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Event signature extraction and traffic modeling in WSNs

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Abstract

Motivated by earlier work on adaptive event forecasting, this paper proposes an iterative event signature extraction method for wireless sensor networks, and a probabilistic approach to model non i.i.d. (independent and identically distributed) aperiodic traffic. The model is validated on well described (collection included) real world measurements of various types, and it is then used to demonstrate the effectiveness of the proposed signature extraction method to support reliable event forecasting.

Our scheme can continuously keep the event signature database low on artifacts, dynamically estimate the number of sequences and extract the signatures from noisy, overlapped events.

The proposed solution is inspired by unsupervised competitive Hebbian learning used in self-organizing Kohonen maps. We evaluate the proposed solution analytically, but also empirically, by means of simulations.

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Acknowledgements

The work described in this paper has been (partially) supported by HSNLab, Budapest University of Technology and Economics, http://www.hsnlab.hu.

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  1. Budapest University of Technology and Economics, Magyar tudósok krt. 2, 1117, Budapest, Hungary

    Gergely Öllös & Rolland Vida

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  1. Gergely Öllös
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  2. Rolland Vida
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Correspondence to Gergely Öllös.

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Öllös, G., Vida, R. Event signature extraction and traffic modeling in WSNs. Telecommun Syst 55, 513–523 (2014). https://doi.org/10.1007/s11235-013-9806-y

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