智慧城市中基于物联网的知识提取和边缘设备可持续性

Dimitrios Sikeridis
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引用次数: 1

摘要

物联网(IoT)部署正在成为所有未来智慧城市(SC)环境的支柱。因此,通过与SC用户的移动设备及其无线接口的设备对设备交互,它们可以充当大规模的众包数据聚合器。在此前提下,我们的研究重点是开发概率和机器学习模型,以(a)实现从被动用户与无线物联网基础设施的交互中发现知识,以及(b)应用收集到的智能来提高智慧城市物联网网络的能源效率和弹性。在这篇扩展的摘要中,我们详细阐述了我们工作背后的动机,以及相关的挑战,同时指出了迄今为止开发的解决方案。
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IoT-enabled Knowledge Extraction and Edge Device Sustainability in Smart Cities
Internet of Things (IoT) deployments are becoming the backbone of all future Smart City (SC) environments. They can, therefore, act as massive crowd-sourced data aggregators, driven by device-to-device interactions with SC users' mobile devices and their wireless interfaces. Provided that, our research focuses on developing probabilistic and machine learning models to (a) enable knowledge discovery from passive user interactions with the wireless IoT infrastructure and (b) apply the collected intelligence to increase the energy-efficiency and resiliency of the Smart City's IoT network. In this extended abstract we elaborate on the motivation behind our work, and the related challenges, while pointing to the solutions developed so far.
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