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).
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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
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DOI: https://doi.org/10.1007/978-3-642-11482-3_6
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