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Structural and behavioral pattern-based soft development for IoT devices control

The task of creating a software system designed to control devices Internet of Things (IoT) cyberphysical systems (CPS) of smart buildings, industries or urban environment is considered. An architectural approach to software design based on using typical solutions – structural and behavioral patterns - is proposed. Adaptation of templates to the subject area of CPS leads to the emergence of new methods of rapid creation of software services, including open source, with high stability, interoperability and modifiability. The software developed with the use of patterns can be used as a prototype of control system of «smart» offi - room with integrated subsystems: climate control, fi safety and leak control, lighting control, multimedia control, access control, interaction with clients, automation of rental processes, etc. The implementation of interaction between the server and the IoT controller through non-standard formats determines the application of the «Interpreter» pattern. Data transfer between components with packet caching and access control is proposed to be implemented using the «Proxy» pattern, and «Observer» is proposed to be used as a stable means of scripting management. Interpretation of design templates of system architectures for IoT devices management in CPