Energy-Efficient GPS Usage in Location-Based Applications

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 701)

Abstract

GPS is one of the most used services in any location-based app in our smartphone, and almost a quarter of all Android apps available in the Google Play store are using this GPS. There are many apps which require monitoring your locations in a continuous fashion because of the application’s nature, and those kinds of apps consume the highest power from the smartphones. Because of the high-power draining nature of this GPS, we hesitate to take part in different crowd-sourced applications which are very much important for the smart city realization as maximum of these applications use GPS in real time or in a very frequent manner for the realization of participatory sensing in a smart city scenario. To resolve this, we have introduced an energy-efficient context-aware approach which utilizes user’s mobility information from the user’s context and as well smartphone’s sensing values from the inbuilt accelerometer, magnetometer, and gyroscope of the smartphone to provide us a very close estimation of the present location of the user without using continuous GPS. It is an energy-efficient solution without sacrificing the accuracy compared to energy saving which will boost the crowd to take part in the smartphone-based crowd-sourced applications that depend on participatory sensing for the smart city environment.

Keywords

Energy efficient GPS Location estimation Sensor fusion Smart city 

Notes

Acknowledgements

The research work of the first author is funded by “Visvesvaraya PhD Scheme, Ministry of Communications & IT, Government of India”.

References

  1. 1.
    Abdesslem, F.B., Phillips, A., Henderson T.: Less is more: energy-efficient mobile sensing with senseless. In Proceedings of the 1st ACM workshop on Networking, Systems, and Applications for Mobile Handhelds (2009)Google Scholar
  2. 2.
    Singhal, T., Harit, A., Vishwakarma, D.N.: Kalman filter implementation on an accelerometer sensor data for three state estimation of a dynamic system. Int. J. Res. Eng. Technol. (2012)Google Scholar
  3. 3.
    Muthohar, M.F., Nugraha, I.G.D., Choi, D.: Exploring significant motion sensor for energy-efficient continuous motion and location sampling in mobile sensing application. Int. J. Technol. 38–49 (2016)CrossRefGoogle Scholar
  4. 4.
    Kjærgaard, M.B., Langdal, J., Godsk, T., Toftkjær, T.: Entracked: energy-efficient robust position tracking for mobile devices. In: Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services. ACM. New York. USA (2009) 221–234Google Scholar
  5. 5.
    Paek, J., Kim, J., Govindan, R.: Energy-efficient rate-adaptive GPS-based positioning for smartphones. In Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services. ACM. New York. USA (2010) 299–314Google Scholar
  6. 6.
    Dutta, J., Gazi, F., Roy, S., Chowdhury, C.: AirSense: opportunistic crowd-sensing based air quality monitoring system for smart city. In: Proceedings of IEEE Sensors. Orlando, FL, USA (2016)  https://doi.org/10.1109/icsens.2016.7808730
  7. 7.
    Dutta, J., Chowdhury, C., Roy, S., Middya, A.I., Gazi, F.: Towards smart city: sensing air quality in city based on opportunistic crowd-sensing. In: Proceedings of the 18th International Conference on Distributed Computing and Networking. Hyderabad, India. ACM. (2017)  https://doi.org/10.1145/3007748.3018286
  8. 8.
    Dutta, J., Roy, S.: IoT-fog-cloud based architecture for smart city: prototype of a smart building. In: Proceedings of 7th International Conference on Cloud Computing, Data Science & Engineering. Noida, India, pp. 237–242 (2017).  https://doi.org/10.1109/confluence.2017.7943156
  9. 9.
    Liu, J., et al.: CO-GPS: energy efficient GPS sensing with cloud offloading. IEEE Trans. Mob. Comput. 15(6), 1348–1361 (2016).  https://doi.org/10.1109/TMC.2015.2446461CrossRefGoogle Scholar
  10. 10.
    Taylor, I. M., Labrador, M. A.: Improving the energy consumption in mobile phones by filtering noisy GPS fixes with modified Kalman filters. IEEE Wireless Communications and Networking Conference, Cancun, Quintana Roo, Mexico 2006–2011. (2011).  https://doi.org/10.1109/wcnc.2011.5779437
  11. 11.
    Khaleghi, B., El-Ghazal, A., Hilal, A. R., Toonstra, J., Miners, W. B., Basir O. A.: Opportunistic calibration of smartphone orientation in a vehicle. In: IEEE 16th International Symposium on a World of Wireless, Mobile and Multimedia Networks. Boston, MA, USA (2015).  https://doi.org/10.1109/wowmom.2015.7158210
  12. 12.
    Abyarjoo, F. et al.: Implementing a sensor fusion algorithm for 3D orientation detection with inertial/magnetic sensors. Innovations and Advances in Computing, Informatics, Systems Sciences, Networking and Engineering. Springer. Cham pp. 305–310 (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia

Personalised recommendations