Dynamic Wifi Transmit Power Reduction to Conserve Device Power in a Mixed 5G/Wifi Service Area

    公开(公告)号:US20250031142A1

    公开(公告)日:2025-01-23

    申请号:US18356736

    申请日:2023-07-21

    Abstract: In one embodiment, a method can receive transmission metadata associated with a plurality of devices within an Internet of Things (IoT) network. The method can use a transmission sensing component (TSC) to measure transmission energy cost for each of the plurality of devices over a time period using the transmission metadata. The method can use a central compute engine (CCE) and the transmission energy cost for each of the plurality of devices to determine a plurality of transmission features associated with the plurality of devices having a transmission energy cost that is minimized. The method can use a transmission scheduling engine (TSE) and the plurality of transmission features to generate a transmission mode schedule to reduce a transmission energy cost for the plurality of devices within the IoT network. The method can adjust a transmission mode associated with the plurality of devices based on the transmission mode schedule.

    DETERMINING CONTEXT AND ACTIONS FOR MACHINE LEARNING-DETECTED NETWORK ISSUES

    公开(公告)号:US20210281492A1

    公开(公告)日:2021-09-09

    申请号:US16812517

    申请日:2020-03-09

    Abstract: In one embodiment, a network assurance service that monitors a network detects a network issue in the network using a machine learning model and based on telemetry data captured in the network. The service assigns the detected network issue to an issue cluster by applying clustering to the detected network issue and to a plurality of previously detected network issues. The service selects a set of one or more actions for the detected network issue from among a plurality of actions associated with the previously detected network issues in the issue cluster. The service obtains context data for the detected network issue. The service provides, to a user interface, an indication of the detected network issue, the obtained context data for the detected network issue, and the selected set of one or more actions.

    Traffic-based inference of influence domains in a network by using learning machines

    公开(公告)号:US10540605B2

    公开(公告)日:2020-01-21

    申请号:US13946386

    申请日:2013-07-19

    Abstract: In one embodiment, techniques are shown and described relating to traffic-based inference of influence domains in a network by using learning machines. In particular, in one embodiment, a management device computes a time-based traffic matrix indicating traffic between pairs of transmitter and receiver nodes in a computer network, and also determines a time-based quality parameter for a particular node in the computer network. By correlating the time-based traffic matrix and time-based quality parameter for the particular node, the device may then determine an influence of particular traffic of the traffic matrix on the particular node.

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