A cloud-IoT model for reconfigurable radio sensing

Abstract

In this paper we elaborate on the challenges that emerge when designing open IoT models and methods to enable passive 'radio vision' functions within a cloud Platform-as-a-Service (PaaS) environment. Radio vision allows to passively detect and track any moving/fixed object or people, 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 is based on data analytic tools, that combine multiple radio measurements from possibly heterogeneous IoT networks. The goal of the study is to define the baseline specifications that are necessary to integrate this new technology into a cloud-IoT architecture. Following emerging semantic interoperability concepts, we propose an expressive ontology model to represent the radio vision concept and allow for interoperability with other systems. For accelerated integration of radio vision functions the open Radio.Sense platform is designed as compliant with existing models (oneM2M based ontologies).