Raspberry Pi implemented with MATLAB simulation and communication of physiological signal-based fast chaff point (RPSC) generation algorithm for WBAN systems
-
Karthikeyan Venkatesan Munivel
and
Tephillah Samraj
Abstract
Wireless Body Area Network (WBAN) has gained considerable significance in medical fields like implantable cardiac defibrillators (ICDs), neuro-stimulators etc. The body area networks information with in the implantable medical devices (IMDs) must be secure and their privacy must be protected. The absence of protection at the interface makes it easy for the attackers to take control of the IMDs. Thus, protection of wireless interface has become mandatory in IMDs during key agreement schemes. To secure the key agreement scheme, the most practical light weight bio-cryptosystem schemes popularly known as fuzzy vault (FV) is implemented. The most computationally intensive task in the FV scheme is the chaff point generation process, used for hiding the secret key and valid point inside the vault. Thus, a Raspberry Pi implemented with MATLAB simulation and communication of physiological signal based fast chaff point generation (RPSC) algorithm for WBAN. RPSC algorithm reduced the number of candidate chaff points in the chaff point generation and reduced the overall execution time. The RPSC algorithm has an algorithm complexity of O(n2), which is a significant over the existing O(n3) complexity. The RPSC algorithm has a speedup performance of 206 times over Clancy’s, 130 times over Khalil’s and 93 times than Nguyen algorithms for the generation of 504 chaff points, within smaller computation duration of 0.7 s. Raspberry Pi pro 3 (RPi3) hardware modules are considered as IMD and programmer devices, are used for implementation of chaff point generation and real-time communication module for proposed WBAN.
Research funding: Authors state no funding involved.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: The authors do not have any conflict of interest regarding this article.
Informed consent: Informed consent is not applicable.
Ethical approval: The authors do not involve any human participants.
References
1. Garth, VC, Ghosh, T, Murimi, R, Craig, AC. WBAN for healthcare: a survey. Int J Ad Hoc Sensor Ubiquitous Comput 2012;3:1–26. https://doi.org/10.5121/ijasuc.2012.3301.Search in Google Scholar
2. Karthikeyan, MV, Martin leo Manickam, J. Secure IR comm design for pre-cardiac arrest detection in WBAN. Int J Recent Innovat Trends Comput Commun 2015;3:3520–5.Search in Google Scholar
3. Karthikeyan, MV, Martin leo Manickam, J. A 128-Bit secret key generation using unique ECG bio-signal for med data cryptography in lightweight WBAN. Pak J Biotechnol 2017;14:257–64.Search in Google Scholar
4. Goldman, M. Principle of clinical electrocardiograph. Los Altos: Lange Medical Publications; 1982.Search in Google Scholar
5. Karthikeyan, MV, Martin Leo Manickam, J. Security issues in WBAN: in bio-signal input fuzzy security model: a survey. Res J Pharmaceut Biol Chem Sci 2016;7:1755–73.Search in Google Scholar
6. Chunxiao, Li, Anand, Raghunathan, Niraj, K. Jha. Hijacking an insulin pump: Security attacks and defenses for a diabetes therapy system. In: International Conference on e-Health Networking, Applications and Services Columbia, MO, USA: IEEE; 2011.10.1109/HEALTH.2011.6026732Search in Google Scholar
7. Halperin, D, Heydt-Benjamin, TS, Ransford, B, Clark, SS, Defend, B, Morgan, W, et al.. Pacemakers and implantable cardiac defbrillators: software radio attacks and zero-power defenses. In: Proceedings of IEEE Symposium on Security and Privacy, Oakland, CA, USA; 2008:129–42.10.1109/SP.2008.31Search in Google Scholar
8. Karthikeyan, MV, Martin Leo Manickam, J. EFSKG scheme a simplified model for WBAN. J Med Img Health Info 2018;8:863–71. https://doi.org/10.1166/jmihi.2018.2415.Search in Google Scholar
