Assessment of Latency Reduction with Edge Sensing Architectures in Industrial Automation Systems
Keywords:
Edge Computing, Latency Reduction, Industrial Automation, Sensor Networks, Deterministic NetworkingAbstract
The rapid evolution of industrial automation systems under the paradigm of the Fourth Industrial Revolution necessitates highly deterministic, low-latency communication networks to support real-time control and monitoring. Traditional cloud-centric architectures, while offering vast computational resources, frequently introduce unacceptable communication delays and jitter, rendering them unsuitable for mission-critical industrial applications. This paper comprehensively investigates the efficacy of edge sensing architectures in mitigating these latency constraints. Through a rigorous systems experiment approach, we deploy and evaluate a localized edge computing framework integrated directly with industrial sensor networks. The study systematically compares the end-to-end latency, packet loss, and jitter characteristics of the proposed edge architecture against conventional cloud-based and hybrid fog computing models. By analyzing high-frequency data streams generated by industrial sensors, the research provides empirical evidence demonstrating that migrating data processing tasks to the network edge significantly reduces both average and peak latency. Furthermore, the localized architecture drastically improves system resilience against transient network congestion. The findings offer a robust empirical foundation for industrial engineers and network architects to design decentralized control systems, ultimately facilitating more responsive, reliable, and scalable automation environments.References
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