Research on data transmission mechanism between digital distribution operations and intelligent detection equipment

This study examines the data transmission mechanism between digital distribution operations (cloud applications) and intelligent detection equipment, referred to as the cloud-detection integration platform as a service (CDE-iPaaS). The components of CDE-iPaaS include the sensor Internet of Things (IoT) layer, the edge layer, and the cloud layer. The sensing layer consists of wireless body sensor networks (WBSNs), which comprise various devices worn by individuals to enable remote behavioral and scientific monitoring. The edge layer is composed of sink nodes that aggregate sensor data from the sensor nodes in the sensing layer. These sink nodes in the edge layer then supply the aggregated sensor data to cloud applications hosted in the cloud layer. The data collection process represents data transmission across sensors, sink nodes, and cloud applications. To identify the optimal data transfer rate for all sensors and edge nodes, this research introduces a novel multi-objective Eagle perching optimization (MO-EPO) method for CDE-iPaaS. This method addresses the issue in terms of multiple objectives while maintaining specific constraints. For this research, a virtual environment with separate on-body devices was established to enable remote patient monitoring. The experimental results indicate that the proposed MO-EPO method is effective in reconciling the data request sequences generated by cloud applications with the Pareto-optimal data transmission speeds for sensor and sink nodes. The performance of MO-EPO was evaluated based on metrics such as hyper-volume, data yield, and energy consumption. The results confirm that the proposed MO-EPO method performs better than existing methods.