Internet of Things (IoT) is spreading increasingly in different areas of application. Accordingly, IoT also gets deployed in health care including ambient assisted living, telemedicine or medical smart homes. However, IoT also involves risks. Next to increased security issues also safety concerns are occurring. Deploying health care sensors and utilizing medical data causes a high need for IoT architectures free of vulnerabilities in order to identify weak points as early as possible. To address this, we are developing a safety and security analysis approach including a standardized meta model and an IoT safety and security framework comprising a customizable analysis language.

More and more devices are being interconnected, thus extending the use of Internet of Things (IoT) systems. However, the larger the networks are the more vulnerable and inscrutable they become. This is a significant challenge especially when IoT is used in safety-and security-critical areas. In these areas, a flawless architecture must be guaranteed already in the design phase. Therefore, a structured possibility is needed to scan models completely for vulnerabilities as early as possible. We developed a pattern recognition framework (PRF) that enables the definition of design patterns and anti-patterns. These patterns are used for a holistic and automated identification of flaws in IoT models during design phase and enable a design optimization.

Data and sensor fusion can enable clinical healthcare systems to improve conditions of a patient. However, hospitals are not the only application field of connected medical devices. Domestic monitoring gets more important day by day and applies Internet of Things with mobile sensors, like wearables. Through data processing data is transferred to smart data and personalized recommendations are improvable, if sensors can be chosen individually. Therefore, we developed a generic medical sensor framework which is able to merge any needed sensor and collect data to improve personalized health of an individual. To evaluate our framework and to prove the added value of sensor fusion we present a sensor-based stress detection game.

Enterprise Architecture Management (EAM) deals with the assessment and development of business processes and IT components. Through the analysis of as-is and to-be states the information flow in organizations is optimized. Thus EAM analyses are an essential part in the EAM cycle. To cover the needs of an architect the analyses pursue different goals and utilize different techniques. In this work we examine the different EA analysis approaches according to their characteristics and requirements. For that purpose we design a generic analysis language which can be used for their description. In order to manage the numerous approaches from literature we develop a categorization. The categories are created based on the goals, constructs and kind of results. We propose a twodimensional classification into functional and technical categories. The goal is to provide a common description for EA analyses for an easy access to their goals and execution requirements.