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Although IoT service and service composition provide effective means to develop IoT applications, the dynamic and time-varying correlation among massive sensors rises up new challenges to the traditional model-based approaches, and the extra uncertainty and complexity of service composition become apparent. This paper proposes a data-driven service composition method based on our previous proactive data service model. We utilize real-time correlation analysis of sensor data to refine model-based service composition at runtime. The correlation among sensor data is usually asynchronous. In this paper, we adopt and improve a Dynamic Time Warping (DTW) algorithm to obtain one-way lag-correlation, and realize dynamic sensor data correlation through refining existing service composition. Based on the real sensor data set in a coal-fired power plant, a series of experiments demonstrate the effectiveness of our service composition method.

The current Sensor Networks are generally domain-specific and task-oriented, tailored for particular applications with little possibility of sharing and reusing sensor data for different applications. The servitization of stream sensor data is an effective solution for sharing and reusing sensor data resources. Considering the limitations of existing methods on processing large-scale stream data and concurrent requests, this paper proposes a lightweight model for stream sensor data service, which processes sensor stream data with service modeling operations, and distributes data based on Pub/Sub mechanism. This paper fulfills the encapsulation by utilizing event-driven mechanism for stream data service processing, using SparkStreaming framework to process sensor events, and improving traditional matching-tree algorithm to distribute stream data efficiently. Finally, we evaluate our approach through experiments, and the stream data service can handle multiple requests and deliver data to corresponding applications in milliseconds level.

Data service mashup provides a development fashion that integrates heterogeneous data from multiple data sources into a single Web application. This paper focuses on the problem of recommending useful suggestions for developing data service mashups based on the association relationship of data services. Firstly the data service association relationship is analyzed from three angles: the data dependence, inheritance and the potential association between data services. Based on the analysis, a measure of the data service association relationship called connectivity is proposed to assess the relationship of any two data services. Then a recommendation method is proposed to suggest the next useful data services based on the connectivity. The experimental evaluation demonstrates the utility of our method.