Abstract
With the rapid development of Information Technology, the existence of Cyber-Physical Systems (CPSs) has revealed, which are slowly emerging to dominate our world through their tight integration between the computational and physical components. Especially the physical components, consist of various devices, known as Internet of Things (IoT) devices, which are responsible for collecting and producing CPSs’ data, whereas simultaneously are able to sense, monitor and interpret the different occasions and environments that are used. However, these devices are typically characterized by a high degree of heterogeneity, emerging the need for programming applications to deal with each specific new device in order to use its data. To address this problem, in this manuscript a generic plug ‘n’ play approach is proposed for connecting and recognizing heterogeneous IoT devices of both known and unknown nature, and integrating them to finally gather their data, focusing mainly in the healthcare domain. This approach is based upon a 4-step mechanism, where in the first stage the mechanism discovers and connects all the available devices of both known and unknown nature, gathering various information of them. The latter is then used in the second, third, and fourth stage, so as to identify the API methods that are responsible for collecting devices’ data, and integrate them into a unified API, for finally gathering the data from all the both known and unknown devices. The proposed mechanism is evaluated through a specific use case, producing reliable results, thus being considered as a reference value of high quality and accuracy.









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Acknowledgements
Τhe research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) and the General Secretariat for Research and Technology (GSRT), under the HFRI PhD Fellowship grants (1792, and 2468).
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Mavrogiorgou, A., Kiourtis, A. & Kyriazis, D. A plug ‘n’ play approach for dynamic data acquisition from heterogeneous IoT medical devices of unknown nature. Evolving Systems 11, 269–289 (2020). https://doi.org/10.1007/s12530-019-09286-5
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DOI: https://doi.org/10.1007/s12530-019-09286-5