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.