Cluster Computing

, Volume 21, Issue 1, pp 469–480 | Cite as

Energy efficient key agreement scheme for ubiquitous and continuous remote healthcare systems using data mining technique

  • Saleh M. Al-Saleem
  • Aftab AliEmail author
  • Naveed Khan


Wireless body area networks (WBANs) based ubiquitous and fully automated healthcare systems provide a platform to share medical information. Energy efficiency and communication security will increase the confidence of the users in adopting such remote healthcare systems. Key agreement and authentication schemes play an important role in the security of remote healthcare systems. The nodes in a WBAN exchange information in order to complete the key agreement and authentication process. In the literature, numerous schemes have used heavy mathematical calculations or overloaded with excessive information exchange. This paper presents a bloom filter-based key agreement scheme using k-mean clustering for WBANs. The key agreement and authentication is performed in clustered environment using k-mean clustering. This makes the scheme more robust and energy efficient. The keys are generated from the EKG values of the human body. The proposed mechanism is energy efficient and secure, due to its more efficient key generation and less memory utilizations for remote healthcare systems. Moreover, the proposed scheme is analyzed and compared with a state-of-the-art scheme in terms of energy consumption, memory utilizations, processing complexity, and false positive rate (FPR). The results show that the proposed scheme outperform significantly the other scheme by consuming less energy and efficient memory utilization, while achieving a very low FPR and linear running complexity.


Body area network Healthcare Security Privacy Bloom filter Key agreement 



The authors appreciate financial support from KSU deanship of scientific research represented by the research chair of Enterprise Resource planning and business process management.


