Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.