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The proliferation of smart spaces, such as smart buildings, is increasing security vulnerabilities arising from the interplay between cyber and physical entities. We proposed that a representation of the topology of cyber and physical spaces may provide security-relevant contextual characteristics and support the verification of security requirements. We also developed a tool that enables editing and visualising of security-relevant topological characteristics of a building, and verifying that access control policies satisfy security requirements. In this article we report on our experience of applying our approach and the tool to solve practical physical access control problems, and provide some lessons learned for researchers and practitioners.

This work deals with the elimination of sensitivity to sensor orientation in the task of human daily activity recognition using a single miniature inertial sensor. The proposed method detects time intervals of walking, automatically estimating the orientation in these intervals and transforming the observed signals to a "virtual" sensor orientation. Classification results show that excellent performance, in terms of both precision and recall (up to 100%), is achieved, for long-term recordings in real-life settings.

This work deals with the task of human daily activity recognition using miniature inertial sensors. The proposed method is based on the development of a hierarchical dynamic model, incorporating both inter-activity and intra-activity dynamics, thereby exploiting the inherently dynamic nature of the problem to aid the classification task. The method uses raw acceleration and angular velocity signals, directly recorded by inertial sensors, bypassing commonly used feature extraction and selection techniques and, thus, keeping all information regarding the dynamics of the signals. Classification results show a competitive performance compared to state-of-the-art methods.