A cloud-IoT platform for passive radio sensing: challenges and application case studies
Sanaz Kianoush, Muneeba Raja, Stefano Savazzi, Stephan Sigg
备注：
This work has been partially carried out in the framework agreement CNR-Regione Lombardia CYBER SORT project,DIT.AD008.052. This paper has been published in IEEE Internet Of Things Journal
We propose a platform for the integration of passive radio sensing and vision technologies into a cloud-IoT framework that performs real-time channel quality information (CQI) time series processing and analytics. Radio sensing and vision technologies allow to passively detect and track objects or persons by using radio waves as probe signals that encode a 2D/3D view of the environment they propagate through. View reconstruction from the received radio signals, or CQI, is based on real-time data processing tools, that combine multiple radio measurements from possibly heterogeneous IoT networks. The proposed platform is designed to efficiently store and analyze CQI time series of different types and provides formal semantics for CQI data manipulation (ontology models). Post-processed data can be then accessible to third parties via JSON-REST calls. Finally, the proposed system supports the reconfiguration of CQI data collection based on the respective application. The performance of the proposed tools are evaluated through two experimental case studies that focus on assisted living applications in a smart-space environment and on driver behavior recognition for in-car control services. Both studies adopt and compare different CQI manipulation models and radio devices as supported by current and future (5G) standards.
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Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Nokia Bell Labs Stuttgart（诺基亚贝尔实验室斯图加特）；Hensoldt Sensors GmbH（亨索尔特传感器有限公司）；Technical University of Munich（慕尼黑工业大学）
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Central South University（中南大学）；Khalifa University（哈利法大学）；Technology Innovation Institute（技术创新研究院）；CRAN（南锡自动控制研究中心）；CNRS（法国国家科学研究中心）；Michigan Technological University（密歇根理工大学）
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