Multimedia Systems

, Volume 21, Issue 1, pp 87–101 | Cite as

The power of smartphones

  • Feng Xia
  • Ching-Hsien Hsu
  • Xiaojing Liu
  • Haifeng Liu
  • Fangwei Ding
  • Wei Zhang
Regular Paper


Smartphones have been shipped with multiple wireless network interfaces to meet their diverse communication and networking demands. However, as smartphones increasingly rely on wireless network connections to realize more functions, the demand of energy has been significantly increased, which has become the limit for people to explore smartphones’ real power. In this paper, we first review typical smartphone computing systems, energy consumption of smartphone, and state-of-the-art techniques of energy saving for smartphones. Then, we propose a location-assisted Wi-Fi discovery scheme, which discovers the nearest Wi-Fi network access points (APs) using the user’s location information. This allows the user to switch to the Wi-Fi interface in an intelligent manner when he/she arrives at the nearest Wi-Fi network AP. Thus, we can meet the user’s bandwidth needs and provide the best connectivity. Additionally, it avoids the long periods in idle state and greatly reduces the number of unnecessary Wi-Fi scans on the mobile device. Our experiments and simulations demonstrate that our scheme effectively saves energy for smartphones integrated with Wi-Fi and cellular interfaces.


Smartphone Power consumption Access point discovery Energy saving 



This work was partially supported by Liaoning Provincial Natural Science Foundation of China under Grant No. 201202032, the Fundamental Research Funds for Central Universities (DUT12JR10), and the Innovation Fund of School of Software, Dalian University of Technology.


