As the medical body sensor network (BSN) is usually resource limited and vulnerable to environmental effects and malicious attacks, faulty sensor data arise inevitably which may resultin false alarms, faulty medical diagnosis, and even serious misjudgment. Thus, faulty sensor data should be detected and removed as much as possible before being utilized for medical diagnosis making. Most available works directly employed fault detection schemes developed in traditional wireless sensor network (WSN) for body sensor fault detection. However, BSNs adopt a very limited number of sensors for vital information collection, lacking the information redundancy provided by densely deployed sensor nodesin traditional WSNs. In light of this, a Dual sensor network model based sensor fault detection scheme is proposed in this project, which relies on double sensor data for establishing the conditional probability distribution of body sensor readings, rather than the redundant information collected from a large number of sensors. Furthermore, the Dual sensor network-based scheme enables us to minimize the inaccuracy rate by optimally tuning the threshold for fault detection. Extensive online dataset has been adopted to evaluate the performance of our fault detection scheme, which shows that our scheme possesses a good fault detection accuracy and allow false alarm rate.

Arduino UNO, Health Care, Radio Frequency, W.S.N.

[1] O. Salem, A. Guerassimov, A. Mehaoua, A. Marcus, and B. Furht, “Sensor fault and patient anomaly detection and classification in medical wireless sensor networks,” in Proc. IEEE Int. Conf. Commun., 2013, pp. 4373–4378.
[2] O. Salem, Y. Liu, and A. Mehaoua, “Anomaly detection in medical wireless sensor networks,” J. Comput. Sci. Eng., vol. 7, no. 4, pp. 272–284, 2013.
[3] J. Branch, B. Szymanski, C. Giannella, and R. Wolff, “In-network outlier detection in wireless sensor networks,” in Proc. IEEE Int. Conf. Distrib. Comput. Syst., 2006, pp. 51–57.
[4] M. Zhang, S. Shi, H. Gao, and J. Li, “Unsupervised outlier detection in sensor networks using aggregation tree,” in Proc. Adv. Data Mining Appl., 2007, pp. 158–169.
[5] V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Comput. Surveys, vol. 41, no. 3, pp. 1–72, 2009.
[6] J. Liu and N. Kato, “A Markovian analysis for explicit probabilistic stopping based information propagation in post-disaster ad hoc mobile networks,” IEEE Trans. Wireless Commun., vol. 15, no. 1, pp. 81–90, Jan. 2016.
[7] Y. Zhang, N. Meratnia, and P. Havinga, “Outlier detection techniques for wireless sensor networks: A survey,” IEEE Commun. Survey Tuts., vol. 12, no. 2, pp. 159–170, Second Quarter 2010.
[8] W. Wu, X. Cheng, M. Ding, K. Xing, F. Liu, and P. Deng, “Localized outlying and boundary data detection in sensor networks,” IEEE Trans. Knowl. Data Eng., vol. 19, no. 8, pp. 1145–1157, Aug. 2007.
[9] B. Sheng, Q. Li, W. Mao, and W. Jin, “Outlier detection in sensor networks,” in Proc. Mobile Ad Hoc Netw. Comput., 2007, pp. 219–228.
[10] T. Palpanas, D. Papadopoulos, V. Kalogeraki, and D. Gunopulos, “Distributed deviation detection in sensor networks,” ACM Special Interest Group Manag. Data, vol. 32, no. 4, pp. 77–82, 2003.
[11] S. Ramaswamy, R. Rastogi, and K. Shim, “Efficient algorithms for mining outliers from large data sets,” ACM Special Interest Group Manag. Data, vol. 29, no. 2, pp. 427–438, 2000.
[12] T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Trans. Inf. Theory, vol. IF-13, no. 1, pp. 21–27, Jan. 1967.
[13] S. Rajasegarar, C. Leckie, M. Palaniswami, and J. C. Bezdek, “Distributed anomaly detection in wireless sensor networks,” in Proc. IEEE Int. Conf. Commun. Syst., 2006, pp. 1–5.
[14] B. Krishnamachari and S. Iyengar, “Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks,” IEEE Trans. Comput., vol. 53, no. 3, pp. 241–250, Mar. 2004.
[15] X. Luo, M. Dong, andY. Huang, “On distributed fault-tolerant detection in wireless sensor networks,” IEEE Trans. Comput., vol. 55, no. 1, pp. 58–70, Jan. 2006.
[16] A. Annichini, E. Asarin, and A. Bouajjani, “Symbolic techniques for parametric reasoning about counter and clock systems,” in Proc. Comput. Aided Verification, 2000, pp. 419–434.