Image Transmission in WMSN Based on Residue Number System


  • Anatoliy Sachenko
  • Vasyl Yatskiv
  • Jürgen Sieck
  • Jun Su



Wireless Multimedia Sensor Networks, Residue Number System, Image Transmission


The paper considers the speedy images processing in Wireless Multimedia Sensor Networks using the Residue Number System (RNS) and the method of arithmetic coding. The proposed method has a two-stage frame: firstly, the RNS transformation is run to divide the data and obtain residues, and secondly, the parallel compression of the resulting residues is provided by employing the arithmetic coding. Within the implementation of binary code transformation in RNS one, the hardware complexity for block conversion is evaluated for various modulo sets and the results are illustrated. Authors employed the arithmetic coding for residue compression to provide the optimum of compression degree in terms of entropy assessment as well as a reduction in image redundancy without loss of quality. A research algorithm is proposed to run an experiment presented by the residues carried out on test images and other types of files. As a result, an increase in the speed of image compression of about 2.5 times is achieved by processing the small data as well as providing the parallel operation of the compression residue units by RNS selected moduli. Finally, the existing and proposed methods are compared and it has been shown the last one provides a better compression ratio of more than twice.


M. El-Hajj, A. Fadlallah, M. Chamoun, A. Serhrouchni, “A survey of internet of things (IoT) authentication schemes,” Sensors, vol. 19, issue 5, pp. 1141, 2019.

C. Puliafito, E. Mingozzi, F. Longo, A. Puliafito, O. Rana, “Fog computing for the internet of things: A survey,” ACM Transactions on Internet Technology (TOIT), vol. 19, issue 2, pp. 1-41, 2019,

H. Fesenko, V. Kharchenko, A. Sachenko, R. Hiromoto and V. Kochan, “An Internet of Drone-based multi-version post-severe accident monitoring system: Structures and reliability,” In book Dependable IoT for Human and Industry – Modeling, Architecting, Implementation, Editors: Vyacheslav Kharchenko, Ah Lian Kor and Anrzej Rucinski, River Publishers, 2018, pp. 197-218.

E. Satir, H. Isik, “A Huffman compression based text steganography method,” Multimed Tools Appl, vol. 70, pp. 2085–2110, 2014,

O. Ghorbel, I. Jabri, W. Ayedi, M. Abid, “Experimental study of compressed images transmission through WSN,” Proceeding of the IEEE International Conference ICM’2011, Hammamet, 2011, pp. 1-6,

M. Hosseini, D. Pratas, A. J. Pinho, “A survey on data compression methods for biological sequences,” Information, 2016, vol. 7, 56,

Uthayakumar J., Elhoseny M., Shankar K. Highly Reliable and Low-Complexity Image Compression Scheme Using Neighborhood Correlation Sequence Algorithm in WSN. IEEE Transactions on Reliability, 2020, vol. 69, issue 4, pp. 1398-1423,

F. Bouakkaz, W. Ali, & M. Derdour, “Forest fire detection using wireless multimedia sensor networks and image compression,” Instrum. Mes. Métrologie, vol. 20, pp. 57-63, 2021,

S. Jun, K. Przystupa, M. Beshley, et al., “A cost-efficient software based router and traffic generator for simulation and testing of IP network,” Electronics, vol. 9, issue 1, pp. 40, 2020,

V. Yatskiv, A. Sachenko, N. Yatskiv, P. Bykovyy, A. Segin, “Compression and transfer of images in wireless sensor networks using the transformation of residue number system,” Proceedings of the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS’2019), Metz, France, 18-21 September 2019, vol. 2, pp. 1111-1114.

C. M. Sadler, M. Martonosi, “Data compression algorithms for energy-constrained devices in delay tolerant networks,” Proceedings of the 4th ACM International Conference on Embedded Networked Sensor Systems, Boulder, Colorado, USA, 2006, pp. 265-278,

J. Uthayakumar, T. Vengattaraman, P. Dhavachelvan, “A new lossless neighborhood indexing sequence (NIS) algorithm for data compression in wireless sensor networks,” Ad Hoc Networks, vol. 83, pp. 149-157, 2019.

D. Birvinskas, V. Jusas, I. Martisius, I. Damasevicius, “Fast DCT algorithms for EEG data compression in embedded systems,” Computer Science and Information Systems, vol. 12, issue 1, pp. 49–62, 2015,

D. Kandris, C. Nakas, D. Vomvas, G. Koulouras, “Applications of wireless sensor networks: an up-to-date survey,” Applied System Innovation, vol. 3, issue 1, 14, 2020.

M. L. Kaddachi, A. Soudani, I. Nouira, V. Lecuire, K. Torki, “Efficient hardware solution for low power and adaptive image-compression in WSN,” Proceedings of the 2010 17th IEEE International Conference on Electronics, Circuits and Systems, Athens, 2010, pp. 583-586,

A. Abdul-Barik, M. Ibrahim, S. Akobre, “Improved Lempel-Ziv-Welch’s error detection and correction scheme using redundant residue number system,” Circulation in Computer Science, vol. 2, issue 6, pp. 25-30, 2017,

J. Liu, F. Chen, D. Wang, “Data compression based on stacked RBM-AE model for wireless sensor networks,” Sensors, vol. 18, issue 12, 4273, 2018.