  1. 1.
    Iftode, L., Borcea, C., Ravi, N., Kang, P., Zhou, P.: Smart phone: an embedded system for universal interactions. In: Proceedings of the 10th IEEE International Workshop on Future Trends of Distributed Computing Systems, pp. 88–94. Suzhou, China (2004)Google Scholar
  2. 2.
    Wang, Y., Lin, J., Annavaram, M., Jacobson, Q.Q., Hong, J., Krishnamachari, B., Sadeh, N.: (2009) A framework of energy-efficient mobile sensing for automatic user state recognition. In: Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services, pp. 179–192. Kraków, PolandGoogle Scholar
  3. 3.
    Horanont, T., Shibasaki, R.: An implementation of mobile sensing for large-scale urban monitoring. In: Proceedings of International Workshop on Urban, Community, and Social Applications of Networked Sensing Systems, pp. 51–55. Raleigh, NC, USA (2008)Google Scholar
  4. 4.
    Ishida, Y., Konomi, S., Thepvilojanapong, N., Suzuki, R., Sezaki, K., Tobe, Y.: An implicit and user-modifiable urban sensing environment. In: Proceedings of International Workshop on Urban, Community, and Social Applications of Networked Sensing Systems, pp. 36–40. Raleigh, NC, USA (2008)Google Scholar
  5. 5.
    O’Hara, K., Kindberg, T., Glancy, M., Baptista, L., Sukumaran, B., Kahana, G., Rowbotham, J.: Collecting and sharing location-based content on mobile phones in a zoo visitor experience. CSCW 16, 11–44 (2007)Google Scholar
  6. 6.
    Michel, S., Salehi, A., Luo, L., Dawes, N., Aberer, K., Barrenetxea, G., Bavay, M., Kansal, A., Kumar, K.A., Nath, S., et al.: Environmental monitoring 2.0. In: Proceedings of the 25th International Conference on Data Engineering, pp. 1507–1510. Shanghai, China (2009)Google Scholar
  7. 7.
    Hoh, B., Gruteserand, M., Herring, R., Ban, J., Work, D., Herrera, J., Bayen, A.M., Annavaram, M., Jacobson, Q.: Virtual trip lines for distributed privacy-preserving traffic monitoring. In: Proceedings of 6th Annual International Conference on Mobile Systems, Applications and Services, pp. 15–28. Breckenridge, CO, USA (2008)Google Scholar
  8. 8.
    Jones, V., Gay, V., Leijdekkers, P.: Body sensor networks for mobile health monitoring: experience in Europe and Australia. In: Proceedings of the 4th International Conference on Digital Society, pp. 204–209. St. Maarten, Netherlands Antilles (2010)Google Scholar
  9. 9.
    Taylor, I.M., Labrador, M.A.: Improving the energy consumption in mobile phones by filtering noisy GPS fixes with modified Kalman filters. In: 2011 IEEE Wireless Communications and Networking Conference (WCNC), pp. 2006–2011. Quantana-Roo, Mexico, (2011)Google Scholar
  10. 10.
    Xiao, Y., Bhaumik, R., Yang, Z., Siekkinen, M., Savolainen, P., Ylä-Jääski, A.A.: System-level model for runtime power estimation on mobile devices. In: 2010 IEEE/ACM International Conference on Green Computing and Communications (GreenCom) & 2010 IEEE/ACM International Conference on Cyber, Physical and Social Computing (CPSCom), pp. 27–34. Hangzhou, China (2010)Google Scholar
  11. 11.
    Rao, R., Vrudhula, S., Rakhmatov, D.N.: Battery modeling for energy aware system design. Computer 36, 77–87 (2003)CrossRefGoogle Scholar
  12. 12.
    Rahmati, A., Zhong, L.: Context-for-wireless: context-sensitive energy-efficient wireless data transfer. In: Proceedings of The 5th International Conference on Mobile Systems, Applications, and Services, pp. 165-178. San Juan, Puerto Rico (2007)Google Scholar
  13. 13.
    Ananthanarayanan G., Stoica, I.: Blue-Fi: enhancing Wi-Fi performance using bluetooth signals. In: Proceedings of The 7th International Conference on Mobile Systems, Applications, and Services, pp. 249–261. Kraków, Poland (2009)Google Scholar
  14. 14.
    Rozner, E., Navda, V.: NAPman: network-assisted power management for WiFi devices. In: Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services (MobiSys ‘10), pp. 91–105. San Francisco, CA, USA (2010)Google Scholar
  15. 15.
    Zhou, R., Xiong, Y., Xing, G., Sun, L., Ma, J.: ZiFi: wireless LAN discovery via ZigBee interference signatures. In: Proceedings of the 16th Annual International Conference on Mobile Computing and Networking (MobiCom ‘10), pp. 49–60. Chicago, IL, USA (2010)Google Scholar
  16. 16.
    Constandache, I., Gaonkar, S., Sayler, M., Choudhury, R.R., Cox, L.: EnLoc: Energy-Efficient Localization for Mobile Phones, pp. 2716–2720. INFOCOM, Rio de Jaeiro (2009)Google Scholar
  17. 17.
    Sha, K., Zhan, G., Shi, W., Lumley, M., Wiholm, C., Arnetz, B.: Spa: a smartphone assisted chronic illness self-management system with participatory sensing. In: Proceedings of the 2nd International Workshop on Systems and Networking Support for Health Care and Assisted Living Environments, pp. 5:1–5:3. Breckenridge, CO, USA (2008)Google Scholar
  18. 18.
    Jarvinen, P., Jarvinen, T.H., Lahteenmaki, L., Sodergard, C.: HyperFit: hybrid media in personal nutrition and exercise management. In: Proceedings of 2nd International Conference on Pervasive Computing Technologies for Healthcare, pp. 222–226. Tampere, Finland (2008)Google Scholar
  19. 19.
