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Transcript of Context-aware battery management for mobile phones [email protected] N. Ravi et al., Conf....
Context-aware battery management for mobile phones
이시혁
N. Ravi et al., Conf. on IEEE International Pervasive Computing and Communica-tions,
pp. 224-233, 2008.
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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
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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
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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
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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.
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Overview
• 3-categories– System-specific monitors– Predictors– The viceroy/UI
• Consist of 8-components
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The viceroy/UI
Monitors
Predictors
CABMAN system design
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• 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
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Sysem-specific componentsCABMAN system design
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Charging opportunity predictor
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CABMAN system design
• 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
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Charging opportunity predictor(cont.)
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CABMAN system design
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• 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
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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
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CABMAN system design
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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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CABMAN system design
Linear Non-linear spiky
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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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CABMAN system design
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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
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Charging opportunity predictor
• Settings– Half the subjects : single charging station– Other half : two charging stations
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Evaluation
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Call time predictor
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Evaluation
weekdays weekends
The length of phone calls The number of calls made during each hour
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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
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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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