Abstract




 
   

IJE TRANSACTIONS C: Aspects Vol. 31, No. 12 (December 2018) 2037-2043   

PDF URL: http://www.ije.ir/Vol31/No12/C/7-2962.pdf  
downloaded Downloaded: 30   viewed Viewed: 193

  ANALYSE POWER CONSUMPTION BY MOBILE APPLICATIONS USING FUZZY CLUSTERING APPROACH
 
D. Mehrotra, D. Nagpal, R. Srivastava and R. Nagpal
 
( Received: May 31, 2018 – Accepted in Revised Form: October 26, 2018 )
 
 

Abstract    With the advancements in mobile technology and its utilization in every facet of life, mobile popularity has enhanced exponentially. The biggest constraint in the utility of mobile devices is that they are powered with batteries. Optimizing mobile’s size and weight is always the choice of designer, which led limited size and capacity of battery used in mobile phone. In this paper analysis of the energy consumption of some popular mobile apps is done using data mining technique. A large variety of mobile apps with differently functionality are executed on a smart phone. The power consumption of these apps is measured using Power Tutor. For holistic analysis these mobile apps are executed in different environment, which are created by varying the setting and internet facilities. Fuzzy Clustering approach is used to club the mobile apps based on similarity of the behaviour with respect to power consumption. Power consumption behaviour for each cluster and apps lying in overlapping zone is discussed in detail. The study gives the insight that power need of an app is dependent on the environment and code which can be used by app developers for creating an optimized energy app.

 

Keywords    Power Consumption; Mobile Applications; Green Mining; Fuzzy Clustering; Power Tutor

 

چکیده   

با پیشرفت در فن آوری تلفن همراه و استفاده از آن در هر جنبه ای از زندگی، محبوبیت تلفن همراه به طور نمادین افزایش یافته است. بزرگترین محدودیت در استفاده از دستگاه های تلفن همراه این است که آنها با باتری شارژ می شوند. بهینه سازی اندازه و وزن تلفن همراه همیشه انتخاب طراح است، که منجر به اندازه محدود و ظرفیت باتری مورد استفاده در تلفن همراه می شود. در این مقاله، تجزیه و تحلیل مصرف انرژی برخی از برنامه های محبوب تلفن همراه با استفاده از تکنیک داده کاوی انجام می شود. طیف گسترده ای از برنامه های تلفن همراه با عملکرد متفاوت بر روی یک تلفن هوشمند اجرا می شود. مصرف برق این برنامه ها با استفاده از Power Tutor اندازه گیری می شود. برای تجزیه و تحلیل جامع این برنامه های تلفن همراه در محیط های مختلف اجرا می شوند، که با تغییر تنظیمات و امکانات اینترنت ایجاد می شوند. رویکرد خوشه بندی فازی برای برنامه های تلفن همراه بر اساس شباهت رفتار با توجه به مصرف انرژی استفاده می شود. رفتار مصرف برق برای هر خوشه و برنامه های موجود در منطقه همپوشانی به طور دقیق مورد بحث قرار می گیرد. این مطالعه بینش را نشان می دهد که نیاز به یک برنامه به محیط و کد بستگی دارد که می تواند توسط برنامه های توسعه دهنده برای ایجاد یک برنامه انرژی بهینه استفاده شود.

