The technological revolution and the evolution of technology have significantly facilitated the applicability of the IoT in various domains, such as healthcare, transportation, agriculture, retail, education, and, especially, higher education, which encompasses countless areas. Petri nets can be a useful tool to model the behavior of an IoT system. The main objective of this paper was to propose, model, and analyze a complex IoT system for higher education. The system involves the integration of IoT devices for monitoring data. An educational cloud was used as a support tool through which tracking, and control actions were implemented both internally, between the cloud and entities, and externally, between the cloud and the IoT. The system was modeled using Petri nets, which are systems with discrete events, and for simulation, we used the Visual Object Net++ package. Using this application, information was obtained in real time, and it was possible to intervene with changes even in the design phase. The diagrams were easy to read and interpret, which is an advantage for the decision-making system. The general structure of the application was based on n entities, where each entity represented a higher education field. In this paper, we discuss at least three fields: economics, computational linguistics, and engineering.

This paper proposes a formally analyzable Cloud-IoT architecture for educational environments, connecting learners, teachers, and institutional administrators to a shared cloud infrastructure through an intermediate IoT layer. The architecture is instantiated in a business-language learning scenario, with four cloud subsystems: a Domain Terminology Repository, an Adaptive Learning Engine, a Monitoring and Assessment Module, and an Administrative and Reporting Module. Its dynamic behavior is formally represented using a timed Petri net incorporating state evolution, transition-delay parameters, feedback, and formal behavioral conditions. The complete model is simulated using Visual Object Net++, and the results are analyzed across the Cloud, IoT, and Client subnets to examine state evolution and marking variation. Additional controlled experiments examine the model under timing variations and increased concurrent Client workloads. A reduced Generalized Stochastic Petri Net (GSPN), representing a Cloud-IoT processing and feedback cycle, is subsequently analyzed using GreatSPN. State-space analysis establishes boundedness, absence of dead markings, and reachability of the response state for the reduced model. Steady-state analysis further shows that reducing an exponentially timed transition rate increases the occupancy of its preceding place. The combined approach provides behavioral evidence for the complete architecture and complementary formal and quantitative results for the reduced model, without extending these results to the complete Petri net.