Modeling and Performance Optimization for Complex Workflow in IoT

This study addresses the growing challenge of time scheduling in Internet of Things (IoT) workflows, where efficiency in time utilization and resource profitability is increasingly constrained by uncertainty. Real-world workflows are characterized by non-deterministic activity execution and resource preparation times, yet existing research often neglects these fundamental dynamics when modeling IoT-based processes. To bridge this gap, we propose a comprehensive modeling and performance optimization framework that explicitly incorporates uncertainty. Methodologically, the framework introduces two distinct types of places to represent activities and resources, with resource properties capturing reusability and preparation processes abstracted as specialized activities. For workflow activities, timing functions are defined to model minimum and maximum execution times, enabling the computation of earliest and latest start times and the identification of critical activities driving overall workflow duration. To mitigate resource conflicts during execution, three alternative resolution strategies are developed and systematically evaluated. Results demonstrate that the proposed approach effectively identifies optimal scheduling strategies under uncertainty, enhancing both temporal efficiency and resource utilization. A workflow case study illustrates the applicability of the framework, offering methodological and practical insights for designing resilient IoT workflow scheduling systems in complex, real-world environments.