Edge Computing–Enabled Real-Time Data Analytics Frameworks for High-Performance Industrial Internet of Things (IIoT) Applications

Abstract

The rapid growth of the Industrial Internet of Things has transformed modern manufacturing and industrial operations by enabling continuous monitoring, automation, and intelligent decision-making. However, traditional cloud-centric architectures struggle to meet the stringent latency, reliability, and bandwidth requirements of industrial environments. Edge computing has emerged as a promising paradigm that brings computation and data analytics closer to data sources, enabling real-time processing and improved system responsiveness. This paper presents an edge computing–enabled real-time data analytics framework for high-performance Industrial Internet of Things applications. The proposed framework integrates distributed edge nodes, intelligent data filtering, and machine learning–based analytics to support time-critical industrial processes. Architectural design considerations, data flow mechanisms, and computational resource management strategies are examined in detail. Performance evaluation demonstrates significant reductions in latency, network congestion, and energy consumption compared to cloud-only approaches. Security, scalability, and fault tolerance challenges are also discussed. The study highlights the potential of edge computing to enhance operational efficiency, reliability, and autonomy in industrial systems, positioning it as a key enabler of next-generation IIoT infrastructures.