Design and Implementation of a Fast Lossless Compression Engine for Computer Networks

References

[1] D. Salomon, Data Compression: The Complete Reference, Springer.

[2] K. Sayood, Introduction to Data Compression, Morgan Kaufmann.

[3] T. Welch, “A Technique for High-Performance Data Compression,” IEEE Computer, vol. 17, no. 6, pp. 8–19, 1984.

[4] J. Ziv and A. Lempel, “A Universal Algorithm for Sequential Data Compression,” IEEE Transactions on Information Theory, vol. 23, no. 3, pp. 337–343, 1977.

[5] S. Yamagiwa, E. Hayakawa and K. Marumo, “Stream-Based Lossless Data Compression Applying Adaptive Entropy Coding for Hardware-Based Implementation,” Algorithms, 2020.

[6] S. W. Lee and H. Y. Kim, “An Energy-Efficient Low-Memory Image Compression System for Multimedia IoT Products,” EURASIP Journal on Image and Video Processing, 2018.

[7] R. Saranya, S. Kousalya Devi and V. Prabha, “Compression of FPGA Bit Stream Using Modified Decode Aware Placement Algorithm,” International Journal of Computer Applications.

[8] S. Kousalya Devi and A. Mohanraj, “Reconfigurable System Using Parallel Decompression Engine for Time Delay,” IJERAT Journal, vol. 1, 2013.

[9] Mageswari, S., Kalaiselvan, S., Syed Shabudeen, P. S., Sivakumar, N., & Karthikeyan, S. (2014). Optimization of growth temperature of multi-walled carbon nanotubes fabricated by chemical vapour deposition and their application for arsenic removal. International Journal of Materials Science Poland, 32(4), 709–718.

[10] Kalaiselvan, S., Gopal, K., & Karthikeyan, S. (2016). Synthesis and characterization of multiwalled carbon nanotubes using Brassica juncea oil as carbon source. Carbon – Science and Technology, 8(1), 25–31.

[11] Kalaiselvan, S., Karthik, M., Vladimir, R., & Karthikeyan, S. (2014). Growth of bamboo like carbon nanotubes from Brassica juncea as natural precursor. Journal of Environmental Nanotechnology, 3(2), 92–100.

[12] Karthikeyan, S., Mahalingam, P., Mageswari, S., Kalaiselvan, S., & Angulakshmi, V. S. (2010). Carbon nanotubes from unconventional resources: An environment friendly nanotechnology. In Proceedings of the 4th International Congress of Chemistry and Environment (ICCE), Thailand.

[13] Kalaiselvan, S., Balachandran, K., Karthikeyan, S., & Venckatesh, R. (2016). Botanical hydrocarbon sources based MWCNTs synthesized by spray pyrolysis method for DSSC applications. Silicon, 10(2), 211–217.

[14] Kalaiselvan, S., Jothivenkatachallam, K., & Karthikeyan, S. (2016). The effect of catalyst composition on the growth of multi-walled carbon nanotubes from methyl esters of Oryza sativa oil. Journal of Environmental Nanotechnology, 5(1), 33–38.

[15] Kalaiselvan, S., Angulakshmi, V. S., Mageswari, S., & Karthikeyan, S. (2018). Carbon nanotubes from plant derived hydrocarbon – An efficient renewable precursor. Journal of Environmental Nanotechnology, 7(1), 41–47.

[16] Angulakshmi, V. S., Mageswari, S., Kalaiselvan, S., & Karthikeyan, S. (2018). Application of Box-Behnken design to optimize the reaction conditions on the synthesis of multiwalled carbon nanotubes. Journal of Environmental Nanotechnology, 7(1), 30–36.

[17] Manivannan, J., Kalaiselvan, S., & Velmani, N. (2018). Comparative study of polyol with varying hydroxyl values in polyurethane coatings. International Journal for Research in Engineering Application & Management, 4(4), 74–77.

