Reliability modeling of anomaly detection algorithms for Wireless Body Area Networks

Research on Wireless Body Area Networks (WBAN) has witnessed constant advances in small and low power integrated electronic circuits as well as wireless communication schemes for remote monitoring of patients. As such, high reliability anomaly detection has emerged as an important characteristic especially in detection of system faults and abnormal physiological measurements. This work studies the reliability of WBANs by utilizing a Markov-based chain model and considering hardware failure rate, patient health status, and accuracy of anomaly detection algorithms containing Detection Rate (DR), False Positive Rate (FPR), and accuracy of transient fault correction measures. The proposed approach is shown as a powerful evaluation metric in analyzing the reliability and Mean Time to Failure (MTTF) in anomaly detection and specification for WBANs.