Context-aware battery management for mobile phones [email protected] N. Ravi et al., Conf. on IEEE International Pervasive Computing and Communications,

Context-aware battery management for mobile phones

이시혁

[email protected]

N. Ravi et al., Conf. on IEEE International Pervasive Computing and Communica-tions,

pp. 224-233, 2008.

S FT COMPUTING @ YONSEI UNIV . KOREA16

Contents

• Background• Proposed system • CABMAN system design

– Overview– Sysem-specific components– Charging opportunity predictor– Charging opportunity predictor(cont.)– Call time predictor– Battery life time predictor– Viceroy and user interface

• Evaluation– Environment– Charging opportunity predictor– Call time predictor– Battery-lifetime predictor

• Discussion & Conclusions


Background

• Improving rapidly for mobile device(such as smartphone)– Processing power– Storage capacities– Graphics– High-speed connectivity

• Faced battery capacities – Not experiencing the exponential growth curve as other technologies– Remaining the key bottleneck for mobile devices in the near future

3


Background

• Current solutions– “Battery low” audio signal – A remaing time estimate at current power consumption– User interface : unchanged for a number of years

• The reasons for need to be changed– Convergence makes more multi-functional computing devices – WLAN interfaces are relatively hungry consumers of energy– Pervasive computing applications have provided reasons for mobile

devices to be executing always-on background applications

4


Proposed system

• CABMAN (Context-Aware Battery MANagement architecture for mobile device)

• Battery management architecture– Crucial applications to users should not be compromised by non-

crucial applications. – The opportunities for charging should be predicted instead of using

absolute battery level as the guide– Context can be used to predict charging opportunities

• Goal for system– the next charging opportunity– the call time requirements of the user over a period of time (assum-

ing that telephony is the most critical application)– the “discharge speedup factor” of the set of non-crucial applications

running.

5


Overview

• 3-categories– System-specific monitors– Predictors– The viceroy/UI

• Consist of 8-components

6

The viceroy/UI

Monitors

Predictors

CABMAN system design


• Context monitor : sensing and storing context information

• Call monitor : log communication– incoming/outcoming calls– Incoming/outgoing SMSs

• Process monitor : tracks the processes running on the device

• Battery monitor : probe and enquire about remaining charge and voltage level

7

Sysem-specific componentsCABMAN system design


Charging opportunity predictor

8


• Determine the charging opportunity for crucial application– True, CABMAN should not inconvenience the user with unnecessary

warnings or actions– False, If the phone battery if relatively full, CABMAN should warn the

user that they risk a dead battery

• Location sensing– A way of inferring charging opportunity– Disadvantage of using only location

:it does not accommodate for mobile

chargers(such as car)

– Additional context information• Time- of-day• Speed• Presence of other wireless devices• Charge-logs


Charging opportunity predictor(cont.)

9


9

• Cell-based charging opportunity predictior– Dectection of other beacon type

• Wifi• APs

– Direct positioning information• GPS• A-GPS

– Detecting the id of the current cell (e.g. those at home or perhaps the work place)

• marking the cells in which this normally occurs• if the user often “refuses”, then the cell can be unmarked

• Examples– Currnet samples : ABC– History : DEABCFG

A B

C

E

D

F

G


Call time predictor

• To protect the availability of telephony– Crucial application– The call time needs of the user should be predicted

• Methods to predict the call time– Static : Ask to the user, set a minimum call time level – Dynamic : find the average of number of minutes of call time

(each hour of the day)– Hybrid : Static+Dynamic

10



Battery life time predictor

• Difficult to predict battery life time– Different chemistry of the battery– Use of the applications with different battery demand

• Measure the base curve in idle mode– New laptop– Old laptop– HP iPAQ

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Linear Non-linear spiky


Viceroy and user interface

• Viceroy : CABMAN’s central component– Continually monitor whether the bettery lifetime prediction– combined with the battery requirement of the estimated call time re-

quirement from the call time predictor– means that the battery will expire before or after the next charging

opportunity

• Warning t > r−f(m)

t: an estimation of the time interval before

the next charging opportunity surfaces

r: an estimate of the remaining battery Lifetime

m : an estimate of the required calltime

f(m): the map from call time to battery lifetime.

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Environment

• CABMAN prototype– Linux – Symbian OS

• MIT’s Reality Mining project – charging opportunity predictor – call time predictor– gathered by deploying Nokia 6600 phones – 80 subjects for around nine months

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Evaluation


Charging opportunity predictor

• Settings– Half the subjects : single charging station– Other half : two charging stations

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Evaluation


Call time predictor

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Evaluation

weekdays weekends

The length of phone calls The number of calls made during each hour


Battery-lifetime predictor

• Base curve together with discharge curves (actual and derived)

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Evaluation

for the new HP laptop Old Dell laptop

HP iPAQ

Comparing accuracy of our algorithm with ACPIs


Discussion & Conclusions

• Charging-opportunity predictor and call-time predictor perform reasonably well for an average user whose life entropy is not very high.

• Unfixed charging place (e.g. car)

• Describe three key components of CABMAN:– The use of context information such as location to predict the next

charging opportunity– More accurate battery life prediction based on a discharge speedup

factor– The notion of crucial applications such as telephony

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Documents

Context-aware battery management for mobile phones [email protected] N. Ravi et al., Conf. on IEEE International Pervasive Computing and Communications,