IoT-Driven Cold Chain Supply Optimization Utilizing Embedded Systems and Adaptive Communication Protocols

Abstract

To enhance the information transparency and monitoring efficiency of the cold chain logistic supply chain, the study adopts the Internet of Things and embedded technology to construct a cold chain logistic supply chain optimization strategy. Firstly, a cold chain logistic supply chain monitoring model based on Internet of Things is constructed. The model covers the whole chain of “sensing-transmission-application”. The sensing layer adopts heterogeneous sensor networks and follows the density optimization model for deployment. The network layer utilizes hybrid communication architecture and adaptive routing protocol to ensure efficient and stable data transmission. The application layer realizes the comprehensive supervision of the cold chain logistic supply chain through the three-level data fusion model. Meanwhile, based on embedded technology, it builds intelligent terminals, integrating multi-sensor modules and intelligent peripherals. The results showed that, in the proposed model, the temperature error in the sensitive area decreased by 22.2%, from 0.3 ℃ to 0.1 ℃, and the humidity error decreased by 50%, from 3.0% RH to 1.5%. When the channel utilization was 70%, the transmission delay was 180.6 ms, the transportation cost was reduced by 16.5%, the inventory cost was reduced by 21.6%, and the total loss was reduced by 50.6% (vaccine loss was reduced by 62%). According to the study, the suggested approach considerably raises the cold chain logistic supply chain's monitoring accuracy, data transmission efficiency, path planning rationality, and inventory management efficacy. This effectively optimizes the operation of the cold chain logistic supply chain and provides a feasible technical solution for the development of the industry.