Empirical studies of ECG Multiple Fiducial-points based Binary Sequence Generation (MFBSG) algorithm in e-Health Sensor Platform

Kashif Saleem; Haider Abbas; Jalal Al-Muhtadi; Mehmet A. Orgun; Rajan Shankaran; Guanglou Zhang

doi:10.1109/LCN.2016.053

Empirical studies of ECG Multiple Fiducial-points based Binary Sequence Generation (MFBSG) algorithm in e-Health Sensor Platform

Kashif Saleem, Haider Abbas, Jalal Al-Muhtadi, Mehmet A. Orgun, Rajan Shankaran, Guanglou Zhang

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

8 Citations (Scopus)

Abstract

In this paper, the Arduino Uno based e-Health Sensor Platform V2.0 by Libelium is enhanced with the ECG Multiple Fiducial-points based Binary Sequence Generation (MFBSG) algorithm to secure wireless sensors within Wireless Body Area Networks (WBANs). The eHealth kit includes biometric and other medical related functionalities to monitor the human body conditions by utilizing 10 different sensors that includes electrocardiogram (ECG). Therefore, MF-BSG algorithm generates secret keys for encryption, decryption and authentication purposes. Furthermore, we have analyzed the performance of the enhanced version of the toolkit and compared it with the Enhanced Very Fast Decision Tree (EVFDT) mechanism based health care systems in terms attack detection accuracy. The results confirm that the monitored data is transferred to the required destination in a more accurate and secure manner as compared to previous algorithms.

Original language	English
Title of host publication	LCN Workshops 2016
Subtitle of host publication	Proceedings of the 2016 IEEE 41st Conference on Local Computer Networks
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	236-240
Number of pages	5
ISBN (Electronic)	9781509023479, 9781509023462
ISBN (Print)	9781509023486
DOIs	https://doi.org/10.1109/LCN.2016.053
Publication status	Published - 2016
Event	41st IEEE Conference on Local Computer Networks Workshops, LCN Workshops 2016 - Dubai, United Arab Emirates Duration: 7 Nov 2016 → 10 Nov 2016

Other

Other	41st IEEE Conference on Local Computer Networks Workshops, LCN Workshops 2016
Country/Territory	United Arab Emirates
City	Dubai
Period	7/11/16 → 10/11/16

Keywords

applications
biometric
communication
eHealth
electrocardiogram
encryption
medical
security
sensor
wireless body area network (WBAN)

Access to Document

10.1109/LCN.2016.053

Cite this

Saleem, K., Abbas, H., Al-Muhtadi, J., Orgun, M. A., Shankaran, R., & Zhang, G. (2016). Empirical studies of ECG Multiple Fiducial-points based Binary Sequence Generation (MFBSG) algorithm in e-Health Sensor Platform. In LCN Workshops 2016 : Proceedings of the 2016 IEEE 41st Conference on Local Computer Networks (pp. 236-240). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/LCN.2016.053

Saleem, Kashif ; Abbas, Haider ; Al-Muhtadi, Jalal et al. / Empirical studies of ECG Multiple Fiducial-points based Binary Sequence Generation (MFBSG) algorithm in e-Health Sensor Platform. LCN Workshops 2016 : Proceedings of the 2016 IEEE 41st Conference on Local Computer Networks. Piscataway, NJ : Institute of Electrical and Electronics Engineers (IEEE), 2016. pp. 236-240

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title = "Empirical studies of ECG Multiple Fiducial-points based Binary Sequence Generation (MFBSG) algorithm in e-Health Sensor Platform",

abstract = "In this paper, the Arduino Uno based e-Health Sensor Platform V2.0 by Libelium is enhanced with the ECG Multiple Fiducial-points based Binary Sequence Generation (MFBSG) algorithm to secure wireless sensors within Wireless Body Area Networks (WBANs). The eHealth kit includes biometric and other medical related functionalities to monitor the human body conditions by utilizing 10 different sensors that includes electrocardiogram (ECG). Therefore, MF-BSG algorithm generates secret keys for encryption, decryption and authentication purposes. Furthermore, we have analyzed the performance of the enhanced version of the toolkit and compared it with the Enhanced Very Fast Decision Tree (EVFDT) mechanism based health care systems in terms attack detection accuracy. The results confirm that the monitored data is transferred to the required destination in a more accurate and secure manner as compared to previous algorithms.",

