A Novel Hybrid Medical Data Compression Using Huffman Coding and LZW in IoT

References

• I. A. Sawaneh, “An effective method for e-medical data compression using wavelet analysis,” Imaging, Vol. 6, pp. 25–32, Dec. 2018.
   Google Scholar
• K. A. Kumari, R. Padmashani, R. Varsha, and V. Upadhayay, “Securing internet of medical things (IoMT) using private blockchain network,” in Principles of Internet of Things (IoT) Ecosystem: Insight Paradigm, Springer, 2020, pp. 305–26, Springer, Cham.
   Google Scholar
• L. Syed, S. Jabeen, S. Manimala, and A. Alsaeedi, “Smart healthcare framework for ambient assisted living using IoMT and big data analytics techniques,” Future Gener. Comput. Syst, Vol. 101, pp. 136–51, Dec. 2019.
   Web of Science ®Google Scholar
• M. Rajasekaran, A. Yassine, M. S. Hossain, M. F. Alhamid, and M. Guizani, “Autonomous monitoring in healthcare environment: Reward-based energy charging mechanism for IoMT wireless sensing nodes,” Future Gener. Comput. Syst, Vol. 98, pp. 565–76, Sep. 2019.
   Web of Science ®Google Scholar
• F. Al-Turjman, M. H. Nawaz, and U. D. Ulusar, “Intelligence in the internet of medical things era: A systematic review of current and future trends,” Comput. Commun, Vol. 150, pp. 644–60, Jan. 2020.
   Web of Science ®Google Scholar
• F. Alsubaei, A. Abuhussein, V. Shandilya, and S. Shiva, “IoMT-SAF: Internet of medical things security assessment framework,” Internet Things, Vol. 8, pp. 100123, Dec. 2019.
   Google Scholar
• I. T. Adeleke, and Q. B. S. Abdul, “Opinions on cyber security, electronic health records, and medical confidentiality: Emerging issues on internet of medical things from Nigeria,” in In Incorporating the Internet of Things in Healthcare Applications and Wearable Devices, IGI Global, 2020, pp. 199–211.
   Google Scholar
• S. Gull, N. A. Loan, S. A. Parah, J. A. Sheikh, and G. M. Bhat, “An efficient watermarking technique for tamper detection and localization of medical images,” J. Ambient Intell. Humaniz. Comput., Vol. 11, pp. 1799–808, May 2020.
   Web of Science ®Google Scholar
• X. Liu, Z. Sheng, and C. Yin, “Routing protocol for low power and lossy IoT networks,” in From Internet of Things to Smart Cities, Chapman and Hall / CRC, 2017, pp. 89–118.
   Google Scholar
• S. Jabbar, S. Panchikkil, Review On Transform Based Medical Image Compression, Computer Science & Information Technology (CS & ITCSCP 2012), 2012 Oct 26.
   Google Scholar
• S. Chandra, A. Sharma, and G. Singh, “A comparative analysis of performance of several wavelet based ECG data compression methodologies,” IRBM, Vol. 42, pp. 227–44, Aug. 2021.
   Web of Science ®Google Scholar
• C.-C. Chen, C.-C. Chang, and K. Chen, “High-capacity reversible data hiding in encrypted image based on Huffman coding and differences of high nibbles of pixels,” J. Vis. Commun. Image Represent, Vol. 76, pp. 103060, Apr. 2021.
   Web of Science ®Google Scholar
• A. Ibaida, A. Abuadbba, and N. Chilamkurti, “Privacy-preserving compression model for efficient IoMT ECG sharing,” Comput. Commun, Vol. 166, pp. 1–8, Jan. 2021.
   Web of Science ®Google Scholar
• N. H. Salman, and S. Rafea, “The arithmetic coding and hybrid discrete wavelet and cosine transform approaches in image compression,” J. Southwest Jiaotong Univ., Vol. 1, pp. 55, 2020.
   Google Scholar
• S. Gull, S. A. Parah, and K. Muhammad, “Reversible data hiding exploiting Huffman encoding with dual images for IoMT based healthcare,” Comput. Commun, Vol. 163, pp. 134–49, Nov. 2020.
   Web of Science ®Google Scholar
• 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. 168–75, Jul. 2019.
   Web of Science ®Google Scholar
• M. E. Hameed, M. M. Ibrahim, N. Abd Manap, and A. A. Mohammed, “A lossless compression and encryption mechanism for remote monitoring of ECG data using Huffman coding and CBC-AES,” Future Gener. Comput. Syst, Vol. 111, pp. 829–40, Oct. 2020.
   Web of Science ®Google Scholar
• H. P. Medeiros, M. C. Maciel, R. Demo Souza, and M. E. Pellenz, “Lightweight data compression in wireless sensor networks using Huffman coding,” Int. J. Distrib. Sens. Netw, Vol. 10, pp. 672921, Jan. 2014.
   Google Scholar
• N. Sharma, and U. Batrab, “Evaluation of lossless algorithms for data compression,” Top. Intell. Comput. Ind. Des., Vol. 2, pp. 40–4, 2020.
   Google Scholar
• S. Anitha, “Lossless image compression and decompression using Huffman coding,” Int. J. Eng. Res. Technol, Vol. 2, pp. 240–7, 2015.
   Google Scholar
• A. Achroufene, M. Chelik, and N. Bouadem, “Modified CSMA/CA protocol for real-time data fusion applications based on clustered WSN,” Comput. Netw, Vol. 196, 108243, Jun. 2021.
