Edge Computing for Real-Time Decision Making in Industrial Automation

DOI: https://doi.org/10.63345/ijrmeet.org.v10.i2.2

Prof. (Dr) Punit Goel

Maharaja Agrasen Himalayan Garhwal University

Uttarakhand, India

orcid- https://orcid.org/0000-0002-3757-3123

drkumarpunitgoel@gmail.com

Abstract

Edge computing has emerged as a pivotal paradigm for supporting real-time decision making in industrial automation by relocating computational resources closer to field devices, thereby reducing latency, improving reliability, and enhancing Quality of Service (QoS). This manuscript investigates the architectural design, implementation methodologies, and performance evaluation of edge-enabled industrial automation systems as of 2022. We present a comprehensive experimental study comparing cloud-only, edge-only, and hybrid cloud–edge deployments, focusing on key performance indicators such as round‑trip latency, throughput, and packet loss. A one-way ANOVA statistical analysis confirms that edge computing architectures yield statistically significant reductions in latency (F=18.72, p<0.001) compared to cloud-centric approaches. Our methodology details sensor-to-actuator data flows using OPC UA PubSub over Time‑Sensitive Networking (TSN) and MQTT protocols on industrial gateways. Results demonstrate that edge nodes can achieve mean latencies below 10 ms, meeting strict real-time requirements for closed‑loop control, while also enabling localized analytics and fault detection. We conclude by discussing practical considerations for industrial deployment, including resource provisioning, security hardening, and integration with existing PLC infrastructures. This work underscores the critical role of edge computing in the next generation of industrial automation and provides guidance for practitioners seeking to implement real-time decision‑making capabilities in manufacturing and process control environments.

Keywords

Edge computing; Industrial automation; Real-time decision making; Latency; Quality of Service

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