Authors:
Daniel Del Gaudio
;
Amil Imeri
and
Pascal Hirmer
Affiliation:
Institute for Parallel and Distributed Systems, University of Stuttgart, Universitätsstraße 38, Stuttgart, Germany
Keyword(s):
IoT, Robustness, Prediction, Machine Learning.
Abstract:
In the Internet of Things (IoT), interconnected devices communicate through standard Internet protocols to reach common goals. The IoT has reached a wide range of different domains including home automation, health, or manufacturing. With the rising amount of IoT applications, the demand for robustness is increasing as well, which is a difficult issue especially in large IoT applications including hundreds or even thousands of different devices. Devices tend to be very volatile and prone to failures. Usually, IoT devices are comprised of cheap hardware components which enables the creation of larger applications but also leads to an increased amount of failures that endanger operation of the IoT applications. To help in increasing robustness in the IoT, in this paper, we introduce the Failure Prediction Prediction Platform (FPP) for Internet of Things applications, which uses a machine learning based approach to predict failures. We evaluate our platform by showing how different fail
ure prediction algorithms can be integrated and applied.
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