Using predictive analytics to determine expected use patterns of vehicles to recapture under-utilized computational resources of vehicles

1. A distributed computing network comprising:

one or more vehicles, each vehicle configured to act as a node in the distributed computing network; and

a remote server comprising a processor and a memory module storing one or more non-transitory processor-readable instructions that when executed by the processor cause the remote server to:

establish a data connection with the one or more vehicles;

predict a pattern-of-use of the one or more vehicles;

determine a predicted current use of the one or more vehicles based on a current status of the one or more vehicles; and

allocate a computational task to the one or more vehicles based on the predicted pattern-of-use and the predicted current use,

wherein the predicted current use of the one or more vehicles is determined based on a period of time the one or more vehicles is expected to remain stationary at an expected location, and

wherein the remote server assigns a portion of the computational task proportional to the period of time the vehicle is expected to remain stationary at the expected location.

2. The distributed computing network of claim 1 , wherein the current status of the one or more vehicles includes a current location of the one or more vehicles.

3. The distributed computing network of claim 1 , wherein the predicted current use of the one or more vehicles is determined based on a status of one or more critical systems of the one or more vehicles.

4. The distributed computing network of claim 3 , wherein the one or more critical systems include an adaptive cruise control system.

5. The distributed computing network of claim 3 , wherein:

the one or more critical systems include a battery module that monitors a battery voltage and use rate of a vehicle battery; and

the remote server allocates computational tasks to the one or more vehicles based on the battery voltage and use rate.

6. The distributed computing network of claim 1 , wherein the computational task includes mining cryptocurrency.

7. The distributed computing network of claim 1 , wherein at least one of the one or more vehicles is a ride-share vehicle and the pattern-of-use is predicted based on reservation information of the ride-share vehicle.

8. The distributed computing network of claim 1 , wherein the current status of the one or more vehicles includes current speed or acceleration of the one or more vehicles.

9. The distributed computing network of claim 1 , wherein users of the one or more vehicles receive an incentive to perform computational tasks allocated by the remote server.

10. The distributed computing network of claim 1 , wherein the expected location is determined by comparing the actual location of the vehicle against the predicted pattern-of-use.

11. The distributed computing network of claim 1 , wherein the node includes a plurality of nodes each associated with a respective vehicle, and the remote server randomly assigns work to the nodes to determine a discrepancy between the nodes based on whether one of the nodes reports an answer that is not the same with the other node.

12. The distributed computing network of claim 1 , wherein the one or more vehicles operate as rideshare vehicles, the remote server receives a rideshare agenda including a location and time of rideshare travel of each rideshare vehicle, and the pattern-of-use and the current use are predicted based on the rideshare agenda.

13. A vehicle configured to act as a node in a distributed computing network, the vehicle comprising:

network interface hardware;

a processor; and

a memory module storing non-transitory processor-readable instructions that when executed by the processor cause the vehicle to:

establish a communicative connection with a remote server;

predict a pattern-of-use;

predict a current use based on a current status of the vehicle;

transmit the predicted pattern-of-use and the predicted current use to the remote server; and

receive a computational task from the remote server based on the predicted pattern-of-use and the predicted current use,

wherein the predicted current use of the vehicle is determined based on a period of time the vehicle is expected to remain stationary at an expected location, and

wherein the remote server assigns a portion of the computational task proportional to the period of time the vehicle is expected to remain stationary at the expected location.

14. The vehicle of claim 13 , wherein the non-transient processor-readable instructions further cause the vehicle to:

identify the current status of the vehicle including a current location of the vehicle; and

transmit the current location of the vehicle to the remote server.

15. The vehicle of claim 13 , wherein the current use is predicted based on the status of one or more critical systems of the vehicle.

16. The vehicle of claim 15 , wherein the one or more critical systems include an adaptive cruise control system of the vehicle.

17. The vehicle of claim 15 , wherein:

the one or more critical systems include a battery system that monitors a battery voltage and use rate of a vehicle battery; and

the vehicle receives computational tasks based on the battery voltage and use rate.

18. The vehicle of claim 13 , wherein the computational task includes mining cryptocurrency.

19. The vehicle of claim 13 , wherein the vehicle is a ride-share vehicle and the pattern-of-use is predicted based on reservation information.

20. The vehicle of claim 13 , wherein the vehicle accepts one or more incentives in exchange for performing computational tasks.

21. A method of allocating computational tasks in a distributed computing network comprising a remote server communicatively coupled between a grid computing network and one or more vehicles acting as nodes in the network, the method comprising:

establishing, by the remote server, a data connection with the one or more vehicles;

predicting, by the remote server, a pattern-of-use of the one or more vehicles;

predicting, by the remote server, a current use of the one or more vehicles based on a current status of the one or more vehicles; and

allocating, by the remote server, a computational task to the one or more vehicles based on the predicted pattern-of-use and the predicted current use,

wherein the predicted current use of the one or more vehicles is determined based on a period of time the one or more vehicles is expected to remain stationary at an expected location, and

wherein the remote server assigns a portion of the computational task proportional to the period of time the vehicle is expected to remain stationary at the expected location.

22. The method of claim 21 , wherein users of the one or more vehicles receive an incentive to perform the allocated computational task.

23. The method of claim 21 , wherein the computational task includes mining cryptocurrency.