Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-745bb68f8f-mzp66 Total loading time: 0 Render date: 2025-02-05T19:40:30.726Z Has data issue: false hasContentIssue false

15 - Example scenarios for energy optimization

from Part III - Advanced energy optimization

Published online by Cambridge University Press:  05 August 2014

Sasu Tarkoma ,
Matti Siekkinen ,
Eemil Lagerspetz  and
Yu Xiao
Sasu Tarkoma
Affiliation:
University of Helsinki
Matti Siekkinen
Affiliation:
Aalto University, Finland
Eemil Lagerspetz
Affiliation:
University of Helsinki
Yu Xiao
Affiliation:
Aalto University, Finland
Get access

Summary

We now turn our attention from individual optimization techniques to applications. We investigate a few different cases where the principles and techniques we have learned earlier can be applied in mobile applications.

We first look at a specific application, namely video streaming, which is one of the most important internet applications today, from the mobile internet's perspective. We first study the way that video streaming consumes energy and illustrate that through measurement results from real systems. We then cover different strategies that can be used to save energy in video streaming. It turns out that there are a few things that need to be taken into account when applying generic energy-saving techniques to mobile video streaming.

The next two examples are not really specific applications but rather integral parts of many applications and, therefore, they represent extremely important cross-application scenarios. The first of those is sensing. Sensing is a hot research topic at the moment and it is expected to become a very important part of smartphone applications. We study ways to reduce energy consumption with applications that require different kinds of sensing by exploring separately each category of sensors included in modern smartphones. We focus on two well-established techniques: sensor selection and duty cycling.

The second cross-application energy optimization scenario is security. We look at the energy overhead caused by security protocols and algorithms, based on measurement studies. Then, we discuss whether and when it is possible to find a tradeoff between the level of security and energy consumption.

Type
Chapter
Information
Smartphone Energy Consumption
Modeling and Optimization
 , pp. 297 - 314
Publisher: Cambridge University Press
Print publication year: 2014

