DOI QR코드

DOI QR Code

A Simulation Framework for Wireless Compressed Data Broadcast

  • Seokjin Im (Dept. of Computer Engineering, Sungkyul Univ.)
  • Received : 2023.04.07
  • Accepted : 2023.05.10
  • Published : 2023.06.30

Abstract

Intelligent IoT environments that accommodate a very large number of clients require technologies that provide secure information service regardless of the number of clients. Wireless data broadcast is an information service technique that ensures scalability to deliver data to all clients simultaneously regardless of the number of clients. In wireless data broadcasting, clients access the wireless channel linearly to explore the data, so the access time of clients is greatly affected by the broadcast cycle. Data compression-based data broadcasting can reduce the broadcast cycle and thus reduce client access time. Therefore, a simulation framework that can evaluate the performance of data broadcasting by applying different data compression algorithms is essential and important. In this paper, we propose a simulation framework to evaluate the performance of data broadcasting that can adopt data compression. We design the framework that enables to apply different data compression algorithms according to the data characteristics. In addition to evaluating the performance according to the data, the proposed framework can also evaluate the performance according to the data scheduling technique and the kind of queries the client wants to process. We implement the proposed framework and evaluate the performance of data broadcasting using the framework applying data compression algorithms to demonstrate the performances of data compression broadcasting.

Keywords

References

  1. S.K. Kim and S.H. Park, "A Study on Intelligent Combat Robot Systems for Future Warfare," International Journal of Advanced Culture Technology (IJACT), Vol. 11, No. 1, pp 165-179, March 2023. DOI: https://doi.org/10.17703/IJACT.2023.11.1.165
  2. G.S. Lee and S.H. Lee, "Study on Real-time Detection Using Odor Data Based on Mixed Neural Network of CNN and LSTM," International Journal of Advanced Culture Technology (IJACT), Vol. 11, No. 1, pp 325-331, March 2023. DOI: https://doi.org/10.17703/IJACT.2023.11.1.325
  3. K. Seo, K. Kim, J. Kim, S. Cho, and S. Park "A Case Study on the Threat of Small Drone and the Development of Counter-Drone System," The Journal of the Convergence on Culture Technology (JCCT), Vol. 9, No. 2, pp 327-332, March 2023. DOI: https://doi.org/10.17703/JCCT.2023.9.2.327
  4. I. Imielinski, S. Viswanathan, and B.R. Bardrinath, "Data on Air: Organization and access,", IEEE Trans. TKDE, Vol. 9, No. 3, pp. 353-372, 1997. DOI: https://doi.org/10.1109/69.599926
  5. S. Im, M. Song, S. Kang, J. Kim, C. Hwang, and S. Lee, "Energy Conserving Multiple Data Access in Wireless Data Broadcast Environments," IEICE Trans. Communication, Vol. E90-B, No. 9, pp 2629-2633, September 2004. DOI: https://doi.org/10.1093/ietcom/e90-b.9.2629
  6. P. C. Shields, "Performance of LZ algorithms on individual sequences," IEEE Trans. On Information Theory, pp. 1283-1288, Vol. 45, No. 4, 1999. DOI: https://doi.org/10.1109/18.761286
  7. O. Plugariu, L. Petrica, R. Pirea, and R. Hobincu, "Hadoop ZedBoard cluster with GZIP compression FPGA acceleration," Proc. of ICECAI 2019. DOI: https://doi.org/10.1109/ECAI46879.2019.9042006
  8. H. Luo, Y. Cai, Q. Luo, and R. Mao, "FPGA-Based Parallel Multi-Core Gzip Compressor I HDFS," Proc. of PDCAT 2019. DOI: https://doi.org/10.1109/PDCAT46702.2019.00017
  9. Real Dataset, available at https://www.nps.gov/subjects/nationalregister/data-downloads.htm
  10. B. Zheng, W.C. Lee, and D.L. Lee, "Spatial Queries in Wireless Broadcast Systems", Wireless Network, Vol. 10, No. 6, pp. 723-736, December 2004. DOI: https://doi.org/10.1023/B:WINE.0000044031.03597.97
  11. S. Im and H. Hwang, "A Two-Tier Spatial Index for Non-flat Spatial Data Broadcasting on Air," IEICE Trans. Communication, Vol. E97-B, No. 12, pp 2809-2818, December 2014. DOI: https://doi.org/10.1587/transcom.E97.B.2809