A Comparative Analysis of Latency and Accuracy in PLC-Driven, IoT-Enabled Robotic Sorting Systems

Abstract

Adding Internet of Things (IoT) telemetry to localized, automated robotic sorting systems makes it much easier to keep an eye on things from afar and plan maintenance ahead of time. Nonetheless, the implementation of cloud-based data management engenders apprehensions about network latency and its possible effects on the precision of physical sorting. This study performs a secondary data analysis of current robotic sorting prototypes to assess the operational trade-offs between localized Programmable Logic Controller (PLC) systems and Internet of Things (IoT)-enabled architectures. The results indicate that while MQTT-based IoT integration introduces an average telemetry latency of 120ms, the overarching sorting accuracy remains robust at 94.5%. This represents a statistically significant, though operationally acceptable, variance from the 96.2% baseline accuracy of strictly localized systems. This study presents a validated theoretical framework for enhancing control logic and sensor integration in multi-axis robotic sorting environments.