   Web of Science ®Google Scholar
• H. Aljuaid, and S. A. Parah, “Secure patient data transfer using information embedding and hyperchaos,” Sensors, Vol. 21, no. 1, pp. 282, 2021.
   Web of Science ®Google Scholar
• A. Ghosh, A. Raha, and A. Mukherjee, “Energy-efficient IoT-health monitoring system using approximate computing,” Internet Things, Vol. 9, pp. 100166, Mar. 2020.
   Google Scholar
• A. Jan, et al., “Secure data transmission in IoTs based on CLoG edge detection,” Future Gener. Comput. Syst., Vol. 121, pp. 59–73, 2021.
   Web of Science ®Google Scholar
• S. A. Parah, et al., “Efficient security and authentication for edge-based internet of medical things,” IEEE Internet Things J., Vol. 8, no. 21, pp. 15652–62, 2020.
   PubMed Web of Science ®Google Scholar
• K. B. Adedeji, “Performance evaluation of data compression algorithms for IoT-based smart water network management applications,” JASPE, Vol. 7, pp. 554–63, Oct. 2020.
   Google Scholar
• I. F. Wumi, and O. Binuyo Adeyemi, “Medical image compression using the hybrid of Lempel ZIV Welch (LZW) and Huffman algorithms,” Int. J. Res. Innov. Appl. Sci., Vol. 4, no. 4, pp. 14–9, April 2019.
   Google Scholar
• F. Ajala, A. Adigun, and A. Oke, “Development of hybrid compression algorithm for medical images using Lempel-Ziv-Welch and Huffman encoding,” Int. J. Recent Technol. Eng, Vol. 7, pp. 1–5, 2018.
   Google Scholar
• A. B. Jambek, and N. A. Khairi, “Performance comparison of Huffman and LZW data compression for wireless sensor node application,” Amer. J. Appl. Sci., Vol. 11, no. 1, pp. 119–26, 2014.
   Google Scholar
• M. Hanoune, and M. E. G. Lysmos, “Data compression mechanisms in an intelligent E-health gateway for medical monitoring applications,” Procedia Comput. Sci., Vol. 175, pp. 578–84, Jan. 2020.
   Google Scholar
• S. T. Ahmed, and S. Sankar, “Investigative protocol design of layer optimized image compression in telemedicine environment,” Procedia. Comput. Sci., Vol. 167, pp. 2617–22, 2020.
   Google Scholar
• K. M. Hosny, A. M. Khalid, and E. R. Mohamed, “Optimized medical image compression for telemedicine applications,” in Artificial Intelligence and Data Mining in Healthcare, Cham: Springer, 2021, pp. 119–42.
   Google Scholar
• S. Fatima, “High speed data exchange algorithm in telemedicine with wavelet based on 4D medical image compression,” Int. J. Eng. Manag. Res., Vol. 11, no. 4, pp. 45–9, 2021.
   Google Scholar
• S. T. Ahmed, M. Sandhya, and S. Sankar, “A dynamic MooM dataset processing under TelMED protocol design for QoS improvisation of telemedicine environment,” J. Med. Syst., Vol. 43, no. 8, pp. 1–12, 2019.
   Web of Science ®Google Scholar
• D. A. Huffman, “A method for the construction of minimum-redundancy codes,” Proc. IRE, Vol. 40, pp. 1098–101, Sep. 1952.
   Google Scholar
• V. Aggarwal, S. Gupta, M. S. Patterh, and L. Kaur, “Analysis of compressed foetal phono-cardio-graphy (PCG) signals with discrete cosine transform and discrete wavelet transform,” IETE J. Res, Vol. 25, pp. 1–7, Feb. 2020. 10.1080/03772063.2020.1725662.
   Google Scholar
• P. Kaur, “Compression using fractional Fourier transform,” A thesis submitted in the partial fulfillment of requirement for the award of the degree of Master of Engineering in Electronics and Communication, Deemed University.
   Google Scholar
• A. Odat, M. Otair, and M. Al-Khalayleh, “Comparative study between LM-DH technique and Huffman coding,” Int. J. Appl. Eng. Res, Vol. 10, pp. 36004–11, 2015.
   Google Scholar
• P. Singh, M. Duhan, Enhancing LZW Algorithm to Increase Overall Performance, In 2006 Annual IEEE India Conference IEEE, 2006, pp. 1–4.
   Google Scholar
• Z. J. Ahmed, and L. E. George, “A comparative study using LZW with wavelet or DCT for compressing color images,” in 2020 International Conference on Advanced Science and Engineering (ICOASE), 2020, pp. 53–8.
   Google Scholar
• P. Pławiak, “Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system,” Expert Syst. Appl, Vol. 92, pp. 334–49, Feb. 2018.
   Web of Science ®Google Scholar
• H. Mamaghanian, N. Khaled, D. Atienza, and P. Vandergheynst, “Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes,” IEEE. Trans. Biomed. Eng, Vol. 58, pp. 2456–66, May 2011.
   PubMed Web of Science ®Google Scholar
• M. Raeiatibanadkooki, S. R. Quchani, M. KhalilZade, and K. Bahaadinbeigy, “Compression and encryption of ECG signal using wavelet and chaotically Huffman code in telemedicine application,” J. Med. Syst, Vol. 40, pp. 73, Mar. 2016.
   PubMed Web of Science ®Google Scholar
• T. Y. Liu, K. J. Lin, and H. C. Wu, “ECG data encryption then compression using singular value decomposition,” IEEE J. Biomed. Health Inform, Vol. 22, pp. 707–13, Apr. 2017.
   PubMed Web of Science ®Google Scholar