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Stream data from devices and sensors is considered a typical kind of big data. Though being promising, they have a good prospect only when we can reasonably correlate and effectively use them. Herein, services come back to the spotlight. The paper reports some of the authors' efforts in promoting service-based fusion and correlation of such stream data in a real setting – monitoring and optimized coordination of individual devices in a power plant. This paper advocates a decentralized and service-based approach to dynamically correlating the sensor data and proactively generating higher-level events between sensors and applications. A novel service model for transforming and correlating massive stream data is proposed. This service model shows potential in realizing various middle-way programmable nodes to form larger-granularity and software-defined ‘sensors' in an IoT context.

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With the prevalent development and use of predictive maintenance models for Internet‐of‐Things scenarios, the deep learning technology is gaining momentum. Feature extraction helps to increase efficiency in training the deep‐learning–based predictive maintenance model. However, there are common situations of time‐lagged correlations among industrial sensor data, resulting in reduction the effect of feature extraction. In this paper, we propose a feature extraction method for multisensors data with time‐lagged correlation. A curve‐registration method of correlation maximization algorithm is used to solve the problem of time‐lagged correlation for multi sensors. Then we apply a recurrent neural network, namely, long short‐term memory to develop a lightweight predictive maintenance model with the help of proposed feature extraction method. The effectiveness of the proposed feature extraction approach is demonstrated by examining real cases in a power plant. The experimental results indicate that our method can (1) effectively improve the accuracy of prediction and (2) improve the performance of the prediction model.