A Multi-Zone Temperature Control Model in an IoT Environment for the Cold Chain Using the Elephant Herding Optimization Algorithm

The article presents the development of a multi-zone temperature control model in an Internet of Things (IoT) environment, designed for the cold chain of pharmaceutical products transported by sea. The model is based on the Elephant Herding Optimization (EHO) algorithm, which is used to regulate cooling modes in three independent temperature zones. The study is designed as a simulation-based proof-of-concept rather than as a full-scale experimental validation on an industrial refrigerated container. The proposed framework evaluates whether an EHO-based controller can generate spatially differentiated cooling decisions under synthetic but controlled disturbance scenarios. The variability of sensor readings reflects conditions typical of long-distance maritime transport. These include transitions across different climate zones, changes in solar exposure, and local differences in thermal load. The simulation results indicate that EHO maintains the temperature within the target range required for pharmaceutical cargo, i.e., 0–8 °C. The algorithm responds effectively to local disturbances and to asymmetry between zones. The proposed model provides a basis for further research on autonomous monitoring and control methods in IoT-based cold chain systems; however, validation using measurements from real refrigerated containers, physical heat-transfer modelling, refrigeration-unit response delays, and IoT communication disturbances remains necessary before operational deployment.