Wireless network device and computational task reassignment for power efficiency
A computing device, includes one or more processors, configured to receive first data corresponding to a first radiofrequency signal received from a first wireless communication device, and second data corresponding to a second radiofrequency signal received from a second wireless communication device; select either the first wireless communication device or the second wireless communication device as a repeater device based on the first data or the second data according to one or more repeater selection criteria; instruct a transceiver to send a third radiofrequency signal to the repeater device, wherein the third radiofrequency signal comprises third data and an instruction for the repeater device to repeat the third data to the third wireless communication device.
1 . A primary computing device, comprising:
one or more processors, configured to:
receive a workload report and a power report from each of a plurality of secondary computing devices;
wherein each workload report includes identifiers of applications running on a respective secondary computing device of the plurality of respective computing devices;
generate an optimized workload for the plurality of secondary computing devices based on the workload reports and the power reports;
instruct a transceiver to send a radiofrequency signal including the optimized workload; and
wherein generating the optimized workload includes assigning an application currently executed by a first secondary computing device to a second secondary computing device.
2 . The primary computing device of claim 1 , wherein the power report comprises a power resource value; wherein the power resource value indicates whether a secondary computing device of the plurality of secondary computing devices is connected to a mains power supply; and wherein generating the optimized workload for the plurality of secondary computing devices further includes assigning a class of applications based on the power resource value.
3 . The primary computing device of claim 1 , wherein generating the optimized workload for the plurality of secondary computing devices comprises grouping the applications executed by the secondary computing devices by class and assigning a class of applications to a secondary computing device of the one or more secondary computing devices.
4 . The primary computing device of claim 1 , wherein the power report includes a power demand of one or more applications performed by a corresponding secondary computing device, and wherein generating the optimized workload includes generating the optimized workload based on the power demand by the one or more applications.
5 . The primary computing device of claim 1 , wherein the workload report further comprises a user interaction value, representing user interaction with the applications being executed by the respective secondary computing device of the plurality of computing devices, and wherein generating the optimized workload for the plurality of secondary computing devices further comprises ranking applications within a class based on the user interaction value.
6 . The primary computing device of claim 1 , wherein the primary computing device is configured to receive the workload report and the power report, and to send an optimization report, using a peer-to-peer wireless network connection.
7 . The primary computing device of claim 1 , wherein the one or more processors are further configured to send an instruction for each of the secondary computing devices to send the workload report and the power report.
8 . The primary computing device of claim 1 , wherein the primary computing device further comprises an artificial neural network, configured to generate the optimized workload based on the workload report and the power report, and wherein the one or more processors generating the optimized workload comprises generating the optimized workload using the artificial neural network.
9 . The primary computing device of claim 8 , wherein the artificial neural network is further configured to receive a power efficiency indicator from each of the secondary computing devices; wherein generating the optimized workload further includes generating the optimized workload based on the power efficiency indicators.
10 . The primary computing device of claim 1 , wherein the primary computing device is configured as a hub device, and wherein the primary computing device is configured to connect to the plurality of secondary computing devices as a plurality of spoke devices.
11 . A non-transitory computer readable medium, comprising instructions which, when executed, cause one or more processors to receive a workload report and a power report from each of a plurality of secondary computing devices, wherein each workload report comprises identifiers of applications running on a respective secondary computing device of the plurality of respective computing devices; generate an optimized workload for the plurality of secondary computing devices based on the workload reports and the power reports; and instruct a transceiver to send a radiofrequency signal including the optimized workload;
wherein generating the optimized workload includes assigning an application currently executed by a first secondary computing device to a second secondary computing device.
12 . The non-transitory computer readable medium of claim 11 , wherein generating the optimized workload for the plurality of secondary computing devices includes grouping the applications executed by the secondary computing devices by class and assigning a class of applications to a secondary computing device of the one or more secondary computing devices.
13 . The non-transitory computer readable medium of claim 11 , wherein the power report includes a power resource value;
wherein the power resource value indicates whether a secondary computing device of the plurality of secondary computing devices is connected to a mains power supply; and
wherein generating the optimized workload for the plurality of secondary computing devices further includes assigning a class of applications based on the power resource value.
14 . The non-transitory computer readable medium of claim 11 , wherein the power report includes a power demand of one or more applications performed by a corresponding secondary computing device, and wherein generating the optimized workload includes generating the optimized workload based on the power demand by the one or more applications.
15 . The non-transitory computer readable medium of claim 11 , wherein the workload report further includes a user interaction value, representing user interaction with the applications being executed by the respective secondary computing device of the plurality of computing devices, and wherein generating the optimized workload for the plurality of secondary computing devices further includes ranking applications within a class based on the user interaction value.
16 . The non-transitory computer readable medium of claim 11 , wherein the instructions are further configured to cause the one or more processors to receive the workload report and the power report, and to send an optimization report, using a peer-to-peer wireless network connection.
17 . The non-transitory computer readable medium of claim 11 , wherein the instructions are further configured to cause the one or more processors to send an instruction for each of the secondary computing devices to send the workload report and the power report.
18 . The non-transitory computer readable medium of claim 11 , wherein the instructions are further configured to cause the one or more processors to generate the optimized workload based on the workload report and the power report using an artificial neural network.
19 . The non-transitory computer readable medium of claim 18 , wherein the instructions are further configured to cause the one or more processors to receive a power efficiency indicator from each of the secondary computing devices and to generate the optimized workload based on the power efficiency indicators using the artificial neural network.