Abstract




 
   

IJE TRANSACTIONS C: Aspects Vol. 31, No. 12 (December 2018) 1959-1960   

downloaded Downloaded: 0   viewed Viewed: 45

  ANALYSE POWER CONSUMPTION BY MOBILE APPLICATIONS USING FUZZY CLUSTERING APPROACH
 
D. Mehrotra, D. Nagpal, R. Srivastava and R. Nagpal
 
( Received: May 31, 2018 – Accepted: November 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 in their behaviour with respect to power consumption. Power consumption behaviour for each cluster and apps lying in overlapping zone is discussed in detail.

 

Keywords    Power consumption, mobile applications,green mining,fuzzy clustering,power tutor.

 

References    1.       Wilke, C., Richly, S., Püschel, G., Piechnick, C., Götz, S., & Abmann, U. (2012). Energy Labels for Mobile Applications. Proceedings of the First Workshop for the Development of Energy-aware Software (EEbS 2012), Lecture Notes in Informatics, Vol. 208, Gesellschaft für Informatik, Bonn, 412-426. 2.     Zamfiroiu, A. (2014). Factors influencing the quality of mobile applications. Informatica Economica, 18(1), 131-139.3.   Carroll, A., & Heiser, G. (2010). An analysis of power consumption in a smartphone. Proceedings of the USENIX conference. https://www.usenix.org/legacy/event/usenix10/tech/full_papers/Carroll.pdf4. Metri, G., Agrawal, A., Peri, R., & Shi, W. (2012). What is eating up battery life on my SmartPhone: A case study. Proceedings of International Conference on Energy Aware Computing, IEEE. doi: 10.1109/ICEAC.2012.6471003.5.  Bedregal, J. C. V., & Gutierrez, E. G. C. (2013). Optimizing energy consumption per application in mobile devices. Proceedings of International Conference on Information Society (i-Society), 106-110, IEEE.6.  Chowdhury, S. A., & Hindle, A. (2016). Greenoracle: Estimating software energy consumption with energy measurement corpora. Proceedings of the 13th International Conference on Mining Software Repositories (MSR), 49-60. ACM. DOI: 10.1109/MSR.2016.0157. Chowdhury, S. A., Gil, S., Romansky, S., & Hindle, A. (2016). GreenScaler: Automatically training software energy model with big data. PeerJ Preprints, 1-18. DOI 10.7287/PEERJ.PREPRINTS.2419V28. Dao, T. A., Singh, I., Madhyastha, H. V., Krishnamurthy, S. V., Cao, G., & Mohapatra, P. (2017). TIDE: A user-centric tool for identifying energy hungry applications on smartphones. IEEE/ACM Transactions on Networking, 25 (3), 1459-1474. doi: 10.1109/TNET.2016.2639061.9.  Chang, C. T., Lai, J. Z., & Jeng, M. D. (2011). A fuzzy k-means clustering algorithm using cluster center displacement. Journal of Information Science and Engineering, 27, 995-1009. Retreived from http://www.iis.sinica.edu.tw/page/jise/2011/201105_12.pdf10.  Huang, Z., & Ng, M. K. (1999). A fuzzy k-modes algorithm for clustering categorical data. IEEE Transactions on Fuzzy Systems, 7(4), pp. 446-452.11. Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern recognition letters, 31(8), 651-666.12. Gosain, A., & Dahiya, S. (2016). Performance Analysis of Various Fuzzy Clustering Algorithms: A Review. Procedia Computer Science, 79,100-111.13. Yang, M. S. (1993). A survey of fuzzy clustering. Mathematical and Computer modelling, 18(11), 1-16.14.  Kaufman, L. and Rousseeuw, P.J. (2005). Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley, Hoboken. http://dx.doi.org/10.1002/9780470316801.ch115. Almeida, H., Guedes, D., Meira, W., & Zaki, M. J. (2011, September). Is there a best quality metric for graph clusters?. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 44-59). Springer, Berlin, Heidelberg.16.  Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20, 53-65.17.   R-Tool. https://www.r-project.org/18. Halkidi, M., Vazirgiannis, M., & Batistakis, Y. (2000). Quality scheme assessment in the clustering process. Lecture Notes in Computer Science book Series on Principles of Data Mining and Knowledge Discovery, PKDD 2000, Springer, Berlin, Heidelberg, 265-276. Doi: https://doi.org/10.1007/3-540-45372-5_2619. Tang, Y., Sun, F., & Sun, Z. (2005). Improved validation index for fuzzy clustering. Proceedings of IEEE International Conference on American Control Conference, 1120-1125. doi: 10.1109/ACC.2005.147011120.  