Skip to main content

Advertisement

SpringerLink
Log in

Soft fuzzy computing to medical image compression in wireless sensor network-based tele medicine system

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Wireless sensor network can be used to construct a telemedicine scheme to bring together the patient data and expansion of medical conveniences when disaster occurs. The Remote Medical Monitoring (RMM) scheme of the disaster period can be constructed using the Health care center (CC), Wireless sensor nodes and a few Primary health care centers (PHC). The sensor nodes possess the capacity of making communication between patients and PHCs. This type of WSN experiences limited lifetime problem due to the limited battery energy and transmission of medical data in large quantity. This paper proposes a new and novel WSN based Disaster Rescue Telemedicine Scheme to minimize energy consumption and to maximize network lifetime. The proposed method reaches this milestone using three novel algorithms namely ‘Network clustering using Non-border CH oriented Genetic algorithm, Fuzzy rules and Kernel FCM (NCNBGF)’, ‘High gain MDC algorithm (HGMDC)’ and ‘Critical node handling using job limiting and job shifting (CJLS)’. The principal technologies used in this paper are Network node clustering, Medical image compression and Critical state node energy management to elongate the life period of WSN. The Simulation results prove that the proposed method amplifies the WSN topology lifetime to a significant level than the earlier versions. The Existing methods compared in this paper holds only 20% energy at the round 80,the proposed method stays with 43% of energy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Ahilan A, Deepa P (2015) Design for built-in FPGA reliability via fine-grained 2-D error correction codes. Microelectron Reliab 55(9–10):2108–2112

    Article  Google Scholar 

  2. Ahilan A, Deepa P (2015) A reconfigurable virtual architecture for memory scrubbers (VAMS) for SRAM based FPGA’s. Int J Appl Eng Res 10(10):9643–9648

    Google Scholar 

  3. Ahilan A, Deepa P (2015) Modified decimal matrix codes in FPGA configuration memory for multiple bit upsets, 2015 International Conference on Computer Communication and Informatics (ICCCI), p 1–5

  4. Ahilan A, Deepa P (2016) Improving lifetime of memory devices using evolutionary computing based error correction coding. In: Computational intelligence, cyber security and computational models. Springer, Singapore, pp 237–245

  5. Ahilan A, James EAK (2011) Design and implementation of real time car theft detection in FPGA. 2011 Third International Conference on Advanced Computing, Chennai, p 353–358

  6. Ahmadinia M, Meybodi MR, Esnaashari M, Rokny HA (2013) Energy-efficient and multi-stage clustering algorithm in wireless sensor networks using cellular learning automata. IETE J Res 59(6):774–782

    Article  Google Scholar 

  7. Arunraja M, Malathi V, Sakthivel E (2015) Distributed similarity based clustering and compressed forwarding for wireless sensor networks. ISA Transactions, Published by Elsevier Ltd., https://doi.org/10.1016/j.isatra.2015.07.014

  8. Dutta T (2015) Medical data compression and transmission in wireless ad hoc networks. IEEE Sensors J 15(2):778–786

    Article  Google Scholar 

  9. Ebrahimi F, Chamik M, Winkler S (2004) JPEG vs. JPEG2000: an objective comparison of image encoding quality. Proc SPIE ADIP 5558:300–308

    Article  Google Scholar 

  10. Elhabyan RSY, Yagoub MCE (2015) Two-tier particle swarm optimization protocol for clustering and routing in wireless sensor network. J Netw Comput Appl Elsevier Ltd. https://doi.org/10.1016/j.jnca.2015.02.004

  11. Fong B, Ansari N, Fong ACM (2012) Prognostics and health management for wireless telemedicine networks. IEEE Wirel Commun 19(5):83–89

    Article  Google Scholar 

  12. Hsu S, Chen C, Chen S, Huang W, Chang Y, Chen Y (2010) Conserving bandwidth in a wireless sensor network for telemedicine application. Intelligent Automation & Soft Computing, Pub: Taylor & Francis 16(4):537–551

    Article  Google Scholar 

  13. Islam MR, Kim J (2012) Step-by-step approach for energy-efficient wireless sensor network. IETE Tech Rev 29:336–345

    Article  Google Scholar 

  14. Izadi D, Abawajy J, Ghanavati S (2015) An alternative clustering scheme in WSN. IEEE Sensors J 15(7):4148–4155

    Article  Google Scholar 

  15. Kalayci TE, Uger A (2011) Genetic algorithm–based sensor deployment with area priority. Cybern Syst, Pub: Taylor & Francis. https://doi.org/10.1080/01969722.2011.634676