keywords = "applications, biometric, communication, eHealth, electrocardiogram, encryption, medical, security, sensor, wireless body area network (WBAN)",

author = "Kashif Saleem and Haider Abbas and Jalal Al-Muhtadi and Orgun, {Mehmet A.} and Rajan Shankaran and Guanglou Zhang",

year = "2016",

doi = "10.1109/LCN.2016.053",

language = "English",

isbn = "9781509023486",

pages = "236--240",

booktitle = "LCN Workshops 2016",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "41st IEEE Conference on Local Computer Networks Workshops, LCN Workshops 2016 ; Conference date: 07-11-2016 Through 10-11-2016",

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Saleem, K, Abbas, H, Al-Muhtadi, J, Orgun, MA , Shankaran, R & Zhang, G 2016, Empirical studies of ECG Multiple Fiducial-points based Binary Sequence Generation (MFBSG) algorithm in e-Health Sensor Platform. in LCN Workshops 2016 : Proceedings of the 2016 IEEE 41st Conference on Local Computer Networks. Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 236-240, 41st IEEE Conference on Local Computer Networks Workshops, LCN Workshops 2016, Dubai, United Arab Emirates, 7/11/16. https://doi.org/10.1109/LCN.2016.053

Empirical studies of ECG Multiple Fiducial-points based Binary Sequence Generation (MFBSG) algorithm in e-Health Sensor Platform. / Saleem, Kashif; Abbas, Haider; Al-Muhtadi, Jalal et al.
LCN Workshops 2016 : Proceedings of the 2016 IEEE 41st Conference on Local Computer Networks. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2016. p. 236-240.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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AU - Abbas, Haider

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AU - Orgun, Mehmet A.

AU - Shankaran, Rajan

AU - Zhang, Guanglou

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N2 - In this paper, the Arduino Uno based e-Health Sensor Platform V2.0 by Libelium is enhanced with the ECG Multiple Fiducial-points based Binary Sequence Generation (MFBSG) algorithm to secure wireless sensors within Wireless Body Area Networks (WBANs). The eHealth kit includes biometric and other medical related functionalities to monitor the human body conditions by utilizing 10 different sensors that includes electrocardiogram (ECG). Therefore, MF-BSG algorithm generates secret keys for encryption, decryption and authentication purposes. Furthermore, we have analyzed the performance of the enhanced version of the toolkit and compared it with the Enhanced Very Fast Decision Tree (EVFDT) mechanism based health care systems in terms attack detection accuracy. The results confirm that the monitored data is transferred to the required destination in a more accurate and secure manner as compared to previous algorithms.

AB - In this paper, the Arduino Uno based e-Health Sensor Platform V2.0 by Libelium is enhanced with the ECG Multiple Fiducial-points based Binary Sequence Generation (MFBSG) algorithm to secure wireless sensors within Wireless Body Area Networks (WBANs). The eHealth kit includes biometric and other medical related functionalities to monitor the human body conditions by utilizing 10 different sensors that includes electrocardiogram (ECG). Therefore, MF-BSG algorithm generates secret keys for encryption, decryption and authentication purposes. Furthermore, we have analyzed the performance of the enhanced version of the toolkit and compared it with the Enhanced Very Fast Decision Tree (EVFDT) mechanism based health care systems in terms attack detection accuracy. The results confirm that the monitored data is transferred to the required destination in a more accurate and secure manner as compared to previous algorithms.

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ER -

Saleem K, Abbas H, Al-Muhtadi J, Orgun MA , Shankaran R, Zhang G. Empirical studies of ECG Multiple Fiducial-points based Binary Sequence Generation (MFBSG) algorithm in e-Health Sensor Platform. In LCN Workshops 2016 : Proceedings of the 2016 IEEE 41st Conference on Local Computer Networks. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). 2016. p. 236-240 doi: 10.1109/LCN.2016.053

Empirical studies of ECG Multiple Fiducial-points based Binary Sequence Generation (MFBSG) algorithm in e-Health Sensor Platform

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