9. Karthikeyan, MV, Martin Leo Manickam, J. ESKG scheme for WBAN and hardware implementation. Wireless Pers Commun, Springer Professional 2018;106:2037–52. https://doi.org/10.1007/s11277-018-5924-x.Search in Google Scholar
10. Venkatasubramanian, K, Banerjee, A, Gupta, SKS. Plethysmogram-based secure inter-sens comm in BAN. In: Proceedings of IEEE Military Communications Conference, San Diego, CA, USA: IEEE; 2008:1–7.10.1109/MILCOM.2008.4753199Search in Google Scholar
11. Venkatasubramanian, K, Banerjee, A, Gupta, SKS. PSKA: usable and secure key agreement scheme for BAN. IEEE Trans Inf Technol Biomed 2010;14:60–8. https://doi.org/10.1109/titb.2009.2037617.Search in Google Scholar PubMed
12. Fen, M, Shu, DB, Ye, L. A Modified Fuzzy Vault Scheme for Biometrics-Based Body Sensor Networks Security. GLOBECOM IEEE 2010:1–5, https://doi.org/10.1109/GLOCOM.2010.5683998.Search in Google Scholar
13. Wonsuk, C, In, SK, Dong, HL. E2PKA scheme for BAN. Wireless Pers Commun 2017;97:977–98, https://doi.org/10.1007/s11277-017-4547-y.Search in Google Scholar
14. Juels, A, Wattenberg, W. A fuzzy commitment scheme. In: Proceedings of Sixth ACM Conference on Computer & Communications Security. Singapore: ACM Digital Library; 1999:28–36.10.1145/319709.319714Search in Google Scholar
15. Clancy, TC, Kiyavash, N, Lin, DJ. Secure smartcard based fingerprint authentication Process. In: Proceedings of the 2003. New York, USA: ACM SIGMM (WBMA); 2003. p. 45–52.10.1145/982507.982516Search in Google Scholar
16. Umut, U, Sharath, P, Anil, KJ. Fuzzy vault for fingerprints. In: Proceedings of Fifth International Conference on AVBPA. Berlin: Springer; 2005. p. 310–19.10.1007/11527923_32Search in Google Scholar
17. Khalil-Hani, M, Marsono, MN, Bakhteri, R. Bio-metric encryption based on a fuzzy vault scheme with a fast chaff generation algorithm. Future Generat Comput Syst 2013;29:800–10. https://doi.org/10.1016/j.future.2012.02.002.Search in Google Scholar
18. Nguyen, TH, Wang, Y, Ha, Y, Li, R. Improved chaff point generation for vault scheme in bio-cryptosystems. J IET Biomed 2013;2:48–55. https://doi.org/10.1049/iet-bmt.2012.0060.Search in Google Scholar
19. Amirthalingam, G, Adhamani, G. New chaff point based fuzzy vault for multimodal biometric cryptosystem using particle swarm optimization. J King Saud Univ Comput Inf Sci 2016;28:381–94. https://doi.org/10.1016/j.jksuci.2014.12.011.Search in Google Scholar
20. Goldberger, AL, Amaral, LA, Glass, L, Hausdorff, JM, Ivanov, PC, Mark, RG, et al. PhysioBank, PhysioTool kit and PhysioNet. Components of a new research resource for complex physio sig. 2000;101: e215–220. https://doi.org/10.1161/01.cir.101.23.e215.Search in Google Scholar PubMed
21. Hao, W, Chen, Y, Xin, Y. ECG baseline wander correction by mean-median filter and discrete wavelet transform. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, Massachusetts, USA: IEEE; 2011.Search in Google Scholar
22. Cuiwei, L, Chongxun, Z, Changfeng, T. Detection of ECG characteristic points using wavelet transforms. IEEE Trans Biomed Eng 1995;42:21–28, https://doi.org/10.1109/10.362922.Search in Google Scholar PubMed
23. Vibha, A, Manjeet, SP. ECG signal compression using morphological Haar wavelet transform. An Int J Eng Sci 2016;17:263–5.Search in Google Scholar
24. Li, H, Tan, J. Body sensor network based context aware QRS detection. In: Proceedings of pervasive health conference and workshops. Austria: Innsbruck; 2006. p. 1–8.10.1109/PCTHEALTH.2006.361683Search in Google Scholar
25. Patel, AM, Gakare, PK, Cheeran, A. Real time ECG feature extraction and arrhythmia detection on a mobile platform. Int J Comp App 2012;44:40–5.Search in Google Scholar
