Abstract
Fog computing architectures provide a new platform for distributed stream processing in Internet of Things (IoT) applications. In a hierarchical infrastructure, processing of stream data arriving from sensors starts at the lowest level edge nodes and proceeds to the intermediate level fog nodes and eventually to the top-level cloud node. The goal is to do as much processing as possible at the lower level nodes and react to unexpected or interesting input values as early as possible. The unexpected values are referred to as anomalies. They may occur due to malfunctioning of sensors, which may be due to accidents or intentional attacks, or changes in the environment. The anomalies must be detected and proper actions must be taken quickly to bring the application to a steady state. We describe a generic framework for detection of data anomalies in a fog hierarchy in this paper. Our framework can be adapted to any application and other fog architectures.
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Acknowledgements
This work is supported in part by an NSERC (Natural Sciences and Engineering Research Council of Canada) Discovery Grant 3182.
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Vidyasankar, K. (2020). Detection of Data Anomalies in Fog Computing Architectures. In: Zhang, YD., Mandal, J., So-In, C., Thakur, N. (eds) Smart Trends in Computing and Communications. Smart Innovation, Systems and Technologies, vol 165. Springer, Singapore. https://doi.org/10.1007/978-981-15-0077-0_15
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DOI: https://doi.org/10.1007/978-981-15-0077-0_15
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