T-mobile Usa, Inc.

Kevin Hinson

Jie Hui

Antoine T Tran

Doru Culiac

Narciso Faustino

Bryan Yang

Hongliang Zhang

Jacob Lee

Julian Loaiza

Techniques are disclosed for locating a user device in an indoor environment such as a building based at least on a building topology model using one or more Internet of Things (IoT) devices. Certain aspects of the techniques include detecting a presence of a user device in communication with an IoT device in a building, wherein the IoT device is associated with device attributes. The building is identified based at least on a building topology model that is associated with the building and the device attributes. The location of the IoT device is determined based at least on the building topology model. The user device is located relative to the location of the IoT device.

Localization using internet of things devices

Techniques are described for locating user devices in an indoor environment based at least on a building topology model. The techniques include detecting a presence of a user device in a building. The building may include ingress and egress points connecting defined spaces within the building. The locations of the individual ingress and egress points are identified based at least on the movement data of the user device. A building topology model may be generated using the locations of the individual ingress and egress points. To locate the user device at a given timestamp, one or more of the ingress and egress points passed through by the user device may be identified. The location of the user device may be determined based at least on the locations of the one or more ingress and egress points passed through by the user device mapped to the building topology model.

Constrained user device location using building topology

A network speed map indicating data rates (e.g., download speeds) provided by a mobile network within a geographical area can be created based on crowd-sourced data. An example process includes receiving a plurality of data samples from mobile devices, each sample including a CQI value and a geolocation. Efficiency values can be determined from sampled CQI values associated with a bin of interest within the geographical area, and a spectrum efficiency value associated with the bin can be determined based on the determined efficiency values. A data rate (e.g., a download speed) associated with the bin can be estimated based on the spectrum efficiency value, and a network speed map showing one or more bins, including the bin, with estimated data rates (e.g., download speed) can be generated and displayed on a user computing device.

Network speed map

Techniques are described herein for developing a fingerprint map that may be used for 3D indoor localization. In one example, a network server may leverage a building footprint from an open source database with signal measurements taken by probing user devices from signal sources such as access point (AP) devices. The network server may use the signal measurements to remotely calculate corresponding 3D positions of the AP devices in a particular building. Further, the network server may use the building footprint and the calculated 3D positions of the AP devices as references for developing the fingerprint map for 3D indoor localization.

Developing a fingerprint map for determining an indoor location of a wireless device

A telemetry data computing engine may receive the telemetry data of a user device, via a network, and it may apply a machine learning algorithm to at least one telemetry data and generate a predicted location for the user device. The predicted location may be used to generate a location pattern for the user device and a user device profile for the user device. The user device profile may be routed to the wireless carrier core network.

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Seattle, WA, United States of America

Kevin Hinson