26. Yang, XS. Flower pollination algorithm for global optimization. In: Unconventional computation and natural computation. Berlin: Springer; 2012.10.1007/978-3-642-32894-7_27Search in Google Scholar
27. Pavlyukevich, I. L´evy flights: non-local search and simulated annealing. J Comp Phys 2007;226:1830–44. https://doi.org/10.1016/j.jcp.2007.06.008.Search in Google Scholar
28. Karthikeyan, MV, Martin Leo Manickam, J. A novel fast chaff point generation method using bio-inspired flower pollination algorithm for fuzzy vault systems with physiological signal for wireless body area sensor networks. In: Artificial intelligent techniques for bio-medical signal processing. Biomedical Research; 2017:s242–254.Search in Google Scholar
29. Deschamps, JP, Bioul, GJA, Sutter, GD. Synthesis of arithmetic circuits: FPGA, ASIC and embedded systems. New Jersey: John Wiley & sons, Inc.; 2006.10.1002/0471741426Search in Google Scholar
30. Thaier, H, Bassam, JM, Muhammad, I, Ghada, A, Athanasios, VV. Secure authentication for remote patient monitoring with wireless medical sensor networks. Sensors 2016;16:1–25, https://doi.org/10.1109/10.362922.Search in Google Scholar PubMed
31. Vijayarajan, R, Muttan, S. Discrete wavelet transform based principal component averaging fusion for medical images. Int J Electron Commun 2015;69:896–902. https://doi.org/10.1016/j.aeue.2015.02.007.Search in Google Scholar
32. Karthikeyan, MV, Martin Leo Manickam, J. An enhanced flower pollination algorithm-based chaff point generation method with hardware implementation in WBAN. Int J Commun Syst 2020;33:1–20, https://doi.org/10.1002/dac.4447.Search in Google Scholar
© 2020 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Research Articles
- Nonlinear analysis of scalp EEGs from normal and brain tumour subjects
- Dimensionality reduction for EEG-based sleep stage detection: comparison of autoencoders, principal component analysis and factor analysis
- EEG signal classification based on SVM with improved squirrel search algorithm
- The effect of attentional focusing strategies on EMG-based classification
- Identification of dental pain sensation based on cardiorespiratory signals
- ScatT-LOOP: scattering tetrolet-LOOP descriptor and optimized NN for iris recognition at-a-distance
- A detailed and comparative work for retinal vessel segmentation based on the most effective heuristic approaches
- Visual enhancement of brain cancer MRI using multiscale dyadic filter and Hilbert transformation
- Raspberry Pi implemented with MATLAB simulation and communication of physiological signal-based fast chaff point (RPSC) generation algorithm for WBAN systems
- Short Communication
- How yarn orientation limits fibrotic tissue ingrowth in a woven polyester heart valve scaffold: a case report
Articles in the same Issue
- Frontmatter
- Research Articles
- Nonlinear analysis of scalp EEGs from normal and brain tumour subjects
- Dimensionality reduction for EEG-based sleep stage detection: comparison of autoencoders, principal component analysis and factor analysis
- EEG signal classification based on SVM with improved squirrel search algorithm
- The effect of attentional focusing strategies on EMG-based classification
- Identification of dental pain sensation based on cardiorespiratory signals
- ScatT-LOOP: scattering tetrolet-LOOP descriptor and optimized NN for iris recognition at-a-distance
- A detailed and comparative work for retinal vessel segmentation based on the most effective heuristic approaches
- Visual enhancement of brain cancer MRI using multiscale dyadic filter and Hilbert transformation
- Raspberry Pi implemented with MATLAB simulation and communication of physiological signal-based fast chaff point (RPSC) generation algorithm for WBAN systems
- Short Communication
- How yarn orientation limits fibrotic tissue ingrowth in a woven polyester heart valve scaffold: a case report