    Denning, T., Andrew, A., Chaudhri, R., Hartung, C., Lester, J., Borriello, G., Duncan, G.: Ba-lance: towards a usable pervasive wellness application with accurate activity inference. In: Proceedings of the 10th Workshop on Mobile Computing Systems and Applications, p. 5. Santa Cruz, CA, USA (2009)Google Scholar
  20. 20.
    Sashima, A., Inoue, Y., Ikeda, T., Yamashita, T., Kurumatani, K.: Consorts-s: a mobile sensing platform for context-aware services. In: Proceedings of International Conference on Intelligent Sensors, Sensor Networks and Information 2008, pp. 417–422. Sydney, Australia (2008)Google Scholar
  21. 21.
    Mun, M., Reddy, S., Shilton, K., Yau, N., Burke, J., Estrin, D., Hansen, M., Howard, E., West, R., Boda, P.: Peir, the personal environmental impact report, as a platform for participatory sensing systems research. In: Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services, pp. 55–68. Kraków, Poland (2009)Google Scholar
  22. 22.
    Maisonneuve, N., Stevens, M., Niessen, M.E., Steels, L.: Noisetube: measuring and mapping noise pollution with mobile phones. Inf. Technol. Environ. Eng. 2, 215–228 (2009)Google Scholar
  23. 23.
    Kanjo, E., Benford, S., Paxton, M., Chamberlain, A., Fraser, D.S., Woodgate, D., Crellin, D., Woolard, A.: MobGeoSen: facilitating personal geosensor data collection and visualization using mobile phones. Pers. Ubiquit. Comput. 12, 599–607 (2008)CrossRefGoogle Scholar
  24. 24.
    Bilandzic, M., Banholzer, M., Peev, D., Georgiev, V., Balagtas-Fernandez, F., De Luca, A.: Laermometer: a mobile noise mapping application. In: Proceedings of the 5th Nordic conference on Human-computer interaction: building bridges, pp. 415–418. Lund, Sweden (2008)Google Scholar
  25. 25.
    Thiagarajan, A., Ravindranath, L., LaCurts, K., Madden, S., Balakrishnan, H., Toledo, S., Eriksson, J.: Vtrack: accurate, energy-aware road traffic delay estimation using mobile phones. In: Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, pp. 85–98. Berkeley, CA, USA (2009)Google Scholar
  26. 26.
    Mohan, P., Padmanabhan, V.N., Ramjee, R.: Nericell: rich monitoring of road and traffic conditions using mobile smartphones. In: Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, pp. 323–336. Raleigh, NC, USA (2008)Google Scholar
  27. 27.
    Rachuri, K.K., Musolesi, M., Mascolo, C., Rentfrow, P.J., Longworth, C., Aucinas, A.: Emotionsense: a mobile phones based adaptive platform for experimental social psychology research. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing, pp. 281–290. Copenhagen, Denmark (2010)Google Scholar
  28. 28.
    Kwapisz, J.R., Weiss, G.M., Moore, S.A.: Activity recognition using cell phone accelerometers. ACM SIGKDD Explor. Newsl. 12, 74–82 (2010)CrossRefGoogle Scholar
  29. 29.
    Miluzzo, E., Lane, N.D., Eisenman, S.B., Campbell, A.T.: Cenceme: injecting sensing presence into social networking applications. Smart Sens. Context 4793, 1–28 (2007)CrossRefGoogle Scholar
  30. 30.
    Das, T., Mohan, P., Padmanabhan, V.N., Ramjee, R., Sharma, A.: Prism: platform for remote sensing using smartphones. In: Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services, pp. 63–76. San Francisco, CA, USA (2010)Google Scholar
  31. 31.
    Anand, A., Manikopoulos, C., Jones, Q., Borcea, C.A.: Quantitative analysis of power consumption for location-aware applications on smart phones. In: Proceedings of IEEE International Symposium on Industrial Electronics, pp. 1986–1991. Vigo, Spain (2007)Google Scholar
  32. 32.
    Carroll, A., Heiser, G.: An analysis of power consumption in a smartphone. In: Proceedings of the 2010 USENIX Annual Technical Conference, p. 21. Boston, MA, USA (2010)Google Scholar
  33. 33.
    Zhang, T., Madhani, S., Gurung, P., van den Berg, E.: Reducing energy consumption on mobile devices with WiFi interfaces. In: Proceedings of Global Telecommunications Conference (Globecom 05), pp. 561–565. St. Louis, MO, USA (2005)Google Scholar
  34. 34.
    Wu, H., Tan, K., Liu, J., Zhang, Y.: Footprint: cellular assisted Wi-Fi AP discovery on mobile phones for energy saving, WiNTECH’09 (MobiCom workshop), Beijing, China, pp. 67–75. (2009)Google Scholar
  35. 35.
    Pering, T., Agarwal, Y., Gupta, R., Want, R.: Coolspots: reducing the power consumption of wireless mobile devices with multiple radio interfaces. In: Proceedings of The Fourth International Conference on Mobile Systems, Applications, and Services, pp. 220–232. Uppsala, Sweden (2006)Google Scholar
  36. 36.
    Linden, D., Reddy, T.: Handbook of Batteries, 3rd edn. McGraw-Hill, New York, NY (2001)Google Scholar
  37. 37.
    Fuller, T.F., Doyle, M., Newman, J.: Simulation and optimization of the dual lithium ion insertion cell. J. Electrochem. Soc. 141, 1–10 (1994)CrossRefGoogle Scholar
  38. 38.
    Chiasserini, C.F., Rao, R.R.: Pulsed battery discharge in communication devices. MobiCom ‘99, pp. 88–95. Seattle, WA, USA (1999)Google Scholar
  39. 39.
    Manwell, J., McGowan, J.: Lead acid battery storage model for hybrid energy systems. Sol. Energy 50, 399–405 (1993)CrossRefGoogle Scholar
