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<p>Industrial Internet of Things (IIoT) systems face growing demands for low-latency, energy-efficient, and trustworthy operation under heterogeneous devices, mobility, and renewable energy variability. Existing fog–cloud approaches typically optimize isolated objectives and lack integrated mechanisms for sustainability and verifiable coordination. This paper presents the Energy-Aware Hierarchical Green Fog (EAHGF) framework, which introduces a unified reinforcement learning (RL) orchestration layer that explicitly incorporates residual energy, renewable energy availability, spatial proximity (via BLE), and task deadlines into hierarchical fog–cloud decision-making. A lightweight Proof-of-Stake blockchain provides immutable auditability of allocations with minimal overhead. A stochastic multi-layer queuing model captures system dynamics, while RL-based scheduling and proximity-aware offloading jointly optimize energy and latency. Extensive OMNeT++/INET simulations with up to 3,000 heterogeneous IIoT devices (Poisson arrivals λ = 0.5–2 tasks/s, random waypoint mobility 1–5&#xa0;m/s, 70% renewable offset on fog nodes) demonstrate that EAHGF achieves a workload acceptance rate of ~ 92%, reduces energy consumption by approximately 28%, and improves latency by ~ 22% compared to baseline fog frameworks and FogNetSim++. The integrated PoS blockchain maintains ~ 100 ms confirmation latency while providing blockchain-assisted accountability, traceability, and trust in resource allocation decisions. EAHGF thus offers a scalable, sustainable, and trustworthy foundation for next-generation Green IIoT deployments, preserving ~ 65% residual energy versus ~ 45% in conventional systems.</p>

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Sustainable and trust-aware multi-tier fog–cloud infrastructure for energy-optimal IIoT operations: adaptive resource management and blockchain-assured security

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月6日

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  • Mahsa Nazemi Harandi,
  • Afshin Yaghoobi

摘要

Industrial Internet of Things (IIoT) systems face growing demands for low-latency, energy-efficient, and trustworthy operation under heterogeneous devices, mobility, and renewable energy variability. Existing fog–cloud approaches typically optimize isolated objectives and lack integrated mechanisms for sustainability and verifiable coordination. This paper presents the Energy-Aware Hierarchical Green Fog (EAHGF) framework, which introduces a unified reinforcement learning (RL) orchestration layer that explicitly incorporates residual energy, renewable energy availability, spatial proximity (via BLE), and task deadlines into hierarchical fog–cloud decision-making. A lightweight Proof-of-Stake blockchain provides immutable auditability of allocations with minimal overhead. A stochastic multi-layer queuing model captures system dynamics, while RL-based scheduling and proximity-aware offloading jointly optimize energy and latency. Extensive OMNeT++/INET simulations with up to 3,000 heterogeneous IIoT devices (Poisson arrivals λ = 0.5–2 tasks/s, random waypoint mobility 1–5 m/s, 70% renewable offset on fog nodes) demonstrate that EAHGF achieves a workload acceptance rate of ~ 92%, reduces energy consumption by approximately 28%, and improves latency by ~ 22% compared to baseline fog frameworks and FogNetSim++. The integrated PoS blockchain maintains ~ 100 ms confirmation latency while providing blockchain-assisted accountability, traceability, and trust in resource allocation decisions. EAHGF thus offers a scalable, sustainable, and trustworthy foundation for next-generation Green IIoT deployments, preserving ~ 65% residual energy versus ~ 45% in conventional systems.