Positioning techniques for dead zones using beacons

1. A computing system comprising:

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, wherein the one or more signal strength readings are a first signal strength reading, wherein the current position is a first position, wherein the fingerprint is a first fingerprint, wherein the location information associated with the first fingerprint is first location information, wherein the control signal is a first control signal, and the processor further configured to execute instructions stored in the non-transitory computer-readable medium to:

receive a second signal strength reading from the communication device;

determine, using the trained machine learning model, a second position that corresponds to a second fingerprint; and

output a second control signal configured to cause the communication device to alter the source or type of network connectivity of the communication device based on second location information associated with the second fingerprint.

2. The computing system of claim 1 , wherein the current position is at least one of an indoor location or a geofenced location.

3. The computing system of claim 1 , wherein the computing system is at least one of a server or is integrated within the communication device.

4. The computing system of claim 1 , wherein the type of network connectivity is at least one of a cellular service, a Wi-Fi service, or a point-to-point (P2P) connection.

5. The computing system of claim 1 , wherein the control signal is configured to cause the communication device to alter the state of the communication device by switching from a first subscriber identification module (SIM) card to a second SIM card.

6. The computing system of claim 1 , wherein the control signal is configured to cause the communication device to alter the state of the communication device by changing at least one of a service provider, a Wi-Fi network, a Wi-Fi access point (“AP”), SIM card, or a P2P connection.

7. The computing system of claim 1 , wherein the control signal is configured to cause the communication device to alter the state of the communication device by enabling or disabling at least one of a radio, a modem, or a service in the communication device.

8. The computing system of claim 1 , wherein the control signal is configured to cause the communication device to alter the state of the communication device based in part on at least one of a user preference for a cellular mode or a Wi-Fi mode.

9. The computing system of claim 1 , wherein the location information associated with the fingerprint indicates either of: (i) a lack of a particular source or type of network connectivity or (ii) a presence of a particular source or type of network connectivity.

10. The computing system of claim 1 , wherein the one or more signal strength readings are a first signal strength reading, wherein the current position is a first position, wherein the fingerprint is a first fingerprint, wherein the location information associated with the first fingerprint is first location information, wherein the control signal is a first control signal, and the processor further configured to execute instructions stored in the non-transitory computer-readable medium to:

in response to a determination of a lack of cellular network connectivity and Wi-Fi network connectivity, output a second control signal configured to cause the communication device to establish a P2P connection with a remote computing device.

11. The computing system of claim 1 , wherein the communication device comprises a client device, and the processor further configured to execute instructions stored in the non-transitory computer-readable medium to:

transmit the location information associated with the fingerprint to the client device, wherein the location information is configured to cause the client device to display a graphical user interface (GUI) that includes a notification of a change to the source or type of network connectivity of the communication device.

12. The computing system of claim 1 , the processor further configured to execute instructions stored in the non-transitory computer-readable medium to:

determine label information for each of a plurality of areas of interest, wherein each of the plurality of areas of interest correspond to a fingerprint, and wherein the label information comprises an indication of a dead zone; and

annotate the location map using the label information.

13. The computing system of claim 12 , wherein the label information further comprises a name or a graphical representation corresponding to the fingerprint.

14. The computing system of claim 1 , wherein the trained machine learning model determines that the current position corresponds to the fingerprint within the location map based on the one or more signal strength readings by:

retrieving the location map, wherein the location map comprises a plurality of fingerprints that are derived from a corpus of location information;

determining a probability that the current position corresponds to the fingerprint within the location map; and

determining the probability that the current position corresponds to the fingerprint exceeds a threshold value.

15. A method comprising:

receiving, by a processor, 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;

determining, 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

outputting, by the processor, a control signal configured to cause the communication device to 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, wherein the one or more signal strength readings are a first signal strength reading, wherein the current position is a first position, wherein the fingerprint is a first fingerprint, wherein the location information associated with the first fingerprint is first location information, wherein the control signal is a first control signal;

receiving, by the processor, a second signal strength reading from the communication device;

determining, using the trained machine learning model, a second position that corresponds to a second fingerprint; and

outputting, by the processor, a second control signal configured to cause the communication device to alter the source or type of network connectivity of the communication device based on second location information associated with the second fingerprint.

16. The method of claim 15 , wherein the source or type of network connectivity is at least one of a cellular service, a Wi-Fi service, or a P2P connection, and wherein altering the state of the communication device comprises at least one of: changing a service provider, changing a cellular service, changing a Wi-Fi service, changing a Wi-Fi network, changing a Wi-Fi AP, changing a SIM card, changing a P2P connection, enabling a radio, enabling a modem, enabling a service, disabling a radio, disabling a modem, or disabling a service in the communication device.

17. The method of claim 15 , further comprising:

determining label information for each of a plurality of areas of interest, wherein each of the plurality of areas of interest correspond to a fingerprint, and wherein the label information comprises (i) an indication of a dead zone and (ii) a name or a graphical representation corresponding to a respective area of interest that is mapped to the fingerprint; and

annotating the location map using the label information.

18. A non-transitory computer-readable medium comprising program code executable by a processor to cause the processor to train a machine learning model 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 the current position corresponds to a fingerprint within a location map based on the one or more signal strength readings, wherein the location map comprises a plurality of fingerprints;

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 type of network connectivity, wherein the one or more signal strength readings are a first signal strength reading, wherein the current position is a first position, wherein the fingerprint is a first fingerprint, wherein the location information associated with the first fingerprint is first location information, and wherein the control signal is a first control signal,

receive, by the processor, a second signal strength reading from the communication device;

determine a second position that corresponds to a second fingerprint; and

output, by the processor, a second control signal configured to cause the communication device to alter the source or type of network connectivity of the communication device based on second location information associated with the second fingerprint.