Abstract: In an example, a method includes obtaining, using a set of one or more processors, location information relating a sparse set of anchor beacons to respective locations within an environment; incorporating, by the set of one or more processors, the location information relating the sparse set of anchor beacons to respective locations within the environment into an environment dataset comprising a dense set of point data obtained via one or more portable communication device (PCD), wherein each point data has a position and respective wireless signal data at that position within the environment; and configuring a first PCD to use the environment dataset for a local location determination process based on a comparison of newly obtained wireless signal data from one or more wireless access points in the environment to the environment dataset.

Abstract: In an example, a method includes obtaining, from an odometry system, odometry data comprising distance data. The distance data includes data indicative of wireless signals at each respective distance. An initial path is generated using the odometry data. The initial path is displayed on a display device and adjustments to the initial path are received from a user. The initial path is changed based on the adjustments, and an updated path is displayed.

Abstract: Embodiments of the present disclosure relate enabling a user equipment (UE) to independently process and determine its own location within a designated coverage area. For example, the UE can obtain signals from wireless communication nodes deployed throughout the designated coverage, and can determine its location based on these signals. The UE can then selectively communicate its location to a communication platform service (CPS), such as when a change in location is detected. In this way, the CPS can maintain an accurate record of the UE's location without relying on continuous updates of location data from the UE. By allowing the UE to handle its own location determination and convey the location information only when certain conditions are met, the inventive concepts advantageously decrease the frequency of communications by the UE, thereby conserving battery life of the UE and reducing overall data consumption.

Abstract: In an example, a method includes obtaining, at a portable communication device (PCD), an environment dataset comprising point data. Each point data has a position and respective wireless signal data at that position within a fixed environment. Wireless access point data is received from one or more wireless access points in the fixed environment and includes a unique access point identifier and received signal strength values. The PCD compares the wireless access point data and the environment dataset and determines a best match for the received wireless access point data in the environment dataset, which is identified as a location.

Abstract: Techniques are disclosed for determining a location of a mobile device within a dead zone. One example computing system includes a non-transitory computer-readable medium; and a processor in communication with the non-transitory computer-readable medium, the processor configured to execute instructions stored in the non-transitory computer-readable medium to: receive a service request from a communication device, the service request comprising one or more signal strength readings associated with a current position of the communication device; determine, using a trained machine learning model, the current position corresponds to a fingerprint within a location map based on the one or more signal strength readings; and output a control signal configured to cause the communication device to alter a state of the communication device based on location information associated with the fingerprint, wherein the state of the communication device comprises a source or type of network connectivity.

Abstract: Techniques are disclosed for determining a location of a mobile device within an area of interest. One example computing system includes a processor configured to: obtain location information for areas of interest from a first device, the location information comprising signal strength readings associated with signal sources; receive a location request from a second device, the location request comprising signal strength readings associated with a current position of the second device; determine, using a machine learning model, the current position corresponds to a fingerprint within a location map, wherein the machine learning model is trained to: (i) generate the location map comprising fingerprints based on the location information; and (ii) determine a location based on a probability that a position matches a fingerprint in the location map; and determine that the current position corresponds to the fingerprint; and generate display information that includes the location of the fingerprint.