[18] Kalaiselvan, S., Mathan Kumar, N., & Manivannan, J. (2018). Production of multilayered nanostructure from Zingiberofficinale by spray pyrolysis method. Global Journal of Science Frontier Research: B Chemistry, 18(3).

[19] Manivannan, J., Kalaiselvan, S., & Padmavathi, R. (2020). Vapor-grown carbon fiber synthesis, properties, and applications. In T.-D. Ngo (Ed.), Composite and nanocomposite materials: From knowledge to industrial applications (p. 51). IntechOpen. https://doi.org/10.5772/intechopen.92300

[20] Justin, A. L., Padmavathi, R., & Kalaiselvan, S. (2020). Study of the physico chemical properties of treated water from Coimbatore Lake using ecobiosorbent. AIP Conference Proceedings, 2270(1), 20009.

[21] Manjuladevi, M., & Kalaiselvan, S. (2019). Applications of UV-visible and FT-IR spectral analysis in effluent treatment. Omics International.

[22] Kalaiselvan, S., & Padmavathi, R. (2020). Adsorption of acid dye by activated carbon from agricultural solid waste Leucaenaleucocephala seed shell waste: Kinetics, equilibrium and isotherm study. Materials Science Research India, 17(3), 251–259.

[23] Padmavathi, R., Lydia, I. S., Prasad, S., Selvi, M. T., & Kalaiselvan, S. (2021). Utilization of solar energy for photodegradation of basic violet 10 using tin oxide doped ZnO. Journal of Ovonic Research, 17(3), 261–271. https://doi.org/10.15251/jor.2021.173.261

[24] Manjuladevi, M., Kalaiselvan, S., & Haripriyan, U. (2021). Current updates on COVID-19 vaccine research and an overview of therapeutic drug research. Biosciences Biotechnology Research Asia, 18(3), 439.

[25] Kushwaha, H., Haripriyan, U., Pravina Radhakrishnan, Kalaiselvan, S & Omkar Singh. (2022). Growth of MWCNTs from Azadirachtaindica oil for optimization of chromium(VI) removal efficiency using machine learning approach. Environmental Science and Pollution Research.

[26] Padmavathi, R., Raja, R., Kalaivanan, C., & Kalaiselvan, S. (2022). Syzygiumcumini leaf extract exploited in the green synthesis of zinc oxide nanoparticles for dye degradation and antimicrobial studies. Materials Today: Proceedings, 69, 1200–1205.

[27] Kalaiselvan, S., Kumar, N. V., &Revathy, P. (2021). Inverse domination in bipolar fuzzy graphs. Materials Today: Proceedings, 47, 2071–2075.

@article{1718601,
      author = {Anuja M, Kousalya Devi S},
      title = {Design and Implementation of a Fast Lossless Compression Engine for Computer Networks},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {6},
      pages = {492-497},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1718601.pdf},
      abstract = {The rapid growth of network traffic generated by cloud computing, multimedia applications, data canters, and Internet of Things (IoT) devices has created significant challenges in bandwidth utilization and data transmission efficiency. Lossless compression techniques play a critical role in reducing network traffic while preserving data integrity. However, conventional compression algorithms often introduce computational overhead and latency that limit their suitability for high-speed network environments. This paper presents the design and implementation of a Fast Lossless Compression Engine (FLCE) for computer networks. The proposed engine combines lightweight statistical analysis, adaptive encoding, and block-based compression techniques to achieve efficient data reduction with minimal processing delay. Experimental evaluation demonstrates improvements in compression ratio, throughput, and latency compared with traditional Huffman and LZW-based approaches. The proposed solution is suitable for routers, gateways, edge devices, and real-time communication systems requiring fast and reliable data transmission.},
      keywords = {Fast Lossless Compression Engine (FLCE), Lossless Data Compression, Network Traffic Optimization, Adaptive Encoding, Bandwidth Utilization, High-Speed Networks, Edge Computing, Real-Time Communication Systems.},
      month = {December},
      doi = {https://doi.org/10.64388/IREV6I6-1718601}
  }