Increasing sensor reliability through confidence attribution

The reliability of wireless sensor networks (WSN) is getting increasing importance as this kind of networks are becoming the communication base for many cyber-physical systems (CPS). Such systems rely on sensor data correctness to make decisions; therefore, faulty data can lead such systems to take wrong actions. Errors can be originated by sensor’s hardware failures or software bugs and also from the intentional interference of intruders. The gateways that connect such WSN to the Internet are natural intruders’ targets as they usually run conventional operating systems and communication protocols. This work proposes a confidence attribution scheme, based on lightweight predictors running on the sensors. The solution also proposes a parameterizable formula, in order to stamp every value sent by a sensor with a confidence level, calculated upon the values of a subset of correlated sensors. This work also presents an algorithm that can identify a defective sensor into its subset. The use of predictors and confidence attribution are proposed as the basis of a mechanism that increases the WSN resilience against sensor failure or bad data injection by intruders. Several simulations were performed to evaluate the detection efficiency against different types of sensor errors. This work also analyses mechanisms to deal with concept drifts in the WSN lifetime.