A Squeeze Pack and Transfer Algorithm-based Efficient Framework for Optimized Network Data Transfer in IoT Applications
Keywords:
Lossless compression algorithms, Internet of Things, binary keys, decompression, wireless network, mobile network, data files, lossy compression algorithmsAbstract
The wireless network is the driving force behind the new world's economy, and its invasion has spread far beyond Earth in an interplanetary network using satellites. Wireless networks' productivity must be ur-gently improved because they are prone to signal attenuation, slower transfer speeds, complete signal un-availability due to weather, and overcrowding of connected users. Due to the towers' remote position, ex-cessive latency is also an issue, as is the enormous magnitude of data transfer that comes with the big data revolution. The antiquated TCP protocol, which is ineffective for low consumption and limited storage IoT items as data buffer is insignificant in such devices, is another problem for IoT applications. A novel SPT (Squeeze Pack and Transfer) algorithm has been proposed to increase mobile network productivity and reduce storage by more than 90% for data files. Binary patterns, rather than conventional textual symbols compress data, resulting in a higher compression ratio and faster compression speed. The suggested algo-rithm will significantly improve network performance while also facilitating efficient data transfer for IoT devices with limited storage. Many researchers confront issues such as low compression ratios and re-stricted support for multiple data formats from generic compression. All of these concerns are addressed by the proposed method.
Downloads
References
Euiseok Hwang, "Lossless Data Compression with Bit-back Coding on Massive Smart Meter Data", IEEE, 2022
Adel Mahmoud; Samuel Farid; Mark Maged; Othman Mohamed; Reham Karam; Khaled Salah; M. Watheq El-Kharashi, "An Efficient Hardware Accelerator For Lossless Data Compression", IEEE, 2022
Zhaoyi Sun; Yuliang Huang; Roberto Leonarduzzi; Jie Sun, "A low-complexity destriping method for lossless compression of remote-sensing data", IEEE, 2022
Rani Nandkishor Aher; Mandaar Pande, "Analysis of Lossless Data Compression Algorithm in Columnar Data Warehouse", IEEE, 2022
Xizhe CHENG; Sian–Jheng LIN; Jie SUN, "SortComp (Sort-and-Compress) - Towards a Universal Lossless Compression Scheme for Matrix and Tabular Data", IEEE, 2022
Ge Zhang; Huanyu He; Haiyang Wang; Weiyao Lin, "Integer Network for Cross Platform Graph Data Lossless Compression", IEEE, 2022
Kanemitsu OOTSU, Takashi YOKOTA, Takeshi OHKAWA,"A Consideration on Compres-sion Level Control for Dynamic Compressed Data Transfer Method",IEEE"2016
Jinyan Hu, Shaojing Song, Yumei Gong,"Comparative Performance Analysis of Web Image Compression",IEEE"2017
Masayuki Omote, Kanemitsu Ootsu, Takeshi Ohkawa and Takashi Yokota,"Efficient Data Communication using Dynamic Switching of Compression Method",IEEE"2013
]Hasitha Muthumala Waidyasooriya, Daisuke Ono, Masanori Hariyama and Michitaka Kameyama,"Efficient Data Transfer Scheme Using Word-Pair-Encoding-Based Compres-sion for Large-Scale Text-Data Processing",IEEE"2014
D. Engel and A. Unterweger, "Lossless compression of high-frequency voltage and
current data in smart grids," Proc. IEEE Int. Conf. Big Data, pp. 3131-3139, 2016.
F. Renault, D. Nagamalai and M. Dhanuskodi, "Advances in digital image processing and information technology," Proc. 1st Int. Conf. Digit. Image Process. Pattern Recognit., pp. 23-25, 2011.
Y. Bi, D. Zhang and J. Zhao, "A new data compression algorithm for power quality online monitoring," Proc. Int. Conf. Sustain. Power Gener. Supply, pp. 1-4, 2009.
F. Xiaodong, C. Changling, L. Changling, and S. Huihe, "An improved process data com-pression algorithm," Proc. 4th World Congr. Intell. Control Autom., vol. 3, pp. 2190-2193, 2002.
F. Zhang, L. Cheng, X. Li, Y. Sun, W. Gao and W. Zhao, "Application of a real-time data compression and adapted protocol technique for WAMS," IEEE Trans. Power Syst., vol. 30, no. 2, pp. 653-662, Mar. 2015.
H. Li, N. Sheng, and L. Zhi, "WAMS/PMU data preprocessing and compression," Adv. Mater. Res., vol. 986/987, pp. 1700-1703, Jul. 2014.
J. D. A. Correa, A. S. R. Pinto, C. Montez, and E. Leão, "Swinging door trending compres-sion algorithm for IoT environments," Proc. Companion Proc. 9th SBESC, pp. 143-148, 2019.
J. E. Tate, "Preprocessing and Golomb–Rice encoding for lossless compression of phasor angle data," IEEE Trans. Smart Grid, vol. 7, no. 2, pp. 718-729, Mar. 2016.
J. Uthayakumar, T. Vengattaraman and P. Dhavachelvan, "A survey on data compression techniques: From the perspective of data quality coding schemes datatype and applications," J. King Saud Univ. - Comput. Inf. Sci., vol. 33, pp. 119-140, 2021.