References   

1. Zamfiroiu, A., "Factors influencing the quality of mobile applications", Informatica Economica,  Vol. 18, No. 1, (2014), 131.
2. Carroll, A. and Heiser, G., "An analysis of power consumption in a smartphone", in USENIX annual technical conference, Boston, MA. Vol. 14, (2010), 21-21.
3. Metri, G., Agrawal, A., Peri, R. and Shi, W., "What is eating up battery life on my smartphone: A case study", in Energy Aware Computing, 2012 International Conference on, IEEE. (2012), 1-6.
4. Wilke, C., Richly, S., Püschel, G., Piechnick, C., Götz, S. and Aßmann, U., "Energy labels for mobile applications", in GI-Jahrestagung., (2012), 412-426.
5. Bedregal, J.C.V. and Gutierrez, E.G.C., "Optimizing energy consumption per application in mobile devices", in Information Society (i-Society), 2013 International Conference on, IEEE. (2013), 106-110.
6. Chowdhury, S.A. and Hindle, A., "Greenoracle: Estimating software energy consumption with energy measurement corpora", in Mining Software Repositories (MSR), IEEE/ACM 13th Working Conference on, IEEE., (2016), 49-60.
7. Chowdhury, S.A., Gil, S., Romansky, S. and Hindle, A., Greenscaler: Automatically training software energy model with big data. 2016, PeerJ Preprints.
8. Dao, T.A., Singh, I., Madhyastha, H.V., Krishnamurthy, S.V., Cao, G. and Mohapatra, P., "Tide: A user-centric tool for identifying energy hungry applications on smartphones", IEEE/ACM Transactions on Networking,  Vol. 25, No. 3, (2017), 1459-1474.
9. Bao, L., Lo, D., Xia, X., Wang, X. and Tian, C., "How android app developers manage power consumption?: An empirical study by mining power management commits", in Proceedings of the 13th International Conference on Mining Software Repositories, ACM. (2016), 37-48.
10. Li, D., Hao, S., Gui, J. and Halfond, W.G., "An empirical study of the energy consumption of android applications", in Software Maintenance and Evolution (ICSME), IEEE International Conference on, IEEE. (2014), 121-130.
11. Liu, Y., Xu, C., Cheung, S.-C. and Lu, J., "Greendroid: Automated diagnosis of energy inefficiency for smartphone applications", IEEE Transactions on Software Engineering,  Vol., No. 1, (2014), 1-1.
12. Balasubramanian, N., Balasubramanian, A. and Venkataramani, A., "Energy consumption in mobile phones: A measurement study and implications for network applications", in Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement, ACM., (2009), 280-293.
13. Sun, L., Sheshadri, R.K., Zheng, W. and Koutsonikolas, D., "Modeling wifi active power/energy consumption in smartphones", in Distributed Computing Systems (ICDCS), IEEE 34th International Conference on, IEEE. (2014), 41-51.
14. Di Nucci, D., Palomba, F., Prota, A., Panichella, A., Zaidman, A. and De Lucia, A., "Software-based energy profiling of android apps: Simple, efficient and reliable?", in Software Analysis, Evolution and Reengineering (SANER), IEEE 24th International Conference on, IEEE. (2017), 103-114.
15. Djedidi, O., Djeziri, M., M'Sirdi, N. and Naamane, A., "A novel easy-to-construct power model for embedded and mobile systems-using recursive neural nets to estimate power consumption of arm-based embedded systems and mobile devices", in 15th International Conference on Informatics in Control, Automation and Robotics, SCITEPRESS-Science and Technology Publications., (2018).
16. Ahmad, R.W., Naveed, A., Rodrigues, J.J., Gani, A., Madani, S.A., Shuja, J., Maqsood, T. and Saeed, S., "Enhancement and assessment of a code-analysis-based energy estimation framework", IEEE Systems Journal,  (2018).
17. Mehrotra, D., Srivastava, R., Nagpal, R. and Nagpal, D., "Multiclass classification of mobile applications as per energy consumption", Journal of King Saud University-Computer and Information Sciences,  (2018).
18. Halkidi, M., Vazirgiannis, M. and Batistakis, Y., "Quality scheme assessment in the clustering process", in European Conference on Principles of Data Mining and Knowledge Discovery, Springer., (2000), 265-276.
19. Brock, G., Pihur, V., Datta, S. and Datta, S., "Clvalid, an r package for cluster validation", Journal of Statistical Software,  (2011), https://cran.cnr.berkeley.edu/web/packages/clValid/vignettes/clValid.pdf. 


Download PDF 



International Journal of Engineering
E-mail: office@ije.ir
Web Site: http://www.ije.ir