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Optimizing Sensor Network Coverage and Regional Connectivity in Industrial IoT Systems

机译:优化工业物联网系统中的传感器网络覆盖范围和区域连通性

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摘要

Internet of things (IoT) technologies have been widely used in industrial systems to control the manufacturing environment and monitor production lines. An industrial IoT system can perform data collection and processing and provide services to production decisions. However, the challenge remains for the IoT system to ensure the quality and quantity of data collected from sensor networks. To address the issue, an independent regional connectivity model is presented in the context of sensor networks to guarantee global connectivity with satisfied quality of data service. We also investigate the optimization of sensing coverage and regional connectivity in an industrial IoT system in both deterministic and random deployment. First, a novel optimal network that achieves full sensing coverage and guarantees regional connectivity is presented for deterministic deployment. The optimal pattern is derived, and the advantage of the proposed model is analyzed. Second, based on the assumption that the given sensors are deployed as a Poisson point process, theoretical analysis is presented to determine the minimum number of sensors used for random deployment to achieve certain coverage and connectivity degrees. Numerical results show that our proposed models are efficient for the application of sensor networks in industrial IoT systems.
机译:物联网(IoT)技术已广泛用于工业系统中,以控制制造环境并监视生产线。工业物联网系统可以执行数据收集和处理并为生产决策提供服务。但是,物联网系统要确保从传感器网络收集的数据的质量和数量仍然面临挑战。为了解决该问题,在传感器网络的上下文中提出了一个独立的区域连通性模型,以确保具有令人满意的数据服务质量的全局连通性。我们还研究了确定性和随机部署中工业物联网系统中感测范围和区域连接性的优化。首先,针对确定性部署,提出了一种新颖的最佳网络,该网络可以实现完整的感测范围并确保区域连通性。推导了最佳模式,并分析了所提模型的优势。其次,基于将给定传感器部署为泊松点过程的假设,提出了理论分析,以确定用于随机部署以实现某些覆盖范围和连接度的最小传感器数量。数值结果表明,我们提出的模型对于工业物联网系统中传感器网络的应用是有效的。

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