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<p>Integration of the Industrial Internet of Things (IIoT) into the construction sector is transforming operational efficiency through real-time monitoring and data-driven management. This paper presents a Service Function Tree (SFT) mapping technique that optimizes microservice deployment within fog computing environments for IIoT applications. We employ a mixed-integer linear programming (MILP) formulation to optimize microservice placement by focusing on reducing latency and improving the efficiency of resource usage. Our methodology includes preprocessing steps that ensure feasible mappings between SFT and physical network components to enhance the efficiency of the optimization phase. We demonstrate the model’s applicability through a “concrete pouring” scenario typical of a large construction site, emphasizing the need for precise coordination and immediate response to safety and operational demands within defined Regions of Interest (RoIs). Simulations validate the effectiveness of our approach, demonstrating significant improvements in latency and resource utilization, advancing research on efficient IIoT deployment.</p>

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Service function tree mapping of microservices on resource-constrained fog networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月20日

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  • Babar Shahzaad,
  • Alistair Barros,
  • Colin Fidge

摘要

Integration of the Industrial Internet of Things (IIoT) into the construction sector is transforming operational efficiency through real-time monitoring and data-driven management. This paper presents a Service Function Tree (SFT) mapping technique that optimizes microservice deployment within fog computing environments for IIoT applications. We employ a mixed-integer linear programming (MILP) formulation to optimize microservice placement by focusing on reducing latency and improving the efficiency of resource usage. Our methodology includes preprocessing steps that ensure feasible mappings between SFT and physical network components to enhance the efficiency of the optimization phase. We demonstrate the model’s applicability through a “concrete pouring” scenario typical of a large construction site, emphasizing the need for precise coordination and immediate response to safety and operational demands within defined Regions of Interest (RoIs). Simulations validate the effectiveness of our approach, demonstrating significant improvements in latency and resource utilization, advancing research on efficient IIoT deployment.