Abstract
Data inaccuracy hampers the performance of a healthcare system in terms of throughput, end-to-end delay, and energy consumption. Runtime secret key generation is highly required during communication between a controller and healthcare sensors in order to protect and maintain accuracy of sensitive data of a human. Runtime secret key generation is possible after getting the physiological and behavioral information from a human. Therefore, the healthcare sensors with different sensing capabilities collect biometrics like heartbeat rate, blood pressure, and iris and generate runtime secret key by extracting features from these biometrics to communicate with the controller. On the other hand, the controller maintains a secure biometric template so that the generated key by a healthcare sensor can be verified. Thus biometric-based communication helps to protect sensitive data as well as helps to authenticate the communicators in real-time environment.
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References
M.S. Obaidat, S. Misra, Principles of Wireless Sensor Networks (Cambridge University Press, Cambridge/New York, 2014)
S. Misra, V. Tiwari, M.S. Obaidat, LACAS: learning automata-based congestion avoidance scheme for healthcare wireless sensor networks. IEEE J. Sel. Area Commun. 27(4), 466–479 (2009)
M.S. Obaidat, B. Sadoun, Verification of computer users using keystroke dynamics. IEEE Trans. Syst. Man Cybernet. Part B 27(2), 261–269 (1997)
http://www.idg.com.au/mediareleases/27490/healthcare-cyber-security-market-to-benefit-from/
Technavio: Global Biometric Sensors Market 2016–2020. https://www.technavio.com/report/global-sensors-global-biometric-sensors-market-2016-2020
6Wresearch partnering growth. http://www.6wresearch.com/press-releases/global-biometric-bio-metric-market.html
Olea Sensor Networks. http://www.oleasys.com/
Health in the home. http://findbiometrics.com/biometric-healthcare-in-the-home-302190/
S. Prabhakar, S. Pankanti, A.K. Jain, Biometric recognition: security and privacy concerns. IEEE Secur. Priv. 1(2), 33–42 (2003)
D. Giri, R.S. Sherratt, T. Maitra, A novel and efficient session spanning biometric and password based three-factor authentication protocol for consumer usb mass storage devices. IEEE Trans. Consum. Electron. 62(3), 283–291 (2016)
M.S. Obaidat, D.T. Macchairllo, An on-line neural network system for computer access security. IEEE Trans. Ind. Electron. 40(2), 235–242 (1993)
I. Traore, I. Woungang, B. Khalilian, M.S. Obaidat, A. Ahmed, Dynamic sample size detection in learning command line sequence for continuous authentication. IEEE Trans. Syst. Man Cybern. B 42(5), 1343–1356 (2012)
M.A. Zahhad, S.M. Ahmed, S.N. Abbas, Biometric authentication based on PCG and ECG signals: present status and future directions. SIViP 8(4), 739–751 (2014)
M.S. Obaidat, N. Boudriga, Security of e-Systems and Computer Networks (Cambridge University Press, 2007)
M.S. Obaidat, B. Sadoun, in Biometrics: Personal Identification in Networked Society. Keystroke dynamics based identification (Kluwer, Boston, 1999), pp. 213–229
M.F. Zanuy, On the vulnerability of biometric security systems. IEEE Aerosp. Electron. Syst. Mag. 19(6), 3–8 (2004)
C.C.Y. Poon, Y.-T. Zhang, S.-D. Bao, A novel biometrics method to secure wireless body area sensor networks for telemedicine and m-health. IEEE Commun. Mag. 44(4), 73–81 (2006)
S.D. Bao, C.C.Y. Poon, Y.T. Zhang, L.F. Shen, Using the timing information of heartbeats as an entity identifier to secure body sensor network. IEEE Trans. Inf. Technol. Biomed. 12(6), 772–779 (2008)
S. Cherukuri, K.K. Venkatasubramanian, S.K.S. Gupta, Biosec: a biometric based approach for securing communication in wireless networks of biosensors implanted in the human body, in Proc. of International Conference on Parallel Processing Workshops, Taiwan, pp. 432–439, 2003
F. Miao, L. Jiang, Y. Li, Y.T. Zhang, Biometrics based novel key distribution solution for body sensor networks, in Proc. of Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, pp. 2458–2461, 2009
H. Wang, H. Fang, L. Xing, M. Chen, An integrated biometric-based security framework using wavelet-domain HMM in wireless body area networks (WBAN), in Proc. of IEEE International Conference on Communications (ICC), Kyoto, pp. 1–5, 2011
M.S. Crouse, R.G. Baraniuk, R.D. Nowak, Hidden Markov modelsfor wavelet-based signal processing, in Signals, Systems and Computers, the Thirtieth Asilomar Conference, pp. 1029–1035, vol. 2, 1996
S.-D. Bao, Y.-T. Zhang, L.-F. Shen, Physiological signal based entity authentication for body area sensor networks and mobile healthcare systems, in Proc. of IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, pp. 2455–2458, 2005
B. Shanthini, S. Swamynathan, Genetic-based biometric security system for wireless sensor-based health care systems, in Proc. of International Conference on Recent Advances in Computing and Software Systems, Chennai, pp. 180–184, 2012
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Obaidat, M.S., Maitra, T., Giri, D. (2019). Biometrics Based on Healthcare Sensors. In: Obaidat, M., Traore, I., Woungang, I. (eds) Biometric-Based Physical and Cybersecurity Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-98734-7_13
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