Data reconciliation in a smart home sensor network

This paper describes a data-driven approach to sensor data validation. The data originates from a network of sensors embedded in an indoor environment such as an office, home, factory, public mall or airport. Data analysis is performed to automatically detect events and classify activities taking place within the environment. Sensor failure and in particular intermittent failure, caused by electrical interference, undermines the inference processes. PCA and CCA are compared for detecting intermittent faults and masking such failures. The fault detection relies on models built from historical data. As new sensor observations are collected the model is updated and compared to that previously estimated, where a difference is indicative of a failure.

► The work has for objective to detect faults in a sensor network.
► Two techniques, PCA and CCA, are compared to detact permanent and transient faults.
► Results show CCA performs better in terms of transient faults.