K. Gibson, D. Lee, J. Choi, and A. Sim, "Dynamic online performance optimization in streaming data compression," Proc. IEEE Int. Conf. Big Data, pp. 534-541, 2018.
K. Zhiwu, X. Rui, L. Xianling and Y. Rui, "Research on lossless compression technique based on running-data of the nuclear power plant," Proc. Int. Conf. Comput. Intell. Commun. Netw., pp. 956-959, 2015.
M. A. Khan, J. W. Pierre, J. I. Wold, D. J. Trudnowski, and M. K. Donnelly, "Impacts of swinging door lossy compression of synchrophasor data," Int. J. Elect. Power Energy Syst., vol. 123, 2020.
M. Cui, J. Wang, J. Tan, A. R. Florita and Y. Zhang, "A novel event detection method using PMU data with high precision," IEEE Trans. Power Syst., vol. 34, no. 1, pp. 454-466, Jan. 2019.
M. H. F. Wen and V. O. K. Li, "Optimal phasor data compression unit installation for wide-area measurement systems—An integer linear programming approach," IEEE Trans. Smart Grid, vol. 7, no. 6, pp. 2644-2653, Nov. 2016.
P. H. Gadde, M. Biswal, S. Brahma and H. Cao, "Efficient compression of PMU data in WAMS," IEEE Trans. Smart Grid, vol. 7, no. 5, pp. 2406-2413, Sep. 2016.
R. Jumar, H. Maaß, and V. Hagemeyer, "Comparison of lossless compression schemes for high rate electrical grid time series for smart grid monitoring and analysis," Comput. Elect. Eng., vol. 71, pp. 465-476, 2018.
R. Klump, P. Agarwal, J. E. Tate, and H. Khurana, "Lossless compression of synchronized phasor measurements," Proc. IEEE PES General Meeting, pp. 1-7, 2010.
R. Wenyu, Y. Timothy and N. Klara, "ISAAC: Intelligent synchrophasor data real-time compression framework for WAMS," Proc. IEEE Int. Conf. Smart Grid Commun., pp. 430-436, 2017.
Shiv Preet, Ashish Kr. Luhach, "Comparison of Various Routing and Compression Algo-rithms: A Comparative Study of Various Algorithms in Wireless Networking," Springer, 2016.
Shiv Preet, Ashish Kr. Luhach, Ravindra, "An overview of the Internet of Things and its Research Issues," ``In International Journal of Computer Technology and Applications," IJCTA, 2016.
Shiv Preet, Dr. Amandeep Bagga " Predefined SPT (Squeeze, Pack And Transfer) Key File Update: A Mapreduce Way Of Automatic Key Updates For SPT Algorithm," ICICCT, 2021.
Shiv Preet, Dr. Amandeep Bagga "Satellite Internet Communication: A Race With Con-temporary Optical Fiber Network with the Help of SPT Algorithm," ICTSGS, 2021.
Shiv Preet, Dr. Amandeep Bagga "Squeeze Pack, and Transfer Algorithm: A new over-the-top compression application for Seamless data transfer over the wireless network," IJITEE, 2019.
Shiv Preet, Dr. Amandeep Bagga," Lempel–Ziv–Oberhumer: A critical evaluation of loss-less algorithm and its applications," ICCS, 2018.
W. Wang et al., "Frequency disturbance event detection based on synchrophasors and deep learning," IEEE Trans. Smart Grid, vol. 11, no. 4, pp. 3593-3605, Jul. 2020.
W. Wang, C. Chen, W. Yao, K. Sun, W. Qiu and Y. Liu, "Synchrophasor data compression under disturbance conditions via cross-entropy-based singular value decomposition," IEEE Trans. Ind. Informat., vol. 17, no. 4, pp. 2716-2726, Apr. 2021.
W. Wang, W. Yao, C. Chen, X. Deng, and Y. Liu, "Fast and accurate frequency response estimation for large power system disturbances using the second derivative of frequency da-ta," IEEE Trans. Power Syst., vol. 35, no. 3, pp. 2483-2486, May 2020.
W. Yao et al., "A fast load control system based on mobile distribution-level phasor meas-urement unit," IEEE Trans. Smart Grid, vol. 11, no. 1, pp. 895-904, Jan. 2020.
X. Wang, Y. Liu, and L. Tong, "Adaptive Subband Compression for Streaming of Continu-ous Point-on-Wave and PMU Data," 2021.
Z. Jellali, L. Najjar Atallah and S. Cherif, "Linear prediction for data compression and re-covery enhancement in wireless sensor networks," Proc. Int. Wireless Commun. Mobile Comput. Conf., pp. 779-783, 2016
Vijayalakshmi, V., & Sharmila, K. (2023). Secure Data Transactions based on Hash Coded Starvation Blockchain Security using Padded Ring Signature-ECC for Network of Things. International Journal on Recent and Innovation Trends in Computing and Communication, 11(1), 53–61. https://doi.org/10.17762/ijritcc.v11i1.5986
Mark White, Thomas Wood, Maria Hernandez, María González , María Fernández. Enhancing Learning Analytics with Machine Learning Techniques. Kuwait Journal of Machine Learning, 2(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/184
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
