REDUCING POWER CONSUMPTION OF WI-FI ENABLED MOBILE DEVICES

• US 20090285142A1
• Filed: 05/04/2009
• Published: 11/19/2009
• Est. Priority Date: 08/11/2005
• Status: Active Grant

• Current Assignee: Toshiba Corporation
• Original Assignee: Telcordia Technologies Incorporated (Telefonaktiebolaget LM Ericsson), Toshiba Corporation
• Inventors: GURUNG, Provin, ZHANG, Tao, Van Den Berg, Eric, MADHANI, Sunil
• Granted Patent: US 8,401,594 B2
• Time in Patent Office: Days
• US Class Current: 370/311
• CPC Class Codes: H04W 52/0232 according to average transmission signal activity
• CPC Class Codes: Y02D 30/70 in wireless communication networks

Abstract

A system and method for maximizing the standby time of mobile communication devices that have WiFi or other high energy-consuming network interfaces, by predicting in real time actionable silent periods (ASPs) of the interface and shutting the interface down during these ASPs. Standby times are significantly increased, resulting in longer periods of operation before battery charging is required, while keeping minimal the probabilities of missing incoming data packets when the interface is turned off.

Claims

1. A method of reducing energy consumption of a device that operates intermittently with extended periods of inactivity between intermittent operations, comprising the steps of:

monitoring operation of said device to detect periods of inactivity;

predicting the duration of a detected period of inactivity; and

turning off power to said device for a period of time substantially corresponding to said predicted duration.

2. A method as set forth in claim 1, wherein said device is a network interface.

3. A method as set forth in claim 2, wherein said network interface is coupled to a mobile communication apparatus.

4. A method as set forth in claim 3, wherein said network interface is a WiFi interface.

5. A method as set forth in claim 1, further including the step of storing a distribution of inactivity period durations, wherein said predicting step includes the step of calculating a probability of inactivity period duration based on said stored distribution.

6. A method as set forth in claim 5, wherein said distribution is stored in a histogram.

7. A method as set forth in claim 6, further comprising the step of updating said histogram during periods that said device is active.

8. A method as set forth in claim 5, further comprising the step of setting an initial waiting period that must elapse before commencing a prediction calculation.

9. A method as set forth in claim 6, further comprising the step of setting an initial waiting period that must elapse before commencing a prediction calculation, wherein said initial waiting period corresponds to a bin resolution of bins of said histogram.

10. A method as set forth in claim 8, wherein said distribution is stored in a histogram, said method further comprising the steps of updating said histogram during periods that said device is active, and dynamically adjusting said initial waiting period in accordance with changes to said stored distribution as said histogram is updated.

11. A method as set forth in claim 10, further comprising the step of setting a predetermined maximum adjustment limit for said initial waiting period.

12. A method as set forth in claim 11, wherein said predetermined maximum adjustment limit corresponds to the longest inactivity duration stored in said histogram.

13. A method as set forth in claim 5, wherein said calculating step comprises solving the following equation:

$P_{t_0}<br><br>\left(s\right)=\frac{\underset{t=t_0+s}{\overset{N}{\sum <br><br>}}<br><br>b<br><br>\left[t\right]}{\underset{t=t_0}{\overset{N}{\sum <br><br>}}<br><br>b<br><br>\left[t\right]}$

wherein P_t(s) is the probability that a residual silent period of inactivity is s given that a silent period of to has already occurred.

14. A method as set forth in claim 13, wherein the residual silent period of inactivity s is calculated according to the following equation:

q_t0(α

)=max{s|P_t0(s)≧

α

}=s*

which is the α

^th quantile of the distribution of T_s−

t₀ given T_s≧

t₀, wherein T_s is a random variable representing the length of a silent period of inactivity, and s* corresponds to the predicted value of s.

15. A mobile apparatus, comprising:

a network interface;

a processor; and

a power supply that turns off power to said network interface in response to a signal from said processor generated in accordance with real-time monitoring by said processor of network activity through said network interface and prediction of silent period durations during which no network activity is present in said interface based on said real-time monitoring;

wherein said power supply turns off power to said network interface for a duration substantially corresponding to a predicted silent period duration.

16. The mobile apparatus of claim 15, wherein said network interface is a WiFi network interface.

17. The mobile apparatus of claim 15, further comprising a storage medium that stores a distribution of durations of said silent periods.

18. The mobile apparatus of claim 17, wherein said distribution is stored in the form of a histogram.

19. The mobile apparatus of claim 17, wherein said storage medium further stores processor-executable instructions for calculating a probability of a predicted silent period duration based on said distribution.

20. The mobile apparatus of claim 17, wherein said storage medium further stores a predetermined adjustable waiting time period that must elapse before said processor predicts a silent period duration.

21. The mobile apparatus of claim 20, further including a maximum value to which said predetermined adjustable waiting time period may be adjusted.

22. The mobile apparatus of claim 21, wherein said maximum value corresponds to the longest silent period duration in said distribution.