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<p>Real-time internet of things applications, such as healthcare monitoring and industrial automation, require immediate data processing, making traditional asynchronous middleware-like queues unsuitable. Efficiently orchestrating heterogeneous sensors—including continuous, event-driven, and query-driven types—is a key challenge due to varying priority levels and resource constraints. This paper proposes RT-IMO, a real-time multi-sensor orchestration strategy that integrates priority-based, load-balanced, and adaptive scheduling strategies to ensure low latency and efficient resource allocation. The proposed RT-IMO dynamically adjusts scheduling priorities to balance latency, fairness, and resource constraints in real time. Designed for high-volume, distributed IoT environments, RT-IMO is suitable for deployment on edge-cloud platforms that require low-latency decisions and scalable scheduling under high data ingestion rates—making it well-aligned with the goals of real-time and high-performance systems. Experimental results demonstrate that RT-IMO improves responsiveness, fairness, and system efficiency compared to existing approaches. Future research will explore machine learning-based adaptive scheduling and its extension to heterogeneous edge computing environments. The results show that RT-IMO ensures low latency, efficient resource use, and fairness, prioritizing critical data while selectively dropping lower-priority tasks. It adapts dynamically to workload variations, outperforming static approaches in responsiveness and stability under high load.</p>

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Toward real-time IoT multi-sensor data orchestration on wireless sensor networks

  • 期刊论文

  • 发表日期：

    2025年8月26日

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  • Pedro Henrique Sachete Garcia,
  • Marcelo Caggiani Luizelli,
  • Fábio Diniz Rossi

摘要

Real-time internet of things applications, such as healthcare monitoring and industrial automation, require immediate data processing, making traditional asynchronous middleware-like queues unsuitable. Efficiently orchestrating heterogeneous sensors—including continuous, event-driven, and query-driven types—is a key challenge due to varying priority levels and resource constraints. This paper proposes RT-IMO, a real-time multi-sensor orchestration strategy that integrates priority-based, load-balanced, and adaptive scheduling strategies to ensure low latency and efficient resource allocation. The proposed RT-IMO dynamically adjusts scheduling priorities to balance latency, fairness, and resource constraints in real time. Designed for high-volume, distributed IoT environments, RT-IMO is suitable for deployment on edge-cloud platforms that require low-latency decisions and scalable scheduling under high data ingestion rates—making it well-aligned with the goals of real-time and high-performance systems. Experimental results demonstrate that RT-IMO improves responsiveness, fairness, and system efficiency compared to existing approaches. Future research will explore machine learning-based adaptive scheduling and its extension to heterogeneous edge computing environments. The results show that RT-IMO ensures low latency, efficient resource use, and fairness, prioritizing critical data while selectively dropping lower-priority tasks. It adapts dynamically to workload variations, outperforming static approaches in responsiveness and stability under high load.