Stochastic Model for Evaluating Smart Hospitals Performance

Hospital systems must be efficient to prevent loss of human lives. Low latency and high availability of resources are essential features to guarantee quality of service (QoS) in such environments. Taking advantage of Internet of Things (IoT) emergence, smart hospitals apper as a health revolution by capturing and transmitting patient data to physicians in real time through a wireless sensor network. For that, smart hospitals need local and remote servers for processing and storing data efficiently. Commonly, the patient information is shared among different devices, ensuring continuous operation and high availability. However, there is a significant difficulty in evaluating the performance of such systems in real contexts, because the failures are not tolerated (one can not unpluged the system to perform experiments) and the cost of a prototype implementation is high. To cover this issue, this paper adopts the analytical modeling approach to evaluate the performance of a smart hospital system, avoiding the investment in real equipment. Using Stochastic Petri Nets (SPNs), we propose a model to represent the architecture of a smart hospital, and estimate metrics related to the mean response time and resource utilization probability. The model are quite parametric, being possible to calibrate server resource capacity and service time. One can define 13 parameters, allowing to evaluate a large number of different scenarios. Results show that this work has the potential to assist hospital system administrators to plan more optimized architectures according to their needs.