Evaluation of Scheduling Approaches for Efficient Data Transmission in Wireless HART Networks

The rapid evolution of industrial automation systems has driven the need for highly reliable and efficient communication networks. Wireless HART, a prominent wireless protocol tailored for industrial applications, is widely adopted due to its robust performance in harsh environments. However, optimizing network performance, particularly in terms of latency and packet loss, remains a critical challenge. This paper presents a comparative analysis of various scheduling techniques like Time-Slotted Channel Hopping (TSCH), Low-Power Wireless Bus (LWB), Machine Learning (ML) based approaches, Round-Robin Scheduling (RRS), and Frequency Division Multiple Access (FDMA) within the Wireless HART network architecture. By simulating these techniques in a typical industrial wireless network scenario, the study evaluates their impact on latency and packet loss metrics. The results indicate that ML-based approaches offer significant improvements in latency reduction and reliability, outperforming traditional techniques such as TSCH and LWB. The findings provide valuable insights for industrial practitioners aiming to enhance the performance of Wireless HART networks through the adoption of advanced scheduling strategies.