Partial phase vectors as network sensors

    公开(公告)号:US11579236B2

    公开(公告)日:2023-02-14

    申请号:US16356662

    申请日:2019-03-18

    Abstract: Systems and methods provide for improving the accuracy of a location system. The location system can capture partial phase vector data from one or more access points (APs). The location system can capture associated data associated with the partial phase vector data across multiple dimensions, such as identity data of the APs and client devices generating the partial phase vector data and frequency band data, location data, a time and date, and other data associated with the partial phase vector data. The location system can determine correlation data across the multiple dimensions using the first partial phase vector data and the associated data. The location system can a cause of the partial phase vector data based on the correlation data. The location system can perform one or more remediation actions based on the cause of the partial phase vector data.

    Learning-based service migration in mobile edge computing

    公开(公告)号:US11410046B2

    公开(公告)日:2022-08-09

    申请号:US17474191

    申请日:2021-09-14

    Abstract: Learning-based service migration in mobile edge computing may be provided. First, a service migration policy may be created for a network that includes a plurality of edge clouds configured to provide a service to users. Next, a movement of a user receiving the service from a source edge cloud may be detected. The source edge cloud may be associated with a first area and the detected movement may be from the first area to a second area. Then, the service migration policy may be applied to determine whether to migrate the service for the user from the source edge cloud. In response to determining to migrate the service, a target edge cloud may be identified and the service for the user may be migrated from the source edge cloud to the target edge cloud. The service migration policy may then be updated based on a success of the migration.

    Adaptively calibrated spatio-temporal compressive sensing for sensor networks

    公开(公告)号:US11275366B2

    公开(公告)日:2022-03-15

    申请号:US16405064

    申请日:2019-05-07

    Abstract: In one embodiment, a management entity monitors for a change in a convergence rate of spatio-temporal compressive sensing measurements from a plurality of sensors in a sensor network operating according to a measurement matrix up to a halting criterion, and if the change is below a given threshold, determines whether the change is due to impulse noise or due to continued sensed measurements. If continued sensed measurements, the management entity initiates a single-dimensional compressive sensing in a spatial domain at regular time intervals, and identifies and tracks gradient clusters. In response to a change in joint spatio-temporal sparsity of tracked nodes of the gradient clusters, the management entity can then determine an updated measurement matrix based on the joint spatio-temporal sparsity of tracked nodes while satisfying one or more operating parameters, and directs at least certain sensors of the plurality of sensors to operate according to the updated measurement matrix.

    SCALABILITY OF LOCATION IN THE CLOUD WITH ANGLE OF ARRIVAL (AOA) SEARCH ON THE EDGE

    公开(公告)号:US20200326405A1

    公开(公告)日:2020-10-15

    申请号:US16380668

    申请日:2019-04-10

    Abstract: Determining a device's location in a space in real time is computing intensive. To offload some of the workload in conducting this hyperlocation, the access points in the network conduct some of process in determining the location of a device. The cloud determines a restricted AoA search area based on previous client locations. After this determination, a three-dimensional (3D) AoA search is conducted by each AP in the restricted area (restricted by a range of azimuth directions) for a device. Finally, each AP reports a location(s) for the device, which comprises weights for selected angular sectors. The cloud can then construct a probability heat map for location computation from the weights provided from each AP for the device.

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