RSSI LOCATION TRACKING VIA RADIO SCANNING FOR TRIGGERED OFDMA UPLINK

    公开(公告)号:US20230199432A1

    公开(公告)日:2023-06-22

    申请号:US17555598

    申请日:2021-12-20

    CPC classification number: H04W4/029 H04B17/318 H04W84/12

    Abstract: This technology allows for determining the location of client devices via radio scanning for triggered orthogonal frequency-division multiple access (“OFDMA”) uplinks. Access points (“APs”) are configured for OFDMA transmissions. A first AP transmits a trigger frame on particular channel to stations in the wireless network. Neighboring APs scan channels for trigger frames (“TF”). Upon detection of a TF, neighboring APs associate a station identifier with a frequency allocation, or resource unit, in the TF. The neighboring APs receive an OFDMA uplink from the stations, determine a received signal strength indicator (“RSSI”) value for each frequency allocation in the OFDMA uplink, and transmit the RSSI values with the associated station identifier to the first AP. The first AP determines the location of each station by mapping a distance value to the RSSI values.

    CHANNEL COORDINATION FOR OPTIMIZATION OF IIoT DEVICE LOCATION AND DATA SERVICES IN WIFI NETWORK

    公开(公告)号:US20230067588A1

    公开(公告)日:2023-03-02

    申请号:US17983502

    申请日:2022-11-09

    Abstract: In one embodiment, a method comprises first causing, by a controller device, wireless access points (APs) to allocate first non-interfering wireless channels for a prescribed reliable data service for wireless client devices in a WLAN; second causing the wireless APs to allocate a second shared channel having a bandwidth that is greater than the corresponding bandwidth of any of the first non-interfering wireless channels; allocating for each wireless client device a corresponding location service interval on the second shared channel for transmission of at least a corresponding identifiable wireless data unit for locating the corresponding wireless client device between two or more of the wireless APs; and determining a location of at least one of the wireless client devices based on reception of at least the corresponding wireless data unit between the one wireless client device and the two or more wireless APs during the corresponding location service interval.

    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.

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