IoT-based Emergency Evacuation Systems

The increasing topological changes in the urban environment have caused human civilization to be subjected to an increased risk of emergencies. Hence, designing infrastructures to handle possible emergencies has become an ever-increasing need. The safe evacuation of occupants from the building takes precedence when dealing with the necessary mitigation and disaster risk management. Nowadays, evacuation plans appear as static maps designed by civil protection operators, that provide some pre-selected routes through which pedestrians should move in case of emergency. The static models may work in low congested spacious areas. However, the situation may barely be imagined static in case of a disaster. By simply tracking people in an indoor area, possible congestions can be detected, the best evacuation paths can be periodically re-calculated, and minimum evacuation time under ever-changing emergency conditions can be evaluated. We propose a well-designed internet of things (IoT) infrastructure that is able to provide various solutions in both design-time and real-time. Since the evacuation time of people from a scene of an emergency (e.g. building) is crucial, the IoT-based evacuation infrastructure needs to have an optimization algorithm as their core. In order to reduce the time taken for evacuation, better and more robust exit strategies are developed. This thesis makes the following main contributions: -Addressing to an up to date state of the art class for IoT architectural styles and patterns. -Proposing a set of self-adaptive IoT patterns and assessing their specific quality attributes (fault-tolerance, energy consumption and performance). -Designing an IoT infrastructure and testing its performance in both real-time and design-time applications. -Developing a network flow algorithm that facilitates minimizing the time necessary to evacuate people from a scene of disaster. -Modeling various social agents and their interactions during an emergency to improve the IoT system accordingly. -Evaluating the system by using empirical and real case studies.