Combinatorial analysis of body sensor networks subject to probabilistic competing failures

Body Sensor Networks (BSNs) have been developed to provide wearable, real-time health monitoring systems for many life-critical applications that require a high level of reliability. Therefore it is significant to analyze the reliability attribute of BSNs, contributing to their reliable designs and operations. This paper models reliability of BSNs subject to probabilistic competing failures. Specifically, in a BSN system, biomedical sensors sense physiological information that is then transmitted through a relay node to a sink device used by decision makers. When the relay fails, these sensors may be isolated in transmission with certain probabilities, depending on whether the remaining power can enable a long-range, direct transmission to the sink. This isolation effect prevents the system from being compromised by further failures of those sensors. However, biomedical sensors may experience propagated failures. If any of the propagated failures occurs before the relay failure, the entire system can fail. Therefore, there exists a competition in time domain between probabilistic failure isolation and propagation effects. This paper considers such probabilistic competing effects and different statistical relationships between local and propagated failures of sensors in reliability analysis of BSNs. A case study is given to illustrate application and advantages of the proposed combinatorial method.