edn. Prentice-Hall, New Jersey (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Ali, A., Khelil, A., Shaikh, F.K., Suri, N. (2010). MPM: Map Based Predictive Monitoring for Wireless Sensor Networks. In: Vasilakos, A.V., Beraldi, R., Friedman, R., Mamei, M. (eds) Autonomic Computing and Communications Systems. AUTONOMICS 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 23. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11482-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-11482-3_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11481-6
Online ISBN: 978-3-642-11482-3
eBook Packages: Computer ScienceComputer Science (R0)