Dynamic selection of parameter threshold values

1. A method, comprising:

receiving first sensor data from a first set of Internet of Things devices;

determining sensor data collection rates for a first artificial intelligence model by analyzing the first sensor data using a second artificial intelligence model executed by a processor; and

based on the sensor data collection rates, communicating sensor control commands to a second set of Internet of Things devices, the sensor control commands specifying, to the second set of Internet of Things devices, sensor data communication rates that respective ones of the second set of Internet of Things devices are to implement for communicating, to the first artificial intelligence model, second sensor data generated by sensors of the respective ones of the second set of Internet of Things devices.

2. The method of claim 1 , further comprising:

receiving the second sensor data from the second set of Internet of Things devices;

determining, by the first artificial intelligence model, artificial intelligence predictions by analyzing the second sensor data; and

outputting the artificial intelligence predictions.

3. The method of claim 1 , wherein the determining the sensor data collection rates for the first artificial intelligence model comprises:

based on the first sensor data, determining a plurality of contextual information;

based on the plurality of contextual information, determining a plurality of overall contexts; and

determining a respective set of the sensor data collection rates for each of the plurality of overall contexts.

4. The method of claim 3 , wherein each respective set of the sensor data collection rates specifies a plurality of the sensor data collection rates, each of the plurality of the sensor data collection rates assigned to a respective sensor type.

5. The method of claim 4 , wherein a first data collection rate, specified for a particular sensor type, in a first set of the sensor data collection rates differs from a second data collection rate, specified for the particular sensor type, in a second set of the sensor data collection rates.

6. The method of claim 3 , wherein at least a portion of the plurality of contextual information is predicted by the second artificial intelligence model.

7. The method of claim 6 , further comprising:

the second artificial intelligence model improving contextual information predictions by training itself using artificial intelligence, the training comprising:

monitoring actual contextual information;

comparing the actual contextual information to the contextual information predictions; and

updating parameters used for predicting the contextual information.

8. A system, comprising:

a processor programmed to initiate executable operations comprising:

receiving first sensor data from a first set of Internet of Things devices;

determining sensor data collection rates for a first artificial intelligence model by analyzing the first sensor data using a second artificial intelligence model; and

based on the sensor data collection rates, communicating sensor control commands to a second set of Internet of Things devices, the sensor control commands specifying, to the second set of Internet of Things devices, sensor data communication rates that respective ones of the second set of Internet of Things devices are to implement for communicating, to the first artificial intelligence model, second sensor data generated by sensors of the respective ones of the second set of Internet of Things devices.

9. The system of claim 8 , the executable operations further comprising:

receiving the second sensor data from the second set of Internet of Things devices;

determining, by the first artificial intelligence model, artificial intelligence predictions by analyzing the second sensor data; and

outputting the artificial intelligence predictions.

10. The system of claim 8 , wherein the determining the sensor data collection rates for the first artificial intelligence model comprises:

based on the first sensor data, determining a plurality of contextual information;

based on the plurality of contextual information, determining a plurality of overall contexts; and

determining a respective set of the sensor data collection rates for each of the plurality of overall contexts.

11. The system of claim 10 , wherein each respective set of the sensor data collection rates specifies a plurality of the sensor data collection rates, each of the plurality of the sensor data collection rates assigned to a respective sensor type.

12. The system of claim 11 , wherein a first data collection rate, specified for a particular sensor type, in a first set of the sensor data collection rates differs from a second data collection rate, specified for the particular sensor type, in a second set of the sensor data collection rates.

13. The system of claim 10 , wherein at least a portion of the plurality of contextual information is predicted by the second artificial intelligence model.

14. The system of claim 8 , the executable operations further comprising:

the second artificial intelligence model improving contextual information predictions by training itself using artificial intelligence, the training comprising:

monitoring actual contextual information;

comparing the actual contextual information to the contextual information predictions; and

updating parameters used for predicting the contextual information.

15. A computer program product, comprising:

one or more computer readable storage mediums having program code stored thereon, the program code stored on the one or more computer readable storage mediums collectively executable by a data processing system to initiate operations including:

receiving first sensor data from a first set of Internet of Things devices;

determining sensor data collection rates for a first artificial intelligence model by analyzing the first sensor data using a second artificial intelligence model; and

based on the sensor data collection rates, communicating sensor control commands to a second set of Internet of Things devices, the sensor control commands specifying, to the second set of Internet of Things devices, sensor data communication rates that respective ones of the second set of Internet of Things devices are to implement for communicating, to the first artificial intelligence model, second sensor data generated by sensors of the respective ones of the second set of Internet of Things devices.

16. The computer program product of claim 15 , wherein the program code is executable by the data processing system to initiate operations further comprising:

receiving the second sensor data from the second set of Internet of Things devices;

determining, by the first artificial intelligence model, artificial intelligence predictions by analyzing the second sensor data; and

outputting the artificial intelligence predictions.

17. The computer program product of claim 15 , wherein the determining the sensor data collection rates for the first artificial intelligence model comprises:

based on the first sensor data, determining a plurality of contextual information;

based on the plurality of contextual information, determining a plurality of overall contexts; and

determining a respective set of the sensor data collection rates for each of the plurality of overall contexts.

18. The computer program product of claim 17 , wherein each respective set of the sensor data collection rates specifies a plurality of the sensor data collection rates, each of the plurality of the sensor data collection rates assigned to a respective sensor type.

19. The computer program product of claim 18 , wherein a first data collection rate, specified for a particular sensor type, in a first set of the sensor data collection rates differs from a second data collection rate, specified for the particular sensor type, in a second set of the sensor data collection rates.

20. The computer program product of claim 17 , wherein at least a portion of the plurality of contextual information is predicted by the second artificial intelligence model.