Soft Computing

, Volume 21, Issue 18, pp 5223–5233 | Cite as

Low-power sensing model considering context transition for location-based services

  • Jae-Hyeon Park
  • Deok-ki Kim
  • Dusan Baek
  • Jung-Won Lee


Many previous studies have addressed the provision of sustainable context awareness. However, they did not consider the transition between contexts and instead handled each context individually. In other words, they neglected the relationship between contexts, which can be perceived during the transition of contexts, and instead determined the context using only the value output from a sensor. As a result, although the contexts inferred during the transition are meaningless, the service consumes unnecessary power trying to be aware of these contexts. Individual context awareness for Indoor/Outdoor contexts is a representative example of this. The Indoor/Outdoor contexts should not be inferred concurrently. However, the existing services infer each context independently, so they cannot prevent power wastage when two contexts are inferred at once. For this, there is a need to consider the contexts that could simultaneously occur during context transition in order to increase the power efficiency of a context-aware service. To this end, we propose a low-power sensing model capable of considering context transition for a location-based service. In our method, we generate a context-aware model capable of considering context transition based on the activity of sensors and identify the unstable state in which context-aware services do not infer the context and therefore drain the power inefficiently. Then, by adapting the freezing method proposed in this paper to the UNSTABLE state, we block the activation of the sensors to improve the power efficiency until certain conditions are satisfied. On applying our method to context-aware services for Indoor/Outdoor contexts, we were able to improve the power efficiency by 60% in the UNSTABLE state.


Context awareness Low power Sensing model Android mobile application Indoor/outdoor detection 



This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2016-0-00309) supervised by the IITP (Institute for Information & communications Technology Promotion).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.


  1. Abowd GD, Dey AK, Brown PJ, Davies N, Smith M, Steggles P (1999) Towards a better understanding of on text and context-awareness. In: Gellersen HW (ed) Handheld and ubiquitous computing, HUC 1999. Lecture Notes in Computer Science, vol 1707. Springer, Berlin, HeidelbergGoogle Scholar
  2. Contreras G, Martonosi M (2005) Power prediction for intel XScale processors using performance monitoring unit events. In: ISLPED’05. Proceedings of the 2005 international symposium on low power electronics and design, 2005, IEEE, pp 221–226Google Scholar
  3. Dai W, Liu JJ, Korthaus A (2014) Dynamic on-demand solution delivery based on a context-aware services management framework. Int J Grid Util Comput 26 5(1):33–49CrossRefGoogle Scholar
  4. Hammoudi S, Monfort V, Camp O (2015) Model driven development of user-centred context aware services. Int J Space Based Situat Comput 5(2):100–114CrossRefGoogle Scholar
  5. Hao S, Li D, Halfond WG, Govindan R (2013) Estimating mobile application energy consumption using program analysis. In: 2013 35th international conference on software engineering (ICSE) IEEE, pp 92–101Google Scholar
  6. Jung W, Kang C, Yoon C, Kim D, Cha H (2012) DevScope: a nonintrusive and online power analysis tool for smartphone hardware components. In: Proceedings of the eighth IEEE/ACM/IFIP international conference on hardware/software codesign and system synthesis, ACM, pp 353–362Google Scholar
  7. Lee S, Jung W, Chon Y, Cha H (2015) EnTrack: a system facility for analyzing energy consumption of Android system services. In: Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing, ACM, pp 1–202Google Scholar
  8. Liu Y, Xu C, Cheung SC, L J (2014) Greendroid: automated diagnosis of energy inefficiency for smartphone applications. IEEE Trans Softw Eng 40(9):911–940CrossRefGoogle Scholar
  9. Liu Y, Xu C, Cheung SC (2013) Where has my battery gone? Finding sensor related energy black holes in smartphone applications. In 2013 IEEE international conference on pervasive computing and communications (PerCom), IEEE, pp 2–10Google Scholar
  10. Mikhaylov K, Tervonen J (2012) Energy-efficient routing in wireless sensor networks using power-source type identification. Int J Space Based Situat Comput 2(4):253–266CrossRefGoogle Scholar
  11. Nikzad N, Chipara O, Griswold WG (2014) APE: an annotation language and middleware for energy-efficient mobile application development. In: Proceedings of the 36th international conference on software engineering, ACM, pp 515–526Google Scholar
  12. Paek J, Kim J, Govindan R (2010) Energy-efficient rate-adaptive GPS-based positioning for smartphones. In: Proceedings of the 8th international conference on mobile systems, applications, and services, ACM, pp 299–314Google Scholar
  13. Park J-H, Choi K-Y, Kim K-A, Lee J-W (2015) Development of power measurement system in accordance with the state changes of GPS using location APIs. In: Conference of the KIPSGoogle Scholar
  14. Pathak A, Hu YC, Zhang M (2011) Bootstrapping energy debugging on smartphones: a first look at energy bugs in mobile devices. In: Proceedings of the 10th ACM workshop on hot topics in networks, ACM, p 5Google Scholar
  15. Pathak A, Hu YC, Zhang M (2012) Where is the energy spent inside my app?: fine grained energy accounting on smartphones with Eprof. In: Proceedings of the 7th ACM European conference on computer systems, ACM, pp 29–42Google Scholar
  16. Perera C, Zaslavsky A, Christen P, Georgakopoulos D (2014) Context aware computing for the internet of things: a survey communications surveys & tutorials. IEEE 16(1):414–454Google Scholar
  17. Sankaran K, Zhu M, Guo XF, Ananda AL, Chan MC, Peh LS (2014) Using mobile phone barometer for low-power transportation context detection. In: Proceedings of the 12th ACM conference on embedded network sensor systems, ACM, pp 191–205Google Scholar
  18. Singh K, Bhadauria M, McKee SA (2009) Real time power estimation and thread scheduling via performance counters. ACM SIGARCH Comput Arch News 37(2):46–55CrossRefGoogle Scholar
  19. Zhang L, Tiwana B, Qian Z, Wang Z, Dick RP, Mao ZM, Yang L (2010). Accurate online power estimation and automatic battery behavior based power model generation for smartphones. In: Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis, ACM, pp 105–114Google Scholar
  20. Zhang L, Gordon MS, Dick RP, Mao ZM, Dinda P, Yang L (2012) Adel: an automatic detector of energy leaks for smartphone applications. In: Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis, ACM, pp 363–372Google Scholar
  21. Zhang L, Pathak PH, Wu M, Zhao Y, Mohapatra P (2015) Accelword: energy efficient hotword detection through accelerometer. In: Proceedings of the 13th annual international conference on mobile systems, applications, and services, ACM, pp 301–315Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringAjou UniversitySuwon-siKorea

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