FELZMACS: A Novel Data Compression Model in Wireless Sensor Networks for Fast Data Transfer

Authors

  • Seelamanthula Sreenivasu Research Scholar, Department of Computer Sciences & Systems Engineering, A.U.College of Engineering, Andhra University, Visakhapatnam, Andhra Pradesh, India.
  • James Stephen Meka Dr. B. R. Ambedkar Chair Professor, Dean, A.U. TDR-HUB,Andhra University, Visakhapatnam, Andhra Pradesh, India.

Keywords:

Data compression in wireless sensor networks, Hybrid approaches in data compression, fast transmission in wireless sensor networks, FELACS Compression, LZMA compression

Abstract

Recent research on Wireless Sensor Networks has been multi-dimensional and exhaustive. Data compression is one area being focused, due to the reason that the data originated from the sensors and transmitted to the base station through intermediate entities require fast transmission. The data compression during the transmission effectively results in faster communication, node lifetime improvement as well slight protection of the data. The research on data compression encompasses various techniques proposed that include discrete cosine transform, run length encoding, embedded zero tree wavelet coding, and so on. It is well known that incorporating data compression into WSN further enhances energy efficiency. In this paper, a hybrid approach “Fast and Efficient Lempel Ziv Markov-Chain Adaptable Compression Scheme (FELZMACS)” involving two techniques namely Fast and efficient lossless adaptive compression scheme (FELACS) and Lempel Ziv Markov chain Algorithm (LZMA) methods are used for compressing the data each at a different level. The former approach is used to compress the data between the node and cluster head while the latter approach is used for data compression between the cluster head and the base station. The performance of this hybrid approach in terms of energy efficiency and delay is remarkable.

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References

B. William. (2013). Elprocus. Accessed: Feb. 17, 2022. [Online]. Available: https://www.elprocus.com/introduction-to-wireless-sensornetworks-types-and-applications/

J. Azar, A. Makhoul, M. Barhamgi, and R. Couturier, ‘‘An energy efficient IoT data compression approach for edge machine learning,’’ Future Gener. Comput. Syst., vol. 96, pp. 168–175, Jul. 2019, doi: 10.1016/j.future.2019.02.005.

G. Dhand and S. S. Tyagi, ‘‘Data aggregation techniques in WSN: Survey,’’ Proc. Comput. Sci., vol. 92, pp. 378–384, Jan. 2016, doi: 10.1016/j.procs.2016.07.393.

G. Badshah, S.-C. Liew, J. M. Zain, and M. Ali, ‘‘Watermark compression in medical image watermarking using Lempel-Ziv-Welch (LZW) lossless compression technique,’’ J. Digit Imag., vol. 29, no. 2, pp. 216–225, Apr. 2016, doi: 10.1007/s10278-015-9822-4.

W. F. Good, G. S. Maitz, and D. Gur, ‘‘Joint photographic experts group (JPEG) compatible data compression of mammograms,’’ J. Digit. Imag., vol. 7, no. 3, pp. 123–132, Aug. 1994.

P. Thakral and S. Manhas, ‘‘Image processing by using different types of discrete wavelet transform,’’ in Proc. Adv. Informat. Comput. Res., Commun. Comput. Inf. Sci. (ICAICR), 2018, pp. 499–507.

V. Dhandapani and S. Ramachandran, ‘‘Area and power efficient DCT architecture for image compression,’’ EURASIP J. Adv. Signal Process., vol. 2014, no. 1, pp. 1–9, Dec. 2014.

Mazin, Z., & Alak, S. A.-. (2023). A Developed Compression Scheme to Optimize Data Transmission in Wireless Sensor Networks. Iraqi Journal of Science, 64(3), 1463–1476. https://doi.org/10.24996/ijs.2023.64.3.35

Väänänen, O. and Hämäläinen, T. (2022), "Efficiency of temporal sensor data compression methods to reduce LoRa-based sensor node energy consumption", Sensor Review, Vol. 42 No. 5, pp. 503-516. https://doi.org/10.1108/SR-10-2021-0360

Ali Mohammad Norouzzadeh Gil Molk, Seyed Mohsen Ghoreishi, Fatemeh Ghasemi, Iraj Elyasi, "Improve Performances of Wireless Sensor Networks for Data Transfer Based on Fuzzy Clustering and Huffman Compression", Journal of Sensors, vol. 2022, Article ID 3860682, 16 pages, 2022. https://doi.org/10.1155/2022/3860682

B. A. Lungisani, C. K. Lebekwe, A. M. Zungeru and A. Yahya, "Image Compression Techniques in Wireless Sensor Networks: A Survey and Comparison," in IEEE Access, vol. 10, pp. 82511-82530, 2022, doi: 10.1109/ACCESS.2022.3195891.