  16. Kaur SP, Sharma M (2015) Radially optimized zone-divided energy-aware wireless sensor networks (WSN) protocol using BA (bat algorithm). IETE J Res. https://doi.org/10.1080/03772063.2014.999833

  17. Lin W, Li D (2006) Adaptive down sampling to improve image compression at low bit rates. IEEE Trans Image Process 15(9):2513–2521

    Article  Google Scholar 

  18. Mahajan SM, Dubey YK (2015) Color image segmentation using kernalized fuzzy C-means clustering. In: IEEE Fifth International Conference on Communication Systems and Network Technologies, Gwalior, India

  19. Manna PS, Singh S (2016) Improved metaheuristic based energy-efficient clustering protocol for wireless sensor networks. https://doi.org/10.1016/j.engappai.2016.10.014

  20. Menon D, Andriani S, Alvagno G (2007) Demosaicing with directional filtering and a posteriori decision. IEEE Trans Image Process 16(1):132–141

    Article  MathSciNet  Google Scholar 

  21. Mrak M, Grgic S, Grgic (2003) Picture quality measures in image compression systems. In: EUROCON 2003. IEEE, Ljubljana

  22. Nayak P, Anurag D (2016) A fuzzy logic based clustering algorithm for WSN to extend the network lifetime. IEEE Sensors J 16(1):137–144

    Article  Google Scholar 

  23. Prathiba G, Santhi M, Ahilan A (2018) Design and implementation of reliable flash ADC for microwave applications. Microelectron Reliab 88–90:91–97

    Article  Google Scholar 

  24. Saeedian E, Torshiz MN, Jalali M, Tadayon G, Tajari MM (2011) CFGA: Clustering wireless sensor network using fuzzy logic and genetic algorithm. DOI: 978–1–4244-6252-0/11

  25. Satheesh Kumar J, Saravana Kumar G, Ahilan A (2018) High performance decoding aware FPGA bit-stream compression using RG codes. Springer Cluster Computing, p 1–5

  26. SenthilKumar K, Amutha R (2015) Energy-efficient cooperative communication in wireless sensor networks using turbo codes. Aust J Electr Electron Eng 12(4):293–300

    Article  Google Scholar 

  27. Sharmaa R, Mishraa N, Srivastavab S (2015) A proposed energy efficient distance based cluster head (DBCH) Algorithm: an Improvement over LEACH. 3rd International Conference on Recent Trends in Computing., (ICRTC-2015)

  28. Sim I, Lee J (2010) Routing protocol with scalability, energy efficiency and reliability in WSN. Intelligent Automation & Soft Computing, Pub: Taylor & Francis 16(4):567–577

    Article  Google Scholar 

  29. Singh S, Gupta B (2016) OSEECH: optimize scalable energy efficient clustering hierarchy protocol in wireless sensor networks. Intl. Conf. Advances in Computing, Communications and Informatics (ICACCI)

  30. Singh AK, Purohit N (2014) An optimised fuzzy clustering for wireless sensor networks. Int J Electron, Pub: Taylor & Francis 101(8):1027–1041

    Article  Google Scholar 

  31. Sivasankari B, Ahilan A, Jothin R, Malar AJG (2018) Reliable N sleep shuffled phase damping design for ground bouncing noise mitigation. Microelectron Reliab 88–90:1316–1321

    Article  Google Scholar 

  32. Virmani D, Kaurb S, Jain S (2014) Secure and fault tolerant dynamic cluster head selection method for wireless sensor networks. International Conference on Information and Communication Technologies., ICICT

Download references

Author information

Authors and Affiliations

  1. Saveetha School of Engineering, Computer Science and Engineering, Thandalam, Chennai, India

    R. Sheeja

  2. AAA College of Engineering and Technology, Amathur, Sivakasi, India

    J. Sutha

Authors
  1. R. Sheeja
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. Sutha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Sheeja.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sheeja, R., Sutha, J. Soft fuzzy computing to medical image compression in wireless sensor network-based tele medicine system. Multimed Tools Appl 79, 10215–10232 (2020). https://doi.org/10.1007/s11042-019-7223-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7223-2

Keywords

Search

Navigation