Using temporal correlation and time series to detect missing activity-driven sensor events

Increasing numbers of sensors are being deployed in environments to monitor our behaviours and environmental phenomena. Missing data is an inevitable problem in almost every sensorised environment, due to physical failure, poor connection, or dislodgement. This results in an incomplete view of the real-world, leading to poor prediction and consequently, degraded quality of system services. This paper explores generic solutions towards detecting missing data on event-driven sensors using both temporal correlation and time series analysis. The solutions are evaluated on a real-world dataset and achieve promising results with accuracy around 80%.