Context-aware battery management
for mobile phones
N. Ravi et al., Conf. on IEEE International Pervasive Computing and Communications,
pp. 224-233, 2008.
이시혁
theshy@sclab.yonsei.ac.kr
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 noncrucial 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
(assuming that telephony is the most critical application)
– the “discharge speedup factor” of the set of non-crucial applications
running.
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CABMAN system design
Overview
• 3-categories
– System-specific monitors
– Predictors
– The viceroy/UI
• Consist of 8-components
The viceroy/UI
Predictors
Monitors
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CABMAN system design
Sysem-specific components
• 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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CABMAN system design
Charging opportunity predictor
• 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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CABMAN system design
Charging opportunity predictor(cont.)
• 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
G
• Examples
– Currnet samples : ABC
– History : DEABCFG
F
E
D
A
B
C
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CABMAN system design
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
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
Linear
Non-linear
spiky
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CABMAN system design
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
requirement 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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Evaluation
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
weekdays
The length of phone calls
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weekends
The number of calls
made during each hour
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Evaluation
Battery-lifetime predictor
• Base curve together with discharge curves (actual and derived)
for the new HP laptop
Old Dell laptop
Comparing accuracy of our algorithm with ACPIs
HP iPAQ
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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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