Using machine learning (ML) to make observations of network operations is faced with many constraints, including collection constraints, storage constraints, and processing constraints. Additionally, in many instances, data collected from a network may be unusable and will incur collection, storage, and processing costs with potentially limited return. Presented herein are techniques through which pre-filtering tasks can be distributed to wireless access points (APs) to highlight valuable metrics and learn from network deployments.
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Raca, Mirko and Ficara, Domenico, "DISTRIBUTED PARAMETER MONITORING FOR WIRELESS DEPLOYMENTS", Technical Disclosure Commons, (June 18, 2021)