1. Context-aware battery management for mobile phones theshy@sclab.yonsei.ac.kr N. Ravi et al., Conf. on IEEE International Pervasive Computing and Communications, pp. 224-233, 2008.

2. S FT COMPUTING @ YONSEI UNIV. KOREA 16 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

3. S FT COMPUTING @ YONSEI UNIV. KOREA 16 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 2

4. S FT COMPUTING @ YONSEI UNIV. KOREA 16 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 3

5. S FT COMPUTING @ YONSEI UNIV. KOREA 16 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 (assuming that telephony is the most critical application) –the discharge speedup factor of the set of non-crucial applications running. 4

6. S FT COMPUTING @ YONSEI UNIV. KOREA 16 Overview 3-categories –System-specific monitors –Predictors –The viceroy/UI Consist of 8-components 5 The viceroy/UI Monitors Predictors CABMAN system design

7. S FT COMPUTING @ YONSEI UNIV. KOREA 16 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 6 Sysem-specific components CABMAN system design

8. S FT COMPUTING @ YONSEI UNIV. KOREA 16 Charging opportunity predictor 7 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

9. S FT COMPUTING @ YONSEI UNIV. KOREA 16 Charging opportunity predictor(cont.) 8 CABMAN system design 8 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

10. S FT COMPUTING @ YONSEI UNIV. KOREA 16 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 9 CABMAN system design

11. S FT COMPUTING @ YONSEI UNIV. KOREA 16 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 10 CABMAN system design LinearNon-linearspiky

12. S FT COMPUTING @ YONSEI UNIV. KOREA 16 Viceroy and user interface Viceroy : CABMANs 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 > rf(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. 11 CABMAN system design

13. S FT COMPUTING @ YONSEI UNIV. KOREA 16 Environment CABMAN prototype –Linux –Symbian OS MITs Reality Mining project –charging opportunity predictor –call time predictor –gathered by deploying Nokia 6600 phones –80 subjects for around nine months 12Evaluation

14. S FT COMPUTING @ YONSEI UNIV. KOREA 16 Charging opportunity predictor Settings –Half the subjects : single charging station –Other half : two charging stations 13Evaluation

15. S FT COMPUTING @ YONSEI UNIV. KOREA 16 Call time predictor 14Evaluation weekdaysweekends The length of phone calls The number of calls made during each hour

16. S FT COMPUTING @ YONSEI UNIV. KOREA 16 Battery-lifetime predictor Base curve together with discharge curves (actual and derived) 15Evaluation for the new HP laptopOld Dell laptop HP iPAQ Comparing accuracy of our algorithm with ACPIs

17. S FT COMPUTING @ YONSEI UNIV. KOREA 16 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 16