Anomaly Detection with Wireless Sensor Networks

The aim of this study is to suggest two automated techniques able to help medical staff to detect earlier than usual some diseases using wireless sensor networks (WSNs). In this context, a patient is equipped with physical sensors which sense health parameters. This WSN will perform some computations and will run an alarm when a disease is suspected. The first technique uses a population protocol to handle data exchanged between motes and provides an efficient algorithm to suggest that a disease is diagnosed on a patient. The algorithm is distributed, i.e., the decision may be done by any sensor dealing with the disease detection. The second technique uses a token algorithm where, some motes are denoted as masters. Each of them is in charge of deciding if a specific disease occurs. This technique is not totally distributed but enhances the network efficiency regarding to the energy consumption, the time execution and the number of exchanged messages.