DISTRIBUTED PARAMETER MONITORING FOR WIRELESS DEPLOYMENTS

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