A. Alhassan, I. Saeed, P. A. Agbedemnab, “The Huffman’s method of secured data encoding and error correction using residue number system (RNS),” Communication on Applied Electronics (CAE) Journal, Foundation of Computer Science (FCS), New York, USA, vol. 2, no. 9, pp. 14-18, 2015,

G. Kumar, R. Kumar, “Analysis of arithmetic and Huffman compression techniques by using DWT-DCT,” International Journal of Image, Graphics and Signal Processing (IJIGSP), vol. 13, no. 4, pp. 63-70, 2021,

B. A. Lungisani, et al., “Image compression techniques in wireless sensor networks: a survey and comparison,” IEEE Access, vol. 10, pp. 82511-82530, 2022,

S. Li, J. G. Kim, D. H. Han, K. S. Lee, “A survey of energy-efficient communication protocols with QoS guarantees in wireless multimedia sensor networks,” Sensors, vol. 19, 199, 2019.

S. A. Dehkordi, K. Farajzadeh, J. Rezazadeh, R. Farahbakhsh, K. Sandrasegaran, and M. A. Dehkordi, “A survey on data aggregation techniques in IoT sensor networks,” Wireless Networks, vol. 26, issue 2, pp. 1243-1263, 2020,

E. D. Pitchika, S. Bharadwaj, “Fast base extension using single redundant modulus in a residue number system,” Proceedings of the 2019 IEEE International Conference on Power Electronics, Control and Automation (ICPECA), New Delhi, India, 2019, pp. 1-5,

H. Z. Eldin, M. A. Elhosseini, H. A. Ali, “Image compression algorithms in wireless multimedia sensor networks: A survey,” Ain Shams Engineering Journal, vol. 6, issue 2, pp. 481-490, 2015.

M. Zeidan, & B. Kamiloğlu, “Three-dimensional imaging technique to compare digital impression CEREC Omnicam intraoral camera (CAD) and tri-dimensional cone-beam computed tomography, to measure maxillary casts: Unilateral and bilateral cleft lip and palate up to 6 months of age, applied in nanotechnology,” Applied Nanoscience, vol. 13, pp. 1753–1759, 2021.

P. V. Ananda Mohan, “Reverse converters for the moduli set {2n+1-3, 2n-1, 2n-1-1},” IETE Journal of Education, vol. 57, pp. 31-43, 2016,

T. Sheltami, M. Musaddiq, E. Shakshuki, “Data compression techniques in wireless sensor networks,” Future Generation Computer Systems, vol. 64, pp. 151-162, 2016,

A. Drozd, J. Drozd, S. Antoshchuk, V. Antonyuk, K. Zashcholkin, M. Drozd, O. Titomir, “Green experiments with FPGA,” In book: Green IT Engineering: Components, Networks and Systems Implementation. V. Kharchenko, Y. Kondratenko, J. Kacprzyk (Eds.), vol. 105, Berlin, Heidelberg: Springer International Publishing, 2017, pp. 219–239.

V. Yatskiv and T. Tsavolyk, “Improvement of data transmission reliability in wireless sensor networks on the basis of residue number system correcting codes using the special module system,” Proceedings of the 2017 IEEE First Ukraine Conference on Electrical and Computer Engineering (UKRCON), Kyiv, Ukraine, 2017, pp. 890-893, doi: 10.1109/UKRCON.2017.8100376.

Z. Hu, V. Yatskiv, A. Sachenko, “Increasing the data transmission robustness in WSN using the modified error correction codes on residue number system,” Elektronika ir Elektrotechnika, vol. 21, issue 1, pp. 76-81, 2015.

M. Nasri, A. Helali, H. Sghaier, H. Maaref, “Adaptive image transfer for wireless sensor networks (WSNs),” Proceedings of the 5th International Conference on Design & Technology of Integrated Systems in Nanoscale Era, Hammamet, pp. 1-7, 2010.

H. Kasban, S. Nassar, & M. A. M. El-Bendary, “Medical images transmission over wireless multimedia sensor networks with high data rate,” Analog Integr Circ Sig Process, vol. 108, pp. 125–140, 2021,

B. Al Hayani, H. Ilhan, “Image transmission over decode and forward based cooperative wireless multimedia sensor networks for rayleigh fading channels in Medical Internet of Things (MIoT) for remote health-care and health communication monitoring,” Journal of Medical Imaging and Health Informatics, vol. 10, no. 1, pp. 160-168(9), 2020,

S. Suseela, S. Jeevanantham, A. Kavitha, M. Saravanan, A. E. Narayanan, “Challenging issues for handling multimedia data in wireless multimedia sensor networks,” Journal of Computational and Theoretical Nanoscience, vol. 16, no. 4, pp. 1215-1220(6), 2019,

Y. A. Ur Rehman, M. Tariq, “Chapter 3 – Visual information processing and transmission in wireless multimedia sensor networks: a deep learning based practical approach,” Editor(s): S. Shukla, A. K. Singh, G. Srivastava, F. Xhafa, In Intelligent Data Centric Systems, Internet of Multimedia Things (IoMT), Academic Press, 2022, pp. 47-66,

A. Mateen, M. Sehar, K. Abbas and M. A. Akbar, “Comparative analysis of wireless sensor networks with wireless multimedia sensor networks,” Proceedings of the 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), Chennai, India, 2017, pp. 80-83,




How to Cite

Sachenko, A., Yatskiv, V., Sieck, J., & Su, J. (2024). Image Transmission in WMSN Based on Residue Number System. International Journal of Computing, 23(1), 126-133.