In a wide range of settings, various interconnected devices and sensors are used to collect data (e.g., cameras, piezoelectric, acoustic, environmental, and magnetic sensors). Data obtained from these sensors are used to support various types of services dependent on the objective of the sensor network. The accuracy and integrity of collected data are crucial to the reliability of such services and the optimal performance of the network. Recent research has shown that hackers could compromise the sensors and send misleading data to the controller, potentially causing severe disruptions. For example, in a smart-city setting where the sensors are used to monitor traffic patterns, hackers could cause significant traffic problems and compromise the entire operation of the smart city services.
This project breaks the problem of integrity assurance in an Internet of Things (IoT) network into a two-stage process. The first stage focuses on the detection and identification of anomalous data. The second stage focuses on decision support in the presence of anomalous data, i.e. what to do once untrustworthy data has been detected and how to adjust the decision-making process in the presence of untrustworthy data. This project proposes a joint effort by ULL and UVA to tackle this problem. The ULL team will focus on the detection side of the problem, and the UVA team will focus on the decision support side of the problem. This project is a combination of two Year 6 projects which were addressing these issues separately. The combined project will leverage the strengths of each team and ultimately develop an end-to-end method for detecting and reacting to anomalous data in an IoT environment.