Abstract
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.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Raca, Mirko and Ficara, Domenico, "DISTRIBUTED PARAMETER MONITORING FOR WIRELESS DEPLOYMENTS", Technical Disclosure Commons, (June 18, 2021)
https://www.tdcommons.org/dpubs_series/4382