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首页> 外文期刊>IEEE Communications Magazine >Key design of driving industry 4.0: joint energy-efficient deployment and scheduling in group-based industrial wireless sensor networks
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Key design of driving industry 4.0: joint energy-efficient deployment and scheduling in group-based industrial wireless sensor networks

机译：推动工业4.0的关键设计：基于组的工业无线传感器网络中的联合节能部署和调度

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摘要

In the Industry 4.0 framework based on IoT and smart manufacturing, it is essential to support factory automation and flexibility in harsh or dynamic industrial environments. State-of-the-art technology suggests building a controlled workspace using large-scale deployment of wireless sensors. To overcome the technological challenges in scalability and heterogeneity for large-scale industrial deployment, group-based industrial wireless sensor networks (GIWSNs) are suggested, in which wireless sensors are divided into multiple groups for multiple monitoring tasks, and each group of sensors is deployed densely within a subarea in a large plant or along a long production/assembly line, while connectivity between groups is required. As wireless sensors are equipped with batteries with limited power, it has been challenging to plan sleep schedules of sensors, which are influenced significantly by deployment of such a large-scale GIWSN. However, most previous works on wireless sensor networks independently investigated deployment and sleep scheduling problems, both of which have been shown to be NP-hard. Therefore, this work jointly considers deployment and sleep scheduling of sensors in a GIWSN along a production line. Via the theory of symmetries, we alleviate the computational concerns from multiple groups to one group and another medium-size group. Then we propose a hybrid harmony search and genetic algorithm, which incorporates deployment and sleep schedules to reduce energy consumption. Simulations verify this joint methodology to effectively achieve energy efficiency.
机译：在基于物联网和智能制造的工业4.0框架中，至关重要的是在恶劣或动态的工业环境中支持工厂自动化和灵活性。最新技术建议使用无线传感器的大规模部署来建立受控的工作区。为了克服大规模工业部署中可伸缩性和异构性方面的技术挑战，提出了基于组的工业无线传感器网络（GIWSN），其中将无线传感器分为多个组以执行多个监视任务，并部署每组传感器在大型工厂的子区域内密集分布或沿着长的生产/装配线密集分布，而组之间则需要连通性。由于无线传感器配备了功率有限的电池，因此计划传感器的睡眠时间表一直是一项挑战，而睡眠时间表受此类大规模GIWSN部署的影响很大。但是，以前在无线传感器网络上进行的大多数工作都是独立研究部署和睡眠调度问题，这两个问题均已证明对NP不利。因此，这项工作共同考虑了沿生产线在GIWSN中传感器的部署和睡眠调度。通过对称性理论，我们减轻了从多组到一组和另一组中型组的计算关注。然后，我们提出了一种混合和谐搜索和遗传算法，该算法融合了部署和睡眠计划以减少能耗。仿真验证了这种联合方法可有效实现能源效率。

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