The article considers the model and method of converged computing and storage to create SCADA systems based on wireless networks for the energy industry. Computing power of modern wireless sensor network nodes allow the transfer to them some operations sensor data mining and offload the dispatching data centre servers. This fog computing model is used for the aggregation of primary data, forecast trends controlled variables as well as to warn about abnormal and emergency situations on distributed SCADA systems objects. Large arrays of sensor data, integral indicators and heterogeneous information from other sources (e.g., weather stations, security and fire alarm systems, video surveillance systems, etc.) is more appropriate to process via GRID computing model. GRID computing model has three-tier architecture, which includes the main server at the first level, a cluster of servers at the second level, and a lot of GPU video card with support for Compute Unified Device Architecture at the third level. The model of cloud computing and cloud storage today is the basis for the accumulation of the results of data mining and knowledge discovery. Means of communication and remote access can solve the problem of intellectual processing and visualization of information with elements of augmented reality and geo-information technologies within the framework of mobile computing model. The implementation of these four computing models for the operation of components of SCADA system is the convergent approach to distributed sensor data processing, which is discussed in the article.

To respond to the needs of digital transformation, universities must continue to play their role as proving ground for educating the future generation and innovation. The article is devoted to overview, discussion, and investigation of application in higher education of two modern information technologies: big data and internet of things. The article identifies the role of analytics, based on big data, in improvement of education process and outlines the challenges, related with big data mining, storage, and security. Proposed statements are based on practical experience of the authors; architecture of program and methodological solution are the focus of the article. The article contributes to theory by the new approach to combination of big data and internet of things technologies in educational resources and, at the same time, includes implications for practice, presenting examples of the approach's realization and sharing the authors' experiences of such realization.