Brock, G., Pihur, V., Datta, S., & Datta, S. (2008). clValid, an R package for cluster validation,  Journal of Statistical Software,25(4).1.       Wilke, C., Richly, S., Püschel, G., Piechnick, C., Götz, S., & Abmann, U. (2012). Energy Labels for Mobile Applications. Proceedings of the First Workshop for the Development of Energy-aware Software (EEbS 2012), Lecture Notes in Informatics, Vol. 208, Gesellschaft für Informatik, Bonn, 412-426. 2.       Zamfiroiu, A. (2014). Factors influencing the quality of mobile applications. Informatica Economica, 18(1), 131-139. 3.       Carroll, A., & Heiser, G. (2010). An analysis of power consumption in a smartphone. Proceedings of the USENIX conference. https://www.usenix.org/legacy/event/usenix10/tech/full_papers/Carroll.pdf 4.       Metri, G., Agrawal, A., Peri, R., & Shi, W. (2012). What is eating up battery life on my SmartPhone: A case study. Proceedings of International Conference on Energy Aware Computing, IEEE. doi: 10.1109/ICEAC.2012.6471003. 5.       Bedregal, J. C. V., & Gutierrez, E. G. C. (2013). Optimizing energy consumption per application in mobile devices. Proceedings of International Conference on Information Society (i-Society), 106-110, IEEE. 6.       Chowdhury, S. A., & Hindle, A. (2016). Greenoracle: Estimating software energy consumption with energy measurement corpora. Proceedings of the 13th International Conference on Mining Software Repositories (MSR), 49-60. ACM. DOI: 10.1109/MSR.2016.015 7.       Chowdhury, S. A., Gil, S., Romansky, S., & Hindle, A. (2016). GreenScaler: Automatically training software energy model with big data. PeerJ Preprints, 1-18. DOI 10.7287/PEERJ.PREPRINTS.2419V2 8.       Dao, T. A., Singh, I., Madhyastha, H. V., Krishnamurthy, S. V., Cao, G., & Mohapatra, P. (2017). TIDE: A user-centric tool for identifying energy hungry applications on smartphones. IEEE/ACM Transactions on Networking, 25 (3), 1459-1474. doi: 10.1109/TNET.2016.2639061. 9.       Chang, C. T., Lai, J. Z., & Jeng, M. D. (2011). A fuzzy k-means clustering algorithm using cluster center displacement. Journal of Information Science and Engineering, 27, 995-1009. Retreived from http://www.iis.sinica.edu.tw/page/jise/2011/201105_12.pdf 10.    Huang, Z., & Ng, M. K. (1999). A fuzzy k-modes algorithm for clustering categorical data. IEEE Transactions on Fuzzy Systems, 7(4), pp. 446-452. 11.    Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern recognition letters, 31(8), 651-666. 12.    Gosain, A., & Dahiya, S. (2016). Performance Analysis of Various Fuzzy Clustering Algorithms: A Review. Procedia Computer Science, 79,100-111. 13.    Yang, M. S. (1993). A survey of fuzzy clustering. Mathematical and Computer modelling, 18(11), 1-16. 14.    Kaufman, L. and Rousseeuw, P.J. (2005). Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley, Hoboken. http://dx.doi.org/10.1002/9780470316801.ch1 15.    Almeida, H., Guedes, D., Meira, W., & Zaki, M. J. (2011, September). Is there a best quality metric for graph clusters?. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 44-59). Springer, Berlin, Heidelberg. 16.    Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20, 53-65. 17.    R-Tool. https://www.r-project.org/ 18.    Halkidi, M., Vazirgiannis, M., & Batistakis, Y. (2000). Quality scheme assessment in the clustering process. Lecture Notes in Computer Science book Series on Principles of Data Mining and Knowledge Discovery, PKDD 2000, Springer, Berlin, Heidelberg, 265-276. Doi: https://doi.org/10.1007/3-540-45372-5_26 19.    Tang, Y., Sun, F., & Sun, Z. (2005). Improved validation index for fuzzy clustering. Proceedings of IEEE International Conference on American Control Conference, 1120-1125. doi: 10.1109/ACC.2005.1470111 20.    Brock, G., Pihur, V., Datta, S., & Datta, S. (2008). clValid, an R package for cluster validation,  Journal of Statistical Software,25(4).





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