JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Sensing Model for Reducing Power Consumption for Indoor/Outdoor Context Transition
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Journal of KIISE
  • Volume 43, Issue 7,  2016, pp.763-772
  • Publisher : Korean Institute of Information Scientists and Engineers
  • DOI : 10.5626/JOK.2016.43.7.763
 Title & Authors
Sensing Model for Reducing Power Consumption for Indoor/Outdoor Context Transition
Kim, Deok-Ki; Park, Jae-Hyeon; Lee, Jung-Won;
 
 Abstract
With the spread of smartphones containing multiple on-board sensors, the market for context aware applications have grown. However, due to the limited power capacity of a smartphone, users feel discontented QoS. Additionally, context aware applications require the utilization of many forms of context and sensing information. If context transition has occurred, types of needed sensors must be changed and each sensor modules need to turn on/off. In addition, excessive sensing has been found when the context decision is ambiguous. In this paper, we focus on power consumption associated with the context transition that occurs during indoor/outdoor detection, modeling the activities of the sensor associated with these contexts. And we suggest a freezing algorithm that reduces power consumption in context transition. We experiment with a commercial application that service is indoor/outdoor location tracking, measure power consumption in context transition with and without the utilization of the proposed method. We find that proposed method reduces power consumption about 20% during context transition.
 Keywords
context aware application;low-power sensing method;indoor/outdoor detection;mobile devices;
 Language
Korean
 Cited by
 References
1.
H. J. Kim, J. M. Yoo, C. W. Park, A. Y. Kim, J. W. Lee, "Trends of Mobile App. Analytics Plaform," Electronics and Telecommunications Trends, Vol. 29, No. 1, pp. 50-60, 2014. (in Korean)

2.
P. Georgiev, N. D. Lane, K. K. Rachuri, C. Mascolo, "DSP.ear: Leveraging Co-Processor Support for Continuous Audio Sensing on Smartphones," Sensys '14 Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems, pp. 295-309, 2014.

3.
H. Shen, A. Balasubramanian, A. LaMarca, D. Wetherall, "Enhancing Mobile Apps To Use Sensor Hubs Without Programmer Effort," Proc. of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '15), pp. 227-238, 2015.

4.
Y. Liu, C. Xu, S. C. Cheung, "Where has my battery gone? Finding sensor related energy black holes in smartphone applications," 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom), pp. 2-10, 2013.

5.
D. Li, A. H. Tran, W. G. J. Halfond, "Making web applications more energy efficient for OLED smartphones," Proc. of the 36th International Conference on Software Engineering (ICSE 2014), pp. 527-538, 2014.

6.
I. Manotas, L.Pollock, J. Clause, "SEEDS: a software engineer's energy-optimization decision support framework," Proc. of the 36th International Conference on Software Engineering (ICSE 2014), pp. 503-514, 2014.

7.
S. Park, D. Kim, H. Cha, "Reducing Energy Consumption of Alarm-induced Wake-ups on Android Smartphones," Proc. of the 16th International Workshop on Mobile Computing Systems and Applications (HotMobile '15), pp. 33-38, 2015.

8.
L. Xhang, P. H. Pathak, M. Wu, Y. Zhao, P. Mogapatra, "AccelWord: Energy Efficient Hotword Detection through Accelerometer," Proc. of the 13th Annual International Conference on Mobile Systems, Applications, and Services(MobiSys '15), pp. 301-315, 2015.

9.
S. He, Y. Liu, H. Zhou, "Optimizing Smartphone Power Consumption through Dynamic Resolution Scaling," Proc. of the 21st Annual International Conference on Mobile Computing and Networking (MobiCom '15), pp. 27-39, 2015

10.
G. D. Abowd, A. K. Dey, P. J. Brown, N. Davies, M. Smith, P. Steggles, "Towards a Better Understanding of Context and Context-Awareness," Proc. of the 1st international symposium on Handheld and Ubiquitous Computing(HUC '99), pp. 304-307, 1999.

11.
C. Perera, A. Xaslavsky, P. Christen, D. Georgakopoulos, "Context Aware Computing for The Internet of Things: A Survey," Communications Surveys & Tutorials, IEEE, Vol. 16, Issue. 1, pp. 414-454, 2013.

12.
P. Zhou, et al., "IODetector: a generic service for indoor outdoor detection," Proc. of 10th acm conference on embedded network sensor systems, pp. 113-126, 2012.

13.
R. Valentin, P. Katsikouli, R. Sarkar, M. K. Marina, "A Semi-Supervised Learning Approach for Robust Indoor-Outdoor Detection with Smartphones," Proc. of the 12th ACM Conference on Embedded Network Sensor Systems, pp. 280-294, 2014.

14.
O. Canovas, et al., "WiFiBoost: a terminal-based method for detection of indoor/outdoor places," Proc. of the 11th International Conference on Mobile and Ubiquitous Systems: Computin, Networking and Services, 2014.

15.
J. Cheon, "Development of Testing of BMT Model for Evaluating Power Consumption of Mobile Context-Aware Application," Master's Thesis, Ajou University, Suwon, Republic of Korea, 2015.

16.
K. Choi, J. Lee, "Portable Power Measurement System for Mobile Devices," Journal of KIISE : Computing Practices and Letters, Vol. 20, No. 3, pp. 131-142, 2014 (in Korean)

17.
(2015, Nov. 30) GeoLog, [Online]. Available: https://play.google.com/store/apps/details?id=eu.chainfire.geolog (downloaded 2015, Nov. 8)

18.
(2015. Nov. 30) Android Open Source Project, [Online]. Available: https://source.android.com/