  40. 40.
    Martin, T.L.: Balancing batteries, power, and performance: system issues in CPU speed-setting for mobile computing. Ph. D. Thesis, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA (1999)Google Scholar
  41. 41.
    Rakhmatov, D., Vrudhula, S.: Energy management for battery-powered embedded systems. ACM Trans. Embed. Comput. Syst. 2, 277–324 (2003)CrossRefGoogle Scholar
  42. 42.
    Shih, E., Bahl, P., Sinclair, M.J.: Wake on wireless: an event driven energy saving strategy for battery operated devices. In: Proceedings of The 8th Annual International Conference on Mobile Computing and Networking (MobiCom ‘02), pp. 160–171. Atlanta, GA, USA (2002)Google Scholar
  43. 43.
    Agarwal, Y., Schurgers, C., Gupta, R.: Dynamic power management using on demand paging for networked embedded systems. In: Proceedings of The 2005 Conference on Asia and South Pacific Design Automation (ASP-DAC ‘05), pp. 755–759. Yokohama, Japan (2005)Google Scholar
  44. 44.
    Agarwal, Y., Chandra, R., Wolman, A., Bahl, P., Gupta, R.: Wireless wakeups revisited: energy management for voip over wi-fi smartphones. In: Proceedings of The 5th International Conference on Mobile Systems, Applications, and Services, pp. 179–191. San Juan, Puerto Rico (2007)Google Scholar
  45. 45.
    Brakmo, L.S., Wallach, D.A., Viredazand, M.A.: μSleep: a technique for reducing energy consumption in handheld devices. In: Proceedings of the 2nd International Conference on Mobile Systems, Applications, and Services, pp. 12–22. Zurich, Switzerland (2004)Google Scholar
  46. 46.
    Pathak, A., Hu, Y.C., Zhang, M., Bahl, P., Wang, Y.-M.: Enabling automatic offloading of resource-intensive smartphone applications. In: ECE Technical Reports, Purdue University, TR-ECE-11-13 (2011)Google Scholar
  47. 47.
    Xian, C., Lu, Y.-H., Li, Z.: Adaptive computation offloading for energy conservation on battery-powered systems. In: Proceedings of International Conference on Parallel and Distributed Systems, pp. 1–8. Hsinchu, Taiwan (2007)Google Scholar
  48. 48.
    Kumar, K., Lu, Y.-H.: Cloud computing for mobile users: can offloading computation save energy. Computer 43, 51–56 (2010)CrossRefGoogle Scholar
  49. 49.
    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 Application for Mobile Handhelds, pp. 61–62. Barcelona, Spain (2009)Google Scholar
  50. 50.
    Kang, S., Lee, J., Jang, H., Lee, H., Lee, Y., Park, S., Park, T., Song, J.: SeeMon: scalable and energy-efficient context monitoring framework for sensor-rich mobile environments. In: Proceedings of the International Conference on Mobile Systems, Applications, and Services, pp. 267–280. Breckenridge, CO, USA (2008)Google Scholar
  51. 51.
    Priyantha, B., Lymberopoulos, D., Liu, J.: Little rock: enabling energy-efficient continuous sensing on mobile phones. IEEE Pervasive Comput. 10, 12–15 (2011)CrossRefGoogle Scholar
  52. 52.
    Zhong, L., Jha, N.K.: Graphical user interface energy characterization for handheld computers. In: Proceedings of the 2003 International Conference on compilers, Architecture and Synthesis for Embedded Systems, pp. 232–242. San Jose, CA, USA (2003)Google Scholar
  53. 53.
    Tiwari, V., Malik, S., Wolfe, A.: Compilation techniques for low energy: an overview. In: IEEE Symposium on Low Power Electronics, pp. 38–39. San Diego, CA, USA (1994)Google Scholar
  54. 54.
    Aho, A.V., Sethi, R., Ullman, J.D.: Compilers, Principles, Techniques and Tools. Addison Wesley, Boston, MA (1988)Google Scholar
  55. 55.
    Myers, B.A., Rosson, M.B.: Survey on user interface programming. In: Proceedings of ACM Conference on Human Factors in Computing Systems, pp. 195–202. Monterey, CA, USA (1992)Google Scholar
  56. 56.
    Yan, X., Sekercioglu, A., Narayanan, S.: A survey of vertical handover decision algorithms in Fourth Generation heterogeneous wireless networks. Comput. Netw. 54, 1848–1863 (2010)CrossRefzbMATHGoogle Scholar
  57. 57.
    Chamodrakas, I., Martakos, D.: A utility-based fuzzy TOPSIS method for energy efficient network selection in heterogeneous wireless networks. Appl. Soft Comput. 11, 3734–3743 (2011)CrossRefGoogle Scholar
  58. 58.
    Chamodrakas, I., Leftheriotis, I., Martakos, D.: In-depth analysis and simulation study of an innovative fuzzy approach for ranking alternatives in multiple attribute decision making problems based on TOPSIS. Appl. Soft Comput. 11, 900–907 (2011)CrossRefGoogle Scholar
  59. 59.
    Ding, F., Xia, F., Zhang, W., Zhao, X., Ma, C.: Monitoring energy consumption of smartphones. In: Proceedings of 1st International Workshop on Sensing, Networking, and Computing with Smartphones (PhoneCom), pp. 610–613. Dalian, China (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Feng Xia
    • 1
  • Ching-Hsien Hsu
    • 2
  • Xiaojing Liu
    • 1
  • Haifeng Liu
    • 1
  • Fangwei Ding
    • 1
  • Wei Zhang
    • 1
  1. 1.School of SoftwareDalian University of TechnologyDalianChina
  2. 2.Department of Computer Science and Information EngineeringChung Hua UniversityHsinchuTaiwan

Personalised recommendations