  1. 1.
    Venkatasubramanian, K., Gupta, S.K.S.: Security for pervasive health monitoring sensor applications. Proceeding of the 4th International Conference Intelligent Sensing & Information Processing. Bangalore pp. 197–202 (2006)Google Scholar
  2. 2.
    Yong, W., Attebury, G., Ramamurthy, B.: A survey of security issues in wireless sensor networks. IEEE Commun. Surv. Tutor. 8, 2–23 (2006)Google Scholar
  3. 3.
    Ali, A., Khan, F.A.: A broadcast-based key agreement scheme using set reconciliation for wireless body area networks. J. Med. Syst. 38(5), 1–12 (2014)CrossRefGoogle Scholar
  4. 4.
    Bloom, B.H.: Space/time trade-offs in Hash coding with allowable errors. Comm. ACM 13(7), 422–426 (1970)CrossRefzbMATHGoogle Scholar
  5. 5.
    Kristof, L., LoBenny, P., Jason, N.G., et al.: Medical healthcare monitoring with wearable and implantable sensors. Presented at 3rd International Workshop on Ubiquitous Computing for Pervasive Healthcare Applications (UbiHealth), Nottingham (2004)Google Scholar
  6. 6.
    Kumar, P., Lee, H.J.: Security issues in healthcare applications using wireless medical sensor networks: a survey. Sensors 12, 55–91 (2011)CrossRefGoogle Scholar
  7. 7.
    Selimis, G., Huang, L., Mass, F., Tsekoura, I., Ashouei, M., Catthoor, F., et al.: lightweight security scheme for wireless body area networks: design, energy evaluation and proposed microprocessor design. J. Med. Syst. 35, 1289–1295 (2011)CrossRefGoogle Scholar
  8. 8.
    Balfanz, D., Smetters, D.K., Stewart, P., Wong, H.C.: Talking to strangers: authentication in ad-hoc wireless networks, Proceeding of the Network and Distributed System Security Symposium, San Diego, pp. 1-13 (2002)Google Scholar
  9. 9.
    Sampangi, R.V., Saurabh, D., Urs, S.R., Sampalli, S.: A security suite for wireless body area networks. Int. J. Netw. Secur. Appl. (IJNSA) 4, 97–116 (2012)Google Scholar
  10. 10.
    He, D., Chen, C., Chan, S., Bu, J., Zhang, P.: Secure and lightweight network admission and transmission protocol for body sensor networks. IEEE J. Biomed. Health Inform. 17(3), 664–674 (2013)CrossRefGoogle Scholar
  11. 11.
    Hu, C., Zhang, N., Li, H., Cheng, X., Liao, X.: Body area network security: a fuzzy attribute-based signcryption scheme. IEEE J. Sel. Areas Commun. 31(9), 37–46 (2013)CrossRefGoogle Scholar
  12. 12.
    Ali, A., Khan, F.A.: Key agreement schemes in wireless body area networks: taxonomy and state-of-the-art. J. Med. Syst. 39, 115 (2015)CrossRefGoogle Scholar
  13. 13.
    Wu, Y., Sun, Y., Zhan, L., Ji, Y.: Low mismatch key agreement based on wavelet-transform trend and fuzzy vault in body area network. Int. J. Distrib. Sens. Netw. 2013, 1–16 (2013)Google Scholar
  14. 14.
    Xin, H., Bangdao, C., Markham, A., Qinghua, W., Zheng, Y., Roscoe, A.W.: Human interactive secure key and identity exchange protocols in body sensor networks. IET Inf. Secur. 7(1), 30–38 (2013)CrossRefGoogle Scholar
  15. 15.
    Juels, A., Sudan, M.: A fuzzy vault scheme, Proceeding of the International Symposium Information Theory, IEEE, Lausanne pp. 408 (2002)Google Scholar
  16. 16.
    Cherukuri, S., Venkatasubramanian, K.K., Gupta, S.K.S.: Biosec: a biometric based approach for securing communication in wireless networks of biosensors implanted in the human body, Proceeding of the Parallel Processing Workshops, Kaohsiung, pp. 432–439 (2003)Google Scholar
  17. 17.
    Ali, A., Khan, F.: An improved EKG-based key agreement scheme for body area networks, Proceeding of the 4th International Conference on Information Security and Assurance (ISA 2010). Miyazaki, Japan, CCIS, vol. 76, pp. 298–308 (2010)Google Scholar
  18. 18.
    Ali, A., Irum, S., Kausar, F., Khan, F.: A cluster-based key agreement scheme using keyed hashing for Body Area Networks. Multimed. Tools Appl. 66, 201–214 (2013)CrossRefGoogle Scholar
  19. 19.
    Orlitsky, A.: Worst-case interactive communication. I. Two messages are almost optimal. IEEE Trans. Inf. Theory 36(5), 1111–1126 (1990)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Venkatasubramanian, K.K., Gupta, S.K.S.: Physiological value-based efficient usable security solutions for body sensor net- works. ACM Trans. Sens. Netw. 6, 1–36 (2016)CrossRefGoogle Scholar
  21. 21.
    Irum, S., Ali, A., Khan, F.A., Abbas, H.: A hybrid security mechanism for intra-WBAN and inter-WBAN communications. Int. J. Distrib. Sens. Netw. 2013, 11 (2013)Google Scholar
  22. 22.
    Ali, A., Khan, F.A.: Energy-efficient cluster-based security mechanism for intra-WBAN and inter-WBAN communications for healthcare applications. EURASIP J. Wirel. Commun. Netw. 2013, 216 (2013)CrossRefGoogle Scholar
  23. 23.
    Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficiant kÅmeans clustering algorithm: analysis and implementation. IEEE Trans. Patern. Anal. Mach. Intell. 24, 881–892 (2002)CrossRefGoogle Scholar
  24. 24.
    MIT PhysioBank.: Accessed 24 Nov 2016
  25. 25.
    Brown, R.G.: Dieharder: a random number testing suite, Accessed 1 Nov 2016
  26. 26.
    Minsky, Y., Trachtenberg, A., Zippel, R.: Set reconciliation with nearly optimal communication complexity. IEEE Trans. Inf. Theory 49, 2213–2218 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Wander, A.S., Gura, N., Eberle, H., Gupta, V., Shantz, S.C.: Energy analysis of public-key cryptography for wireless sensor networks, Proceeding of the Pervasive Computing and Communications, PerCom 2005, Kauai, pp. 324–328 (2005)Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Information Systems, College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia
  2. 2.Department of Computerized-Based TestingNational Center for Assessment in Higher EducationRiyadhSaudi Arabia
  3. 3.School of Computing and Information EngineeringUlster UniversityColeraineUK

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