Trustworthiness analysis of sensor data in cyber-physical systems

A Cyber-Physical System (CPS) is an integration of sensor networks with informational devices. CPS can be used for many promising applications, such as traffic observation, battlefield surveillance, and sensor-network-based monitoring. One key issue in CPS research is trustworthiness analysis of sensor data. Due to technology limitations and environmental influences, the sensor data collected by CPS are inherently noisy and may trigger many false alarms. It is highly desirable to sift meaningful information from a large volume of noisy data. In this study, we propose a method called Tru-Alarm, which increases the capability of a CPS to recognize trustworthy alarms. Tru-Alarm estimates the locations of objects causing alarms, constructs an object-alarm graph and carries out trustworthiness inference based on the graph links. The study also reveals that the alarm trustworthiness and sensor reliability could be mutually enhanced. The property is used to help prune the large search space of object-alarm graph, filter out the alarms generated by unreliable sensors and improve the algorithmʼs efficiency. Extensive experiments are conducted on both real and synthetic datasets, and the results show that Tru-Alarm filters out noise and false information efficiently and effectively, while ensuring that no meaningful alarms are missed.

► The trustworthiness model is proposed to measure the sensor reliability and alarm confidence.
► The object-alarm graph is constructed to model the relationship between the alarms and related objects.
► The system employs two pruning techniques for efficient trustworthiness inference.
► We conduct extensive experiments on both real and synthetic datasets.