False alarm detection using dynamic threshold in medical wireless sensor networks

  • S. SaraswathiEmail author
  • G. R. Suresh
  • Jeevaa Katiravan


Sensor networks suffer from various sensor faults and false measurements in healthcare application and this vulnerability of the delay should handle efficiently and timely response in various application of WSN. For instance, in healthcare application, the false measurements generate false alarms which require to take unnecessary action from the healthcare department. The quality of the health care service can improve in remote healthcare monitoring system by introducing a new approach to identify the true medical condition and differentiate true and false alarms. In this paper, we proposed a novel approach to analysis past historical data collected from various medical sensors to identify the sensor anomaly. The main goal of this approach is to differentiate between true and false alarms effectively. The proposed system analysis the historical data to predicts the sensor value which compares with real sensed values at a time incident. The dynamically adjust the threshold value by comparing the difference between predicted value and historic value to determine the anomaly of sensor value. This system has been worked on huge real-time healthcare dataset and result shows that the new approach has been applied on real healthcare datasets and result of this system shows the detection rate is high and false positive rate is low which conclude that this approach is very useful in WSN application such as health monitoring system and it will be competitive with others.


Medical sensors Healthcare monitoring system Anomaly detection Prediction Sensor fault True alarm Correlation Feature extraction Dynamic threshold 


Compliance with ethical standards

Conflict of interest

Authors declares no conflict of interest.

Ethical statement

This article does not contain any studies with animals performed by any of the authors.


