The convergence of Cloud Computing and the Internet of Things (IoT) underpins modern, scalable, and data-intensive systems. Cloud-of-Things (CoT) applications are now pervasive across healthcare, smart cities, and industrial automation. Nevertheless, their development remains fragmented and technically demanding. Current CoT development approaches require substantial expertise in heterogeneous platforms, protocols, and deployment technologies. They offer limited model-driven support, weak automation, and poor alignment with low-code principles. As a result, developing CoT applications remains effort-intensive and error-prone. This study aims to simplify CoT application development by introducing the Model of Things (MoT). MoT integrates UML-based modeling with low-code execution concepts to reduce technical barriers while preserving automation and deployment feasibility. We designed a custom UML profile comprising nine CoT-specific stereotypes and an automated transformation pipeline. MoT was evaluated using one end-to-end case study and a Technology Acceptance Model (TAM) questionnaire with 12 participants with software modeling experience. In the evaluated case study, MoT generated executable application components from UML models, while users supplied the required configuration parameters and deployment settings. TAM results indicate strong acceptance: all 12 participants rated MoT positively for usefulness and ease of use, and qualitative feedback emphasized reduced complexity and faster development. MoT demonstrates that combining UML modeling with low-code execution is feasible and valuable for CoT systems. By enabling executable models and lowering entry barriers, MoT advances practical, model-driven CoT engineering and opens avenues for broader industrial adoption.
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Welter et al. (2026) studied this question.
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