An IoT-Enabled Cyber-Physical System Architecture with Adaptive Control: A Case Study in Household Bio-Fermentation

The emergence of the Internet of Things (IoT) in Cyber-Physical Systems (CPS) has advanced real-time monitoring in smart agriculture; however, a critical gap exists in household bio-fermentation, where existing IoT-based systems lack adaptive mechanisms to manage the energy–stability trade-off under resource constraints. This study addresses this limitation by developing a three-layer IoT-enabled CPS architecture integrated with an optimization-guided adaptive scheduling algorithm that minimizes energy consumption while maintaining process stability above a γ threshold. Five 30-L fermenters were tested over 14 days under different headspace conditions using pH, temperature, and electrical conductivity sensors to evaluate physicochemical, microbiological, and reliability responses. The adaptive scheduling model reduced fermentation time by 30% while maintaining system availability above 95%, and the HS50 headspace condition yielded the most stable process behavior and the highest nutrient quality, meeting national organic fertilizer standards. The novelty lies in adapting optimization-based scheduling to resource-constrained household bio-fermentation and validating it against biological outcomes, thereby linking CPS reliability indicators with physicochemical and microbial performance. This work contributes theoretical insight into CPS optimization and offers a practical, scalable approach for sustainable smart agriculture.