Abstract
The automatic Key Performance Indicators (KPIs) assessment for smart cities is challenging, since the input parameters needed for the KPIs calculations are highly dynamic and change with different frequencies. Moreover, they are provided by heterogeneous data sources (e.g., IoT infrastructures, Web Services, open repositories), with different access protocol. Open services are widely adopted in this area on top of open data, IoT, and cloud services. However, KPIs assessment frameworks based on smart city models are currently decoupled from open services. This limits the possibility of having runtime up-to-date data for KPIs assessment and synchronized reports. Thus, this paper presents a generic service-oriented middleware that connects open services and runtime models, applied to a model-based KPIs assessment framework for smart cities. It enables a continuous monitoring of the KPIs’ input parameters provided by open services, automating the data acquisition process and the continuous KPIs evaluation. Experiment shows how the evolved framework enables a continuous KPIs evaluation, by drastically decreasing (\(\sim \)88%) the latency compared to its baseline.
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