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<p>Intelligent IoT networks create massive, diversified data streams that need real-time and massive analytical processing. Traditional cloud-centric architectures feature latency, bandwidth congestion, and limited flexibility for batch and stream data. The AI-HyECC framework for Adaptive Batch–Stream Data Analytics (ABSDA) in intelligent IoT tackles these limits. IoT sensing, edge intelligence, AI-orchestrated adaptive middleware, and cloud cognitive analytics comprise the four-layer AI-HyECC architecture. The Neural Stream Optimizer (NSO) performs real-time edge-level inference, while the reinforcement learning-powered Adaptive Task Orchestration Engine (ATOE) dynamically distributes tasks between edge and cloud based on data type, latency, and network AI-HyECC decreases latency by 38%, resource usage by 32%, and inference speed by 40% compared to cloud-only systems in experimental validation. For many IoT applications, the framework may deliver scalable, context-aware, energy-efficient analytics. Intelligent, adaptable, and resilient AI-HyECC blends batch and stream processing for next-generation IoT ecosystems.</p>

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AI-enabled hybrid edge–cloud computing framework for adaptive batch–stream data analytics in intelligent IoT networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月14日

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  • Weixian Wang,
  • Xue Chang,
  • Yao Chen

摘要

Intelligent IoT networks create massive, diversified data streams that need real-time and massive analytical processing. Traditional cloud-centric architectures feature latency, bandwidth congestion, and limited flexibility for batch and stream data. The AI-HyECC framework for Adaptive Batch–Stream Data Analytics (ABSDA) in intelligent IoT tackles these limits. IoT sensing, edge intelligence, AI-orchestrated adaptive middleware, and cloud cognitive analytics comprise the four-layer AI-HyECC architecture. The Neural Stream Optimizer (NSO) performs real-time edge-level inference, while the reinforcement learning-powered Adaptive Task Orchestration Engine (ATOE) dynamically distributes tasks between edge and cloud based on data type, latency, and network AI-HyECC decreases latency by 38%, resource usage by 32%, and inference speed by 40% compared to cloud-only systems in experimental validation. For many IoT applications, the framework may deliver scalable, context-aware, energy-efficient analytics. Intelligent, adaptable, and resilient AI-HyECC blends batch and stream processing for next-generation IoT ecosystems.