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Graph-based metadata modeling in indoor positioning systems
Simulation Modelling Practice and Theory ( IF 3.5 ) Pub Date : 2020-06-23 , DOI: 10.1016/j.simpat.2020.102140
Saša Pešić , Miloš Radovanović , Mirjana Ivanović , Milenko Tošić , Ognjen Iković , Dragan Bošković

Modeling and persistence of different data structures in indoor positioning systems is a requirement for providing a large number of specialized location-based services. Collection and diversification of indoor positioning systems’ metadata are important to understand the context of the system’s operation to create a positive feedback improvement loop. While metadata from a residential building’s indoor positioning system operational context benefits the system (i.e. through occupancy patterns extraction that drive resource utilization strategies), it can also benefit the tenants’ well-being or drive other decisions through observing social dynamics. Observation of social relationships in residential buildings is rarely addressed due to highly stochastic movement patterns of tenants. In this article we have proposed a set of graph-based approaches for modeling social behavior data: modeling of tenants’ movement paths and detecting the existence of patterns, modeling of tenants’ social relationships (frequency, quality) as well as detecting social communities and tracking their evolution. We have tested our approaches on a real-world private residential building resulting in multidisciplinary implications and applications connecting the fields of IoT and indoor positioning to behavioral sciences. Finally, we provide public, high-quality positioning and occupancy datasets and open-source code for reproducing experiments on the observed residential building.



中文翻译:

室内定位系统中基于图的元数据建模

室内定位系统中不同数据结构的建模和持久性是提供大量专门的基于位置的服务的要求。室内定位系统元数据的收集和多样化对于理解系统操作的上下文以创建积极的反馈改进回路非常重要。虽然来自住宅建筑物的室内定位系统操作环境的元数据使系统受益(例如,通过驱动资源利用策略的占用模式提取),但它也可以通过观察社会动态来使租户的福祉得到改善或做出其他决定。由于租户的高度随机移动模式,很少能观察到住宅建筑中的社会关系。在本文中,我们提出了一套基于图的方法来对社会行为数据进行建模:对承租人的移动路径进行建模并检测模式的存在,对承租人的社会关系(频率,质量)进行建模以及检测社交社区和社区。追踪他们的进化。我们已经在真实的私人住宅建筑上测试了我们的方法,从而产生了多学科的含义,并将应用程序与IoT和室内定位领域的行为科学联系起来。最后,我们提供了公开的高质量定位和占用数据集以及开放源代码,用于在观察到的住宅建筑物上重现实验。质量),以及检测社交社区并跟踪其发展。我们已经在真实的私人住宅建筑上测试了我们的方法,从而产生了多学科的含义,并将应用程序与IoT和室内定位领域的行为科学联系起来。最后,我们提供了公开的高质量定位和占用数据集以及开放源代码,用于在观察到的住宅建筑物上重现实验。质量),以及检测社交社区并跟踪其发展。我们已经在真实的私人住宅建筑上测试了我们的方法,从而产生了多学科的含义,并将应用程序与IoT和室内定位领域的行为科学联系起来。最后,我们提供了公开的高质量定位和占用数据集以及开放源代码,用于在观察到的住宅建筑物上重现实验。

更新日期:2020-06-23
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