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

[1] Cisco, “Cisco visual networking index: Forecast and methodology, 2012-2017,” Cisco, White Paper, May 2013.
[2] M. A., Hoque, M., Siekkinen, J. K., Nurminen, and M., Aalto, “Dissecting mobile video ser¬vices: An energy consumption perspective,” in Proc. 14th IEEE Int. Sym. on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), Jun. 2013.Google Scholar
[3] T., Stockhammer, “Dynamic adaptive streaming over http: standards and design principles,” in Proc. 2nd Annu. ACM Conf. on Multimedia Systems. New York, NY, USA: ACM, 2011, pp. 133–144.Google Scholar
[4] A., Finamore, M., Mellia, M. M., Munafo, R., Torres, and S. G., Rao, “YouTube everywhere: impact of device and infrastructure synergies on user experience,” in Proc. 2011 ACM SIG-COMM Conf. on Internet Measurement, ser. IMC '11. New York, NY, USA: ACM, 2011, pp. 345–360.Google Scholar
[5] M. A., Hoque, M., Siekkinen, and J. K., Nurminen, “Using crowd-sourced viewing statistics to save energy in wireless video streaming,” in Proc. 19th Annu. Int. Conf. on Mobile Comput¬ing and Networking. New York, NY, USA: ACM, 2013, pp. 377–388. [Online]. Available: http://doi.acm.org/10.1145/2500423.2500427Google Scholar
[6] M., Hoque, M., Siekkinen, and J. K., Nurminen, “TCP receive buffer aware wireless multime¬dia streaming - an energy efficient approach,” in Proc. 23rd ACM Workshop on Network and Operating Systems Support for Digital Audio and Video. ACM, 2013, pp. 13–18.Google Scholar
[7] M., Hoque, M., Siekkinen, and J., Nurminen, “Energy efficient multimedia streaming to mobile devices: A survey,” IEEE Communications Surveys Tutorials, vol. PP, no. 99, pp. 1–19, 2012.Google Scholar
[8] S., Mohapatra, R., Cornea, N., Dutt, A., Nicolau, and N., Venkatasubramanian, “Integrated power management for video streaming to mobile handheld devices,” in Proc. 11th ACMInt. Conf. on Multimedia. New York, NY, USA: ACM, 2003, pp. 582–591. [Online]. Available: http://doi.acm.org/10.1145/957013.957134Google Scholar
[9] Y., Li, M., Reisslein, and C., Chakrabarti, “Energy-efficient video transmission over a wireless link,” IEEE Transactions Veh. Technol., vol. 58, no. 3, pp. 1229–1244, 2009.Google Scholar
[10] S., Madgwick, A. J. L., Harrison, and R., Vaidyanathan, “Estimation of IMU and MARG orientation using a gradient descent algorithm,” in 2011 IEEE Int. Conf. on Rehabilitation Robotics (ICORR), 2011, pp. 1–7.Google Scholar
[11] D., Figo, P. C., Diniz, D. R., Ferreira, and J. a. M., Cardoso, “Preprocessing techniques for context recognition from accelerometer data,” Personal Ubiquitous Comput., vol. 14, no. 7, pp. 645–662, Oct. 2010. [Online]. Available: http://dx.doi.org/10.1007/s00779-010-0293-9Google Scholar
[12] Z., Yan, V., Subbaraju, D., Chakraborty, A., Misra, and K., Aberer, “Energy-efficient continuous activity recognition on mobile phones: An activity-adaptive approach,” in Proc. 2012 16th Annu. Int. Symp. on Wearable Computers (ISWC), ser. ISWC '12. Washington, DC, USA: IEEE Computer Society, 2012, pp. 17–24. [Online]. Available: http://dx.doi.org/10.1109/ISWC.2012.23Google Scholar
[13] H., Eren, S., Makinist, E., Akin, and A., Yilmaz, “Estimating driving behavior by a smart¬phone,” in 2012 IEEE Intelligent Vehicles Symp. (IV), 2012, pp. 234–239.Google Scholar
[14] J., Paek, J., Kim, and R., Govindan, “Energy-efficient rate-adaptive GPS-based positioning for smartphones,” in Proc. 8th Int. Conf. on Mobile Systems, Applications, and Services,ser. MobiSys '10. New York, NY, USA: ACM, 2010, pp. 299–314. [Online]. Available: http:// doi.acm.org/10.1145/1814433.1814463Google Scholar
[15] M. B., Kjœrgaard, S., Bhattacharya, H., Blunck, and P., Nurmi, “Energy-efficient trajectory tracking for mobile devices,” in Proc. 9th Int. Conf. on Mobile Systems, Applications, and Services, ser. MobiSys '11. New York, NY, USA: ACM, 2011, pp. 307–320. [Online]. Available: http://doi.acm.org/10.1145/1999995.2000025Google Scholar
[16] H., Lu, W., Pan, N. D., Lane, T., Choudhury, and A. T., Campbell, “Soundsense: Scalable sound sensing for people-centric applications on mobile phones,” in Proc. 7th Int. Conf. on Mobile Systems, Applications, and Services, ser. MobiSys '09. New York, NY, USA: ACM, 2009, pp. 165–178. [Online]. Available: http://doi.acm.org/10.1145/1555816.1555834Google Scholar
[17] G., Raffa, J., Lee, L., Nachman, and J., Song, “Don't slow me down: Bringing energy efficiency to continuous gesture recognition,” in 2010 International Symp. on Wearable Computers (ISWC), 2010, pp. 1–8.Google Scholar
[18] H., Lu, J., Yang, Z., Liu, N. D., Lane, T., Choudhury, and A. T., Campbell, “The Jigsaw contin¬uous sensing engine for mobile phone applications,” in Proc. 8th ACM Conf. on Embedded Networked Sensor Systems, ser. SenSys '10. New York, NY, USA: ACM, 2010, pp. 71–84. [Online]. Available: http://doi.acm.org/10.1145/1869983.1869992Google Scholar
[19] W., Stallings, Cryptography and Network Security: Principles and Practice, 5th ed. Upper Saddle River, NJ, USA: Prentice Hall Press, 2010.Google Scholar
[20] N., Potlapally, S., Ravi, A., Raghunathan, and N., Jha, “A study of the energy consumption characteristics of cryptographic algorithms and security protocols,” IEEE Trans. on Mobile Computing, vol. 5, no. 2, pp. 128–143, 2006.Google Scholar
[21] P., Miranda, M., Siekkinen, and H., Waris, “TLS and energy consumption on a mobile device: A measurement study,” in 2011 IEEE Symp. on Computers and Communications (ISCC), 2011, pp. 983–989.Google Scholar
[22] A. S., Wander, N., Gura, H., Eberle, V., Gupta, and S. C., Shantz, “Energy analysis of public-key cryptography for wireless sensor networks,” in Proc. 3rd IEEE Int. Conf. on Pervasive Computing and Communications. Washington, DC, USA: IEEE Computer Society, 2005, pp. 324–328.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×