Distributed diagnosis over wireless sensors networks

This paper studies how to detect anomalies in a distributed manner by using wireless sensor networks (WSNs). We focus on a medical context, where the existing works generally provide large environments to monitor patients using sensors as simple transducers. Those devices forward sensed health parameters to a main base station. This station collects received data and may perform some computations. In this paper, we perform some distributed tasks on the sensors. We propose a distributed algorithm, which allows to raise alarms under some initial rules to alert efficiently medical staff in case of critical situations without needless warnings. Each mote monitors a parameter. When this parameter reaches an abnormal value, the mote communicates with other motes in order to check if it is a ‘local’ anomaly or if the patient is in an abnormal situation. In such cases an alarm is raised. We implemented our algorithm over a network of micaZ sensors running under TinyOS. The obtained results show promising perspectives.