Clustering algorithms are efficient tools for discov-ering correlations or affinities within large datasets and are the basis of several Machine Learning processes based on data generated by sensor networks. Recently, such algorithms have found an active application area closely correlated to the Edge Computing paradigm. The final aim is to transfer intelligence and decision-making ability near the edge of the networks to detect or prevent, as an example, attacks from insecure domains. In such a context, the present work introduces a new hybrid clustering algorithm for Edge Computing environments that can classify edge nodes taking into account their reliability. The algorithm is later evaluated from the points of view of the performance and energy consumption, comparing it with two high-end G PU-based computing systems. The achieved results confirm the possibility of designing intelligent sensors networks where decisions are taken at the data collection points.

Toward a high-performance clustering algorithm for securing edge computing environments

Montella R.
2022

Abstract

Clustering algorithms are efficient tools for discov-ering correlations or affinities within large datasets and are the basis of several Machine Learning processes based on data generated by sensor networks. Recently, such algorithms have found an active application area closely correlated to the Edge Computing paradigm. The final aim is to transfer intelligence and decision-making ability near the edge of the networks to detect or prevent, as an example, attacks from insecure domains. In such a context, the present work introduces a new hybrid clustering algorithm for Edge Computing environments that can classify edge nodes taking into account their reliability. The algorithm is later evaluated from the points of view of the performance and energy consumption, comparing it with two high-end G PU-based computing systems. The achieved results confirm the possibility of designing intelligent sensors networks where decisions are taken at the data collection points.
978-1-6654-9956-9
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11367/109641
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