Mishra, Mukesh, Gourab Sen Gupta, and Xiang Gui. 2022. "Investigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications" Sensors 22, no. 19: 7685. https://doi.org/10.3390/s22197685

Nasif, Ammar & ali othman, Zulaiha & S Sani, Nor. (2021). The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities. Sensors. 21. 4223. 10.3390/s21124223.

K. L. Ketshabetswe, A. M. Zungeru, B. Mtengi, C. K. Lebekwe and S. R. S. Prabaharan, "Data Compression Algorithms for Wireless Sensor Networks: A Review and Comparison," in IEEE Access, vol. 9, pp. 136872-136891, 2021, doi: 10.1109/ACCESS.2021.3116311.

Chen, C., Zhang, L. & Tiong, R.L.K. A new lossy compression algorithm for wireless sensor networks using Bayesian predictive coding. Wireless Netw 26, 5981–5995 (2020). https://doi.org/10.1007/s11276-020-02425-w

Kalaivani, S., Tharini, C., Saranya, K. et al. Design and Implementation of Hybrid Compression Algorithm for Personal Health Care Big Data Applications. Wireless Pers Commun 113, 599–615 (2020). https://doi.org/10.1007/s11277-020-07241-1

N. Kimura and S. Latifi, "A survey on data compression in wireless sensor networks," International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II, Las Vegas, NV, USA, 2005, pp. 8-13 Vol. 2, doi: 10.1109/ITCC.2005.43.

Horita, Augusto & Bonna, Ricardo & Loubach, Denis & Sander, Ingo & Söderquist, Ingemar. (2019). Lempel-Ziv-Markov Chain Algorithm Modeling using Models of Computation and ForSyDe. 152-155. 10.3384/ecp19162017.

Radio Technical Commission for Aeronautics - RTCA. DO-178C - Software Considerations in Airborne Systems and Equipment Certification, 2012.

Standard Performance Evaluation Corporation (SPEC). 657.xz_s spec cpu 2017 benchmark description. http:/www.spec.org/cpu2017/Docs/ benchmarks/657.xz_s.html, 2019.

Ingo Sander. The forsyde methodology. In Swedish System-on-Chip Conference, 2002.

Kalaivani, S. & Tharini, C.. (2019). Analysis and implementation of novel Rice Golomb coding algorithm for wireless sensor networks. Computer Communications. 150. 10.1016/j.comcom.2019.11.046.

T. Chen, H. Liu, Q. Shen, T. Yue, X. Cao and Z. Ma, "DeepCoder: A deep neural network based video compression," 2017 IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, FL, USA, 2017, pp. 1-4, doi: 10.1109/VCIP.2017.8305033.

2017. A new compression algorithm for small data communication in wireless sensor network. Int. J. Sen. Netw. 25, 3 (January 2017), 163–175. https://doi.org/10.1504/IJSNET.2017.087712

S. Jancy, C. Jayakumar, “Packet level data compression techniques for wireless sensor networks,” Journal of Theoretical and Applied Information Technology, vol.75. no.1, 2015.

Sheltami, Tarek & Musaddiq, Muhammad & Shakshuki, Elhadi. (2016). Data compression techniques in Wireless Sensor Networks. Future Generation Computer Systems. 64. 10.1016/j.future.2016.01.015.

J. G. Kolo, S. A. Shanmugam, D. W. G. Lim, L.-M. Ang, and K. P. Seng, ‘‘An adaptive lossless data compression scheme for wireless sensor networks,’’ J. Sensors, vol. 2012, pp. 1–20, Jul. 2012, doi: 10.1155/2012/539638.

J. G. Kolo, S. A. Shanmugam, D. W. G. Lim, and L.-M. Ang, ‘‘Fast and efficient lossless adaptive compression scheme for wireless sensor networks,’’ Comput. Electr. Eng., vol. 41, pp. 275–287, Jan. 2015.

Medeiros HP, Maciel MC, Demo Souza R, Pellenz ME. Lightweight Data Compression in Wireless Sensor Networks Using Huffman Coding. International Journal of Distributed Sensor Networks. 2014;10(1). doi:10.1155/2014/672921

Song, Wei. (2013). Strategies and Techniques for Data Compression in Wireless Sensor Networks. TELKOMNIKA Indonesian Journal of Electrical Engineering. 11. 10.11591/telkomnika.v11i11.3507.

Wang, you-chiun. (2012). Data Compression Techniques in Wireless Sensor Networks. Pervasive Computing.

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Published

25.12.2023

How to Cite

Sreenivasu, S. ., & Meka, J. S. . (2023). FELZMACS: A Novel Data Compression Model in Wireless Sensor Networks for Fast Data Transfer. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 214–224. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4244

Issue

Vol. 12 No. 2 (2024)

Section

Research Article

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