Human Cognitive Models for Heritage Site Monitoring

Abstract

Integrating evidence from a single sensor over time is becoming more common due to the Internet of Things (IoT). It can play a critical role in Heritage site monitoring, as networks of sensors are required to catalog and analyze data over extended periods of time. Researchers have often adopted the mechanisms used for multi-source integration, such as Bayesian conditioning and Dempster- Shafer reasoning. Research in human cognitive models provides an interesting alternative insights for accumulating evidence over time. We used this research as a foundation for the current approach which integrates the set theoretic nature of Dempster-Shafer theory with an estimation structure based on Kalman filtering. It is well suited for applications to Wide-area Sensor Networks (WSN) that are commonly found in heritage sites.