Abstract
Nowadays LTE network provides high speed data transmission for advanced web based applications and consumes lot of energy. Energy consumption needs to be minimized for a mobile devices running on batteries. In this paper, we have considered a problem of minimizing total energy consumption of a mobile device transmitting an image to cloud through LTE network with a specific bit error rate requirement. Total energy consumption per information bit is calculated by measuring computation energy, circuit energy and radio energy. We have also proposed an algorithm to minimize the total energy consumption while transferring the image. The algorithm makes use of the information entropy of the segments that the image contains. We have also given the complexity of the algorithm in terms of the total number of required operations. As compared to the energy required for transferring the fully uncompressed or fully compressed image, our optimized algorithm can save upto 26 % energy. Energy saving depends on transmission distance and required bit error rate.
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Addis, B., Capone, A., Carello, G., Gianoli, L., & Sanso, B. (2014). Energy management through optimized routing and device powering for greener communication networks. IEEE/ACM Transactions on Networking, 22(1), 313–325.
Deruyck, M., Tanghe, E., Joseph, W., Vereecken, W., Pickavet, M., Martens, L., et al. (2011). Model for power consumption of wireless access networks. IET Science, Measurement Technology, 5(4), 155–161.
Huang, J., Qian, F., Gerber, A., Mao, Z. M., Sen, S., & Spatscheck, O. (2012). A close examination of performance and power characteristics of 4G LTE networks. In Proceedings of the 10th international conference on mobile systems, applications, and services.
G.T.. 3GPP, http://www.3gpp.org/specifications/releases/73-release-7 (Release 7).
Khan, A. R., Othman, M., Madani, S. A., & Khan, S. U. (2014). A survey of mobile cloud computing application models. IEEE Communications Surveys Tutorials, 16(1), 393–413.
Vallina-Rodriguez, N., & Crowcroft, J. (2013). Energy management techniques in modern mobile handsets. IEEE Communications Surveys Tutorials, 15(1), 179–198.
Haberland, B., Derakhshan, F., Grob-Lipski, H., Klotsche, R., Rehm, W., Schefczik, P., et al. (2013). Radio base stations in the cloud. Journal of Bell Labs Technical, 18(1), 129–153.
Wang, X., Chen, M., Kwon, T. T., Yang, L. T., & Leung, V. C. M. (2013). Amescloud: A framework of adaptive mobile video streaming and efficient social video sharing in the clouds. IEEE Transactions on Multimedia, 15(4), 811–820.
Lei, X., Liao, X., Huang, T., Li, H., & Hu, C. (2013). Outsourcing large matrix inversion computation to a public cloud. IEEE Transactions on Cloud Computing, 1(1), 78–87.
Kansal, A., Zhao, F., Liu, J., Kothari, N., & Bhattacharya, A. (2010). Virtual machine power metering and provisioning. In Proceedings of the ACM symposium on cloud computing. Association for Computing Machinery.
Kansal, A., & Zhao, F. (2008). Fine-grained energy profiling for power-aware application design. In Proceedings of first workshop on hot topics in measurement and modeling of computer systems (HotMetrics08) at ACM sigmetrics. Association for Computing Machinery, Inc., Annapolis, MD, USA.
Cui, S., Goldsmith, A. J., & Bahai, A. (2004). Energy-efficiency of mimo and cooperative mimo techniques in sensor networks. IEEE Journal on Selected Areas in Communications, 22(6), 1089–1098.
Flinn, J., Park, S.Y., & Satyanarayanan, M. (2002). Balancing performance, energy, and quality in pervasive computing. In Proceedings of the 22nd international conference on distributed computing systems, pp. 217–226.
Chouhan, S., Bose, R., & Balakrishnan, M. (2009). Integrated energy analysis of error correcting codes and modulation for energy efficient wireless sensor nodes. IEEE Transactions on Wireless Communications, 8(10), 5348–5355.
Dai, M., Lu, Z., Shen, D., Wang, H., Chen, B., Lin, X., et al. (2016) Design of (4, 8) Binary code with MDS and zigzag-decodable property. Wireless Personal Communications, 89(1), 1–13.
Leung, S.O., Chan, K.L., & Fung, P.W. (1993). Compression techniques for still image and motion video. Proceedings of IEEE region 10 conference on computer, communication, control and power engineering, Vol. 3.
Wallace, G. (1992). The jpeg still picture compression standard. IEEE Transactions on Consumer Electronics, 38(1), 18–199.
Myung, H. G., Lim, J., & Goodman, D. J. (2006). Single carrier FDMA for uplink wireless transmission. IEEE Vehicular Technology Magazine, 1(3), xvii–xxxiv.
Mittal, A., Bose, R., & Shevgaonkar, R. (2014). Power analysis of lte system for uplink scenario. Proceedings of twentieth national conference on communications (NCC), pp. 1–4.
Cui, S., Goldsmith, A. J., & Bahai, A. (2005). Energy-constrained modulation optimization. IEEE Transactions on Wireless Communications, 4(5), 2349–2360.
Goldsmith, A. (2005). Wireless communications. Cambridge: Cambridge University Press.
Bose, R. (2008). Information theory, coding and cryptography (2nd ed.). New Delhi: Tata McGraw-Hill Education.
Pun, T. (1980). A new method for grey-level picture thresholding using the entropy of the histogram. Signal Processing, 2(3), 223–237.
Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(4), 623–656.
Image Databases. (2015). http://imageprocessingplace.com.
Imari, M. A., Xiao, P., Imran, M. A., & Tafazolli, R. (2013). Low complexity subcarrier and power allocation algorithm for uplink ofdma systems. Eurasip Journal on Wireless Communications and Networking, 2013(1), 1–6.
Mehrnia, A., & Daneshrad, B. (2004). Minimizing power consumption and complexity in a programmable transmit filter bank for ofdm. In Proceedings of the 2004 international symposium on low power electronics and design. ISLPED ’04, pp. 230–235.
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms (3rd ed.). Cambridge: The MIT Press.
Cho, N. I., & Lee, S. U. (1991). Fast algorithm and implementation of 2-d discrete cosine transform. IEEE Transactions on Circuits and Systems, 38(3), 297–305.
Hasan, M., Nur, K. M., & Noor, T. B. (2012). Computational complexity reduction of jpeg images. International Journal of Scientific & Technology Research, 1(4), 72–75.
Grosse, H. J., Varley, M., Terrell, T., & Chan, Y. (1997). Hardware implementation of versatile zigzag-reordering algorithm for adaptive jpeg-like image compression schemes. In Proceedings of the sixth international conference on image processing and its applications (Vol. 1, pp. 184–188).
Huffman, D. (1952). A method for the construction of minimum-redundancy codes. Proceedings of the IRE, 40, 1098–1101.
Proakis, J. G. (2001). Digital communications (4th ed.). New York: McGraw-Hill.
Proakis, J. G., & Manolakis, D. G. (1996). Digital signal processing principles, algorithms, and applications (3rd ed.). Upper Saddle River, NJ: Prentice-Hall International, Inc.
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Mittal, A., Kundu, C., Bose, R. et al. Entropy Based Image Segmentation for Energy Efficient LTE System with Cloud. Wireless Pers Commun 92, 1145–1162 (2017). https://doi.org/10.1007/s11277-016-3598-9
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DOI: https://doi.org/10.1007/s11277-016-3598-9