  1. 1.
    Aziz, S. M., & Pham, D. M. (2013). Energy efficient image transmission in wireless multimedia sensor networks. IEEE Communications Letters,17, 1084–1087. Scholar
  2. 2.
    Alemdar, H., & Ersoy, C. (2010). Wireless sensor networks for healthcare: A survey. Computer Networks,54, 2688–2710. Scholar
  3. 3.
    Zhang, Y. Y., Chao, H. C., Chen, M., Shu, L., Park, C. H., & Park, M. S. (2010). Outlier detection and countermeasure for hierarchical wireless sensor networks. IET Information Security,4, 361–373. Scholar
  4. 4.
    Qingquan, S., Fei, H., & Qi, H. (2014). Human movement modeling and activity perception based on fiber-optic sensing system. IEEE Transactions on Human-Machine Systems,44, 743–754. Scholar
  5. 5.
    Crossbow Technology, Inc. MICAz ZigBee Series (MPR2400). Retrieved August 1, 2014, from
  6. 6.
    Hall, M., Witten, I., & Frank, E. (2011). Data mining: Practical machine learning tools and techniques. Burlington, MA: Morgan Kaufmann Publishers.Google Scholar
  7. 7.
    Fritsch, V., Varoquaux, G., Thyreau, B., Poline, J.-B., & Thirion, B. (2012). Detecting outliers in high-dimensional neuroimaging datasets with robust covariance estimators. Medical Image Analysis,16, 1359–1370. Scholar
  8. 8.
    Bahrepour, M., Meratnia, N., Poel, M., Taghikhaki, Z., & Havinga, P. J. M. (2010). Distributed event detection in wireless sensor networks for disaster management. In Proceedings of 2010 2nd international conference on intelligent networking and collaborative systems (INCOS), Thessaloniki, Greece, 24–26 November 2010 (pp. 507–512).Google Scholar
  9. 9.
    Salem, O., Guerassimov, A., Mehaoua, A., Marcus, A., & Furht, B. (2013). Sensor fault and patient anomaly detection and classification in medical wireless sensor networks. In Proceedings of 2013 IEEE international conference on communications (ICC), Budapest, Hungary, 9–13 June 2013 (pp. 4373–4378).Google Scholar
  10. 10.
    Ko, J., Lu, C., Srivastava, M. B., Stankovic, J. A., Terzis, A., & Welsh, M. (2010). Wireless sensor networks for healthcare. Proceedings of the IEEE,98(11), 1947–1960.CrossRefGoogle Scholar
  11. 11.
    Sheng, B., Li, Q., Mao, W., & Jin, W. (2007). Outlier detection in sensor networks. In Proceedings of the 8th ACM international symposium on mobile ad hoc networking and computing, Montreal, QC, Canada, 9–14 September 2007.Google Scholar
  12. 12.
    Burns, A., Greene, B. R., McGrath, M. J., O’Shea, T. J., Kuris, B., Ayer, S. M., et al. (2010). SHIMMER™—A wireless sensor platform for noninvasive biomedical research. IEEE Sensors Journal,10, 1527–1534. Scholar
  13. 13.
    Yilmaz, T., Foster, R., & Hao, Y. (2010). Detecting vital signs with wearable wireless sensors. Sensors,10, 10837–10862. Scholar
  14. 14.
    Bishop, C. M. (2006). Pattern recognition and machine learning. New York, NY: Springer.zbMATHGoogle Scholar
  15. 15.
    Grgic, K., Žagar, D., & Križanović, V. (2012). Medical applications of wireless sensor networks—Current status and future directions. MedicinskiGlasnik,9(1), 23–31.Google Scholar
  16. 16.
    PhysioNet. Retrieved 5 July, 2014, from
  17. 17.
    Miao, X., Song, H., & Biming, T. (2011). Highly efficient distance-based anomaly detection through univariate with PCA in wireless sensor networks. In Proceedings of 2011 IEEE 10th international conference on trust, security and privacy in computing and communications (TrustCom), Changsha, China, 16–18 November 2011 (pp. 564–571).Google Scholar
  18. 18.
    Pham, D. M., Aziz, S. M. (2011). FPGA architecture for object extraction in wireless multimedia sensor network. In Proceedings of seventh international conference on intelligent sensors, sensor networks and information processing (ISSNIP2011), Adelaide, Australia, 6–9 December 2011 (pp. 294–299).Google Scholar
  19. 19.
    Zhang, Y., Chao, H.-C., Chen, M., Shu, L., Park, C. H., & Park, M.-S. (2009). Outlier detection and countermeasure for hierarchical wireless sensor networks. IET Information Security,4, 361–373.CrossRefGoogle Scholar
  20. 20.
    Salem, O., Guerassimov, A., Mehaoua, A., Marcus, A., & Furht, B. (2014). Anomaly detection in medical wireless sensor networks using SVM and linear regression models. International Journal of E-Health and Medical Communications. Scholar
  21. 21.
    Mikhaylov, K., & Tervonen, J. (2012). Energy-efficient routing in wireless sensor networks using power-source type identification. International Journal of Space-Based and Situated Computing,2, 253–266. Scholar
  22. 22.
    Haque, S. A., & Aziz, S. M. (2013). Storage node based routing protocol for wireless sensor networks. In Proceedings of 2013 seventh international conference on sensing technology (ICST), Wellington, New Zealand, 3–5 December 2013 (pp. 725–729).Google Scholar
  23. 23.
    Chipara, O., Lu, C., Bailey, T. C., & Roman, G.-C. (2010). Reliable clinical monitoring using wireless sensor networks: Experiences in a step-down hospital unit. In Proceedings of the 8th ACM conference on embedded networked sensor systems, Zurich, Switzerland, 3–5 November 2010 (pp. 155–168).Google Scholar
  24. 24.
    Miao, X., Jiankun, H., & Biming, T. (2012). Histogram-based online anomaly detection in hierarchical wireless sensor networks. In Proceedings of 2012 IEEE 11th international conference on trust, security and privacy in computing and communications (TrustCom), Liverpool, UK, 25–27 June 2012 (pp. 751–759).Google Scholar
  25. 25.
    Shaari, F., Bakar, A., & Hamdan, A. (2009). A predictive analysis on medical data based on outlier detection method using non-reduct computation. In Advanced data mining and applications (pp. 603–610). Heidelberg: Springer.Google Scholar
  26. 26.
    Aggarwal, C. C., & Yu, P. S. (2001). Outlier detection for high dimensional data. In Proceedings of the 2001 ACM SIGMOD international conference on management of data, Santa Barbara, CA, USA, 21–24 May 2001.Google Scholar
  27. 27.
    Salem, O., Yaning, L., Mehaoua, A., & Boutaba, R. (2014). Online anomaly detection in wireless body area networks for reliable healthcare monitoring. IEEE Journal of Biomedical and Health Informatics,18, 1541–1551. Scholar
  28. 28.
    Chipara, O., Lu, C., Bailey, T. C., & Roman, G. C. (2010). Reliable clinical monitoring using wireless sensor networks: Experiences in a step-down hospital unit. In Proceedings of the 8th ACM conference on embedded networked sensor systems (SenSys’10) (pp. 155–168).Google Scholar
  29. 29.
    Hauskrecht, M., Batal, I., Valko, M., Visweswaran, S., Cooper, G. F., & Clermont, G. (2013). Outlier detection for patient monitoring and alerting. Journal of Biomedical Informatics,46, 47–55. Scholar
  30. 30.
    Vretzakis, G., Georgopoulou, S., Stamoulis, K., Tassoudis, V., Mikroulis, D., Giannoukas, A., et al. (2013). Monitoring of brain oxygen saturation (INVOS) in a protocol to direct blood transfusions during cardiac surgery: A prospective randomized clinical trial. Journal of Cardiothoracic Surgery. Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • S. Saraswathi
    • 1
    Email author
  • G. R. Suresh
    • 2
  • Jeevaa Katiravan
    • 3
  1. 1.Department of CSEPanimalar Engineering CollegeChennaiIndia
  2. 2.Department of Biomedical EngineeringSt Peter’s Insititute of Higher Education & ResearchChennaiIndia
  3. 3.Department of ITVelammal Engineering CollegeChennaiIndia

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