Predictive power management in a wireless sensor network

• US 10,133,989 B1
• Filed: 07/27/2017
• Issued: 11/20/2018
• Est. Priority Date: 11/26/2013
• Status: Expired due to Fees

• Current Assignee: Invent.ly LLC
• Original Assignee: Invent.ly LLC
• Inventors: Brown, Stephen J.
• Primary Examiner(s): Rampuria, Satish
• Application Number:
  US15/661,150
• Time in Patent Office: 481 Days
• Field of Search: 700286-306
• CPC Class Codes: G05F 5/00 Systems for regulating electric variables by detecting deviations in the electric input to the system and thereby controlling a device within the system to obtain a regulated output
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/046 Forward inferencing; Production systems

Abstract

An apparatus comprising a power source, one or more sensors, a transceiver, and a memory. The power source may be configured to store energy to power the apparatus. The one or more sensors may be configured to receive captured data from one of a plurality of sources. The transceiver may be configured to send and receive data to and from a wireless network. The processor may be configured to execute computer readable instructions. The memory may be configured to store a set of instructions executable by the processor. The instructions may be configured to (A) evaluate an expected power usage budget calculated using a predictive model of future energy consumption and (B) (i) store the captured data in the memory in a first mode and (ii) transmit the captured data to a remote storage device in a second mode. The first mode or the second mode is selected based on characteristics of the captured data received from the sensors.

Claims

1. An apparatus comprising:

a power source to store energy to power said apparatus;

one or more sensors to capture data received from one of a plurality of sources;

a transceiver to send and receive communications to and from a wireless network;

a processor to execute computer readable instructions; and

a memory to store a set of instructions executable by the processor, said instructions, when executed, are performed to (A) evaluate an expected power usage budget calculated using a predictive model of future energy consumption, (B) store said data in the memory in a first mode and (C) transmit the data to a remote storage device in a second mode, wherein (a) said first mode or said second mode is selected based on characteristics of the data captured by the sensors, (b) said instructions include a machine learning process to optimize said predictive model in response to historical energy consumption data comprising prior power usage patterns and (c) the instructions contain computational logic for computing a threshold condition based on one or more types of said data and one or more data points over time in the wireless network.

2. The apparatus according to claim 1, wherein said one or more sensors receive said data from an external device.

3. The apparatus according to claim 1, wherein said one or more sensors receive said data from forces acting on said apparatus.

4. The apparatus according to claim 1, wherein said instructions are updated through the wireless network.

5. The apparatus according to claim 1, further comprising:

a remote computing device removed from the apparatus to selectively couple to the apparatus and collect said data and transmit new instructions to the apparatus.

6. The apparatus according to claim 1, wherein the instructions contain a set of threshold conditions for storing said data in the first mode.

7. The apparatus according to claim 1, wherein the instructions contain a set of threshold conditions for transmitting said data in the second mode.

8. The apparatus according to claim 1, wherein said computational logic includes a cascade of threshold conditions that determine a powering on or off of additional features of said one or more sensors.

9. The apparatus according to claim 5, wherein the remote computing device is configured to upload to the one or more sensors a schedule of when said data is to be captured and stored.

10. The apparatus according to claim 8, wherein the cascade has a power-aware prioritization of said data capture and communications to lower a duty cycle to average power.

11. The apparatus according to claim 1, wherein the instructions coalesce said data by only recording changes when compared to a base state or expected state.

12. The apparatus according to claim 1, wherein said instructions provide a computationally efficient process to only record an average value of the data captured by the sensors over a period of time and then starts recording events if a measurement is significantly above the average value.

13. The apparatus according to claim 1, wherein the apparatus is self-powered through said power source.

14. The apparatus according to claim 1, wherein said power source is implemented as a battery.

15. The apparatus according to claim 1, wherein said power source is replenished through energy harvesting of light, vibration, thermal variation, thermal gradient, infrared, chemical, RF sources or some combination.

16. The apparatus according to claim 1, wherein the computational logic determines said threshold condition for storing and transmitting said data based on (i) currently available power, (ii) predicted future energy harvesting opportunities based on forecasting a future environment, and (iii) predicted energy consumption based on applying the threshold condition to the future environment.

18. The apparatus according to claim 1, wherein the optimization is performed to maximize a utility within a set of cost parameters, or alternatively, to maximize utility per unit of cost.

17. An apparatus comprising:

a power source to store energy to power said apparatus;

one or more sensors to capture data received from one of a plurality of sources;

a transceiver to send and receive communications to and from a wireless network;

a processor to execute computer readable instructions; and

a memory to store a set of instructions executable by the processor, said instructions, when executed, are to (A) evaluate an expected power usage budget calculated using a predictive model of future energy consumption, (B) store said data in the memory in a first mode and (C) transmit the data to a remote storage device in a second mode, wherein (i) said first mode or said second mode is selected based on characteristics of the data captured by the sensors, (ii) energy use by the apparatus is characterized as an energy cost function in order to adapt to a duty cycle, (iii) the adaptive and conditional powering of additional sensors is determined based on a set of initial sensor readings and (iv) energy harvesting is a negative cost or energy contribution in the predictive model.