The increasing topological changes in the urban environment have caused human civilization to be subjected to an increased risk of emergencies. Fires, earthquakes, floods, hurricanes, overcrowding, or and even pandemic viruses endanger human lives. 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.The static emergency map exposes several limitations such as: i) ignoring abrupt congestion, obstacles or dangerous routes and areas; ii) leading all pedestrians to the same route and making specific areas highly crowded; iii) ignoring the individual movement behavior of people and special categories (e.g. elderly, children, disabled); iv) lack of providing proper training for security operators in various scenarios; v) lack of providing a comprehensive situational awareness for evacuation managers.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. Using a well-designed internet of things (IoT) infrastructure can provide various solutions in both design-time and real-time. At design-time, a building architecture can be assessed regarding safety conditions, even before its (re-) construction. Simulations are among feasible solutions to assess the evaluability of buildings and the feasibility of evacuation plans.At design-time, an IoT-based evacuation system provides: i) Safety considerations for building architecture in early (re-) construction phase; ii) Finding out the building dimensions that lead to an optimum evacuation performance; iii) Bottleneck discovery that is tied with the building characteristics; iv) Comparing various routing optimization models to pick the best match one as a base of real-time evacuation system; v) Visualizing dynamic evacuation executions to demonstrate a variety of scenarios to security operators and train them. In real-time, an IoT architecture supports the gathering of data that will be used for dynamic monitoring and evacuation planning. At real-time, an IoT-based evacuation system provides: i) Optimal solutions that can be continuously updated, so evacuation guidelines can be adjusted according to visitors position that evolves over time; ii) Paths that become suddenly unfeasible can automatically be discarded by the system; iii) The model can be incorporated into a mobile app supporting emergency units to evacuate closed or open spaces.Since the evacuation time of people from a scene of an emergency (e.g. building) is crucial, IoT-based evacuation infrastructures need to have an optimization algorithm as their core. In order to reduce the time taken for evacuation, a better and more robust exit strategy is developed. Some algorithms are used to model participating agents for their exit patterns and strategies and in order to evaluate their movement behavior based on performance, efficiency, and practicality attributes. The algorithms normally provide a way to evacuate the occupants as quickly as possible. While this research and all associated experiences are carried out in Italy, we see the problem from an international viewpoint. Within this thesis, we carried out the following research and experiments to analyze and develop an IoT-based emergency evacuation system:The first two chapters present systematic mapping studies to review the state of the art and help to design high-quality IoT architectures. More specifically, chapter one investigates on IoT software architectural styles, and chapter two assesses the architectural fault-tolerance. Chapter three proposes some adaptive architectural styles and their associated quality of energy consumption. After taking the preliminary design decisions about the architecture, in chapter four we propose a core computational component to be in charge of minimizing the time necessary to evacuate people from a building. We developed a network flow algorithm that decomposes the building space and time into finite elements: unit cells and time slots. In chapter five, we assessed the effectiveness of the IoT system in providing good real-time and design-time solutions. Chapter six focuses on real-time performance and minimizes computational and evacuation delays to a minimum, by using a queuing network.During our research, we designed and implemented a hardware and software IoT infrastructure. We installed sensors throughout the selected building, whose data constantly feed into the algorithm to show the best evacuation routes to the occupants.We further realized that such a system may lack the accuracy since: i) a pure optimization approach can lack realism as building occupants may not evacuate immediately; ii) occupants may not always follow the recommended optimal paths due to various behavioral and organizational issues; iii) the physical space may prevent an effective emergency evacuation. Therefore, in chapter seven we introduced a simulation-optimization approach. The approach allows us to test more realistic evacuation scenarios and compare them with an optimal approach. We simulated the optimized Netflow algorithm under different realistic behavioral agent-based modeling (ABM) constraints, such as social attachment and improved IoT system accordingly.This thesis makes the following main contributions:Contributions on new and legitimate IoT architectures: - Addressing 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.Algorithmic contribution: - Developing a network flow algorithm that facilitates minimizing the time necessary to evacuate people from a scene of a disaster.Evaluation / experimentation environment contributions: 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.