Semi Real-time Data Cleaning of Spatially Correlated Data in Traffic Sensor Networks

Federica Rollo, Chiara Bachechi, Laura Po

Abstract: The new Internet of Things (IoT) era is submerging smart cities with data. Various types of sensors are widely used to collect massive amounts of data and to feed several systems such as surveillance, environmental monitoring, and disaster management. In these systems, sensors are deployed to make decisions or to predict an event. However, the accuracy of such decisions or predictions depends upon the reliability of the sensor data. By their nature, sensors are prone to errors, therefore identifying and filtering anomalies is extremely important. This paper proposes an anomaly detection and classification methodology for spatially correlated data of traffic sensors that combines different techniques and is able to distinguish between traffic sensor faults and unusual traffic conditions. The reliability of this methodology has been tested on real-world data. The application on two days affected by car accidents reveals that our approach can detect unusual traffic conditions. Moreover,