Skip to main content
SpringerLink
Log in

Are Android Smartphones Ready to Locally Execute Intelligent Algorithms?

  • Conference paper
  • First Online:
Advances in Soft Computing (MICAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10062))

Included in the following conference series:

Abstract

Given that thousands of applications are already available for smartphones, we may be inclined to believe that ubiquitous computing is just around the corner, with online processing in these mobile devices. But, how well prepared is current smartphone technology to support the execution of demanding algorithms? Surprisingly, few researchers have addressed the processing capabilities of currently available smartphones. In this paper we investigate some issues in this direction: we employed twelve algorithms for optimization and classification to profile the computational demands they place on current smartphones. For this purpose, we chose twelve devices that go from low to high-end models, from six different makers, and measured execution time, CPU and RAM usage while the devices were running the algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    We chose the Android platform because it represents over 80% of the market share, and this preponderance is expected to remain in the near future [7].

References

  1. IDC: Worldwide smartphone market posts 11.6% year-over-year growth in Q2 2015, the second highest shipment total for a single quarter, according to IDC (2015). http://www.idc.com/getdoc.jsp?containerId=prUS25804315

  2. Entner, R.: International Comparisons: The Handset Replacement Cycle (2013). http://mobilefuture.org/resources/international-comparisons-the-handset-replacement-cycle-2

  3. Lockhart, J.W., Pulickal, T., Weiss, G.M.: Applications of mobile activity recognition. In: Proceedings of the 2012 ACM Conference on Ubiquitous Computing, UbiComp 2012, pp. 1054–1058. ACM, New York (2012)

    Google Scholar 

  4. Reyes-Ortiz, J.L., Oneto, L., Samà, A., Parra, X., Anguita, D.: Transition-aware human activity recognition using smartphones. Neurocomputing 171, 754–767 (2016)

    Article  Google Scholar 

  5. Chen, S., Xue, M., Tang, Z., Xu, L., Zhu, H.: Stormdroid: a streaminglized machine learning-based system for detecting android malware. In: Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security, pp. 377–388. ACM (2016)

    Google Scholar 

  6. Alammar, J.: Supercharging android apps with tensorflow (2016). https://jalammar.github.io/Supercharging-android-apps-using-tensorflow/

  7. IDC: Smartphone Growth Expected to Drop to Single Digits in 2016, Led by China’s Transition from Developing to Mature Market, According to IDC (2016). http://www.idc.com/getdoc.jsp?containerId=prUS41061616

  8. Loghin, D., Tudor, B.M., Zhang, H., Ooi, B.C., Teo, Y.M.: A performance study of big data on small nodes. Proc. VLDB Endow. 8(7), 762–773 (2015)

    Article  Google Scholar 

  9. Pathak, A., Hu, Y.C., Zhang, M.: 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, pp. 29–42. ACM (2012)

    Google Scholar 

  10. Carroll, A., Heiser, G.: An analysis of power consumption in a smartphone. In: USENIX Annual Technical Conference, vol. 14 (2010)

    Google Scholar 

  11. Gutierrez, A., Dreslinski, R.G., Wenisch, T.F., Mudge, T., Saidi, A., Emmons, C., Paver, N.: Full-system analysis and characterization of interactive smartphone applications. In: 2011 IEEE International Symposium on Workload Characterization (IISWC), pp. 81–90. IEEE (2011)

    Google Scholar 

  12. Papandrea, M.: A smartphone-based energy efficient and intelligent multi-technology system for localization and movement prediction. In: 2012 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), pp. 554–555. IEEE (2012)

    Google Scholar 

  13. IDC: Global market share held by the leading smartphone operating systems in sales to end users from 1st quarter 2009 to 1st quarter 2016 (2016). http://www.statista.com/statistics/266136/global-market-share-held-by-smartphone-operating-systems

  14. Durillo, J.J., Nebro, A.J., Alba, E.: The jMetal framework for multi-objective optimization: design and architecture. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2010)

    Google Scholar 

  15. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2, 27:1–27:27 (2011). Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm

  16. Raff, E.: JSAT: Java statistical analysis tool (2015). https://github.com/EdwardRaff/JSAT

  17. Wang, J., Liu, P., She, M.F., Nahavandi, S., Kouzani, A.: Bag-of-words representation for biomedical time series classification. Biomed. Sig. Process. Control 8(6), 634–644 (2013)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Facultad de Ingeniería, Universidad Autónoma de Chihuahua, 31125, Chihuahua, Mexico

    M. Ricardo Carlos, Fernando Martínez & Luis C. González

  2. CONACYT, Universidad Autónoma de Chihuahua, Chihuahua, Mexico

    Raymundo Cornejo

Authors
  1. M. Ricardo Carlos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fernando Martínez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Raymundo Cornejo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luis C. González
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fernando Martínez .

Editor information

Editors and Affiliations

  1. Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, México DF, Mexico

    Obdulia Pichardo-Lagunas

  2. INFOTEC Aguascalientes, Aguascalientes, Mexico

    Sabino Miranda-Jiménez

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Carlos, M.R., Martínez, F., Cornejo, R., González, L.C. (2017). Are Android Smartphones Ready to Locally Execute Intelligent Algorithms?. In: Pichardo-Lagunas, O., Miranda-Jiménez, S. (eds) Advances in Soft Computing. MICAI 2016. Lecture Notes in Computer Science(), vol 10062. Springer, Cham. https://doi.org/10.1007/978-3-319-62428-0_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-62428-0_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62427-3

  • Online ISBN: 978-3-319-62428-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation