Secure and efficient computing sharing for electric automobiles
An electric vehicle computing sharing system ( 100 ) is adapted to receive a signal indicating the electric vehicle ( 110, 120, 130 ) is connected to a charging station ( 115, 125, 135 ). The computing sharing system ( 100 ) may be further adapted to receive information about the electric vehicle ( 110, 120, 130 ). The computing sharing system ( 100 ) may be further adapted to determine a predicted charging duration ( 535 ) for the electric vehicle ( 110, 120, 130 ). The computing sharing system ( 100 ) may be further adapted to identify a task for execution by a computing resource of the electric vehicle ( 110, 120, 130 ) based on the predicted charging duration ( 535 ). The computing sharing system ( 100 ) may be further adapted to transmit the task to the electric vehicle ( 110, 120, 130 ). The computing sharing system ( 100 ) may be further adapted to receive a result for the task from the electric vehicle ( 110, 120, 130 ).
1. A system for coordinating shared computing with an electric vehicle, comprising:
at least one processor; and
memory capable of storing instructions that, when executed by the at least one processor, cause the at least one processor to:
obtain a signal based on connection of the electric vehicle to a charging station;
obtain charging information about the electric vehicle including a current battery level determined from sensor data that measures at least one battery of the electric vehicle;
calculate a predicted charging duration for the electric vehicle at the charging station, based on the charging information;
identify a task for execution by a computing resource of the electric vehicle based on the predicted charging duration, power consumption of the computing resource for execution of the task and other uncompleted tasks, and availability of the computing resource of the electric vehicle to complete the task during the predicted charging duration, wherein the power consumption of the computing resource is capable of draining the at least one battery during the connection of the electric vehicle to the charging station;
cause transmission of the task to the electric vehicle; and
control execution of the task by the computing resource of the electric vehicle, during the connection of the electric vehicle to the charging station, wherein a result for the task is produced in response to completion of the task.
2. The system of claim 1 , wherein the charging information additionally includes a charging history of the electric vehicle.
3. The system of claim 2 , wherein the availability of the computing resource of the electric vehicle to complete the task is determined based on preferences of an operator of the electric vehicle.
4. The system of claim 2 , wherein the instructions further cause the at least one processor to:
invoke a trained machine-learning model to calculate the predicted charging duration, using the charging information, to recharge the at least one battery to a full charge at the charging station.
5. The system of claim 4 , wherein use of the trained machine-learning model to calculate the predicted charging duration further includes using information from a personal calendar.
6. The system of claim 1 , wherein the predicted charging duration is calculated based on at least one of: a location of the electric vehicle, habits of a user of the electric vehicle, time of day, day of the week, or a holiday.
7. The system of claim 1 , wherein the instructions further cause the at least one processor to:
receive validation results from a plurality of electric vehicles for the task; and
validate the result with the validation results.
8. The system of claim 1 , wherein the instructions further cause the at least one processor to:
calculate the availability of the computing resource of the electric vehicle to complete the task during the predicted charging duration, based on power requirements for the task and power requirements for charging the electric vehicle.
9. A method for coordinating shared computing with an electric vehicle, comprising:
receiving a signal based on connection of the electric vehicle to a charging station;
receiving charging information about the electric vehicle including a current battery level determined from sensor data that measures at least one battery of the electric vehicle;
calculating a predicted charging duration for the electric vehicle at the charging station, based on the charging information;
identifying a task for execution by a computing resource of the electric vehicle based on the predicted charging duration, power consumption of the computing resource for execution of the task and other uncompleted tasks, and availability of the computing resource of the electric vehicle to complete the task during the predicted charging duration, wherein the power consumption of the computing resource is capable of draining the at least one battery during the connection of the electric vehicle to the charging station;
transmitting the task to the electric vehicle; and
controlling execution of the task by the computing resource of the electric vehicle, during the connection of the electric vehicle to the charging station, wherein a result for the task is produced in response to completion of the task.
10. The method of claim 9 , wherein the charging information additionally includes a charging history of the electric vehicle.
11. The method of claim 10 , wherein the availability of the computing resource of the electric vehicle to complete the task is determined based on preferences of an operator of the electric vehicle.
12. The method of claim 10 , further comprising:
invoking a trained machine-learning model to calculate the predicted charging duration, using the charging information, to recharge the at least one battery to a full charge at the charging station.
13. The method of claim 12 , wherein use of the trained machine-learning model to calculate the predicted charging duration further includes using information from a personal calendar.
14. The method of claim 9 , wherein the predicted charging duration is calculated based on at least one of: a location of the electric vehicle, habits of a user of the electric vehicle, time of day, day of the week, or a holiday.
15. The method of claim 9 , further comprising:
receiving validation results from a plurality of electric vehicles for the task; and
validating the result with the validation results.
16. The method of claim 9 , further comprising:
calculating the availability of the computing resource of the electric vehicle to complete the task during the predicted charging duration, based on power requirements for the task and power requirements for charging the electric vehicle.
17. At least one non-transitory machine-readable medium to implement coordinating shared computing with an electric vehicle, the at least one non-transitory machine-readable medium capable of storing instructions that, when executed by processing circuitry, cause the processing circuitry to:
obtain a signal based on connection of the electric vehicle to a charging station;
obtain charging information about the electric vehicle including a current battery level determined from sensor data that measures at least one battery of the electric vehicle;
calculate a predicted charging duration for the electric vehicle at the charging station, based on the charging information;
identify a task for execution by a computing resource of the electric vehicle based on the predicted charging duration, power consumption of the computing resource for execution of the task and other uncompleted tasks, and availability of the computing resource of the electric vehicle to complete the task during the predicted charging duration, wherein the power consumption of the computing resource is capable of draining the at least one battery during the connection of the electric vehicle to the charging station;
cause transmission of the task to the electric vehicle; and
control execution of the task by the computing resource of the electric vehicle, during the connection of the electric vehicle to the charging station, wherein a result for the task is produced in response to completion of the task.
18. The machine-readable medium of claim 17 , wherein the charging information additionally includes a charging history of the electric vehicle.
19. The machine-readable medium of claim 18 , wherein the availability of the computing resource of the electric vehicle to complete the task is determined based on preferences of an operator of the electric vehicle.
20. The machine-readable medium of claim 18 , wherein the instructions further cause the processing circuitry to:
invoke a trained machine-learning model to calculate the predicted charging duration, using the charging information, to recharge the at least one battery to a full charge at the charging station.
21. The machine-readable medium of claim 20 , wherein use of the trained machine-learning model to calculate the predicted charging duration further includes using information from a personal calendar.
22. The machine-readable medium of claim 17 , wherein the predicted charging duration is calculated based on at least one of: a location of the electric vehicle, habits of a user of the electric vehicle, time of day, day of the week, or a holiday.
23. The machine-readable medium of claim 17 , wherein the instructions further cause the processing circuitry to:
receive validation results from a plurality of electric vehicles for the task; and
validate the result with the validation results.
24. The machine-readable medium of claim 17 , wherein the instructions further cause the processing circuitry to:
calculate the availability of the computing resource of the electric vehicle to complete the task during the predicted charging duration, based on power requirements for the task and power requirements for charging the electric vehicle.