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<p>The rapid expansion of Saudi Arabia’s industrial has created a critical demand for reliable, delay-aware communication frameworks to ensure occupational safety, real-time emergency coordination, and data-driven decision support. Traditional cellular systems often collapse under congestion, interference, or limited spectrum availability, especially in dense ndoes zones. This paper introduces a mathematically grounded Industrial Internet of Things (IIoT) model that integrates LoRa-based mesh connectivity, Voronoi-driven spatial coverage, and a Neural Route Mapping Protocol (NRMP) designed to minimize latency and interference while optimizing throughput and network lifetime. The proposed model unifies analytical design by combining (i) signal-to-interference-plus-noise ratio (SINR) constraints, (ii) queueing-based access delay modeling using M/M/1 and PCF/DCF formulations, and (iii) multi-objective optimization for energy-aware routing. Closed-form delay bounds are derived under interference and traffic load constraints, forming a theoretical foundation for delay-tolerant yet safety-critical IIoT systems. Simulation-based validation demonstrates that the NRMP model can significantly outperform standard routing schemes such as Dynamic Source Routing (DSR) in terms of throughput, energy efficiency, and latency [<CitationRef AdditionalCitationIDS="CR2 CR3" CitationID="CR1">1</CitationRef>–<CitationRef CitationID="CR4">4</CitationRef>].</p>

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Wireless IIoT network modeling, Neural Route Mapping, and delay-aware analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月7日

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  • Abdulfattah Noorwali

摘要

The rapid expansion of Saudi Arabia’s industrial has created a critical demand for reliable, delay-aware communication frameworks to ensure occupational safety, real-time emergency coordination, and data-driven decision support. Traditional cellular systems often collapse under congestion, interference, or limited spectrum availability, especially in dense ndoes zones. This paper introduces a mathematically grounded Industrial Internet of Things (IIoT) model that integrates LoRa-based mesh connectivity, Voronoi-driven spatial coverage, and a Neural Route Mapping Protocol (NRMP) designed to minimize latency and interference while optimizing throughput and network lifetime. The proposed model unifies analytical design by combining (i) signal-to-interference-plus-noise ratio (SINR) constraints, (ii) queueing-based access delay modeling using M/M/1 and PCF/DCF formulations, and (iii) multi-objective optimization for energy-aware routing. Closed-form delay bounds are derived under interference and traffic load constraints, forming a theoretical foundation for delay-tolerant yet safety-critical IIoT systems. Simulation-based validation demonstrates that the NRMP model can significantly outperform standard routing schemes such as Dynamic Source Routing (DSR) in terms of throughput, energy efficiency, and latency [1–4].