Enhancing Virtual Sensors to deal with Missing Values and Low Sampling Rates.


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Nowadays there is an increasing interest from the edge computing and IoT community for virtual sensors due to their advantages of low cost, robustness, easy installation and multi-purpose use. Virtual sensors are software components capable of replacing physical sensors providing aggregations and higher representations of physical measurements. Since virtual sensors rely on taking input and process measurements from external sources of data, they bear their limitations. To this end, in this paper, we tackle the challenges of missing values and low sampling rate. Specifically, our research goal is to design a virtual sensor that operates smoothly even if it misses some input values. Additionally, even if the sampling rate of the external input is low, the virtual sensor will be capable of providing output values in a higher rate. In order to achieve these functionalities we examine and tailor different lightweight deep learning models appropriate for an edge computing setting. For the experimental evaluation we also developed an IoT platform and run a smart home use case with humidity and temperature sensors. Comparing the evaluation outcomes of our methodology with baseline missing values techniques and multi-step approaches, our proposed methodology is proved to be promising and accurate.
Virtual Sensors,Machine learning,Multi-step Output,Missing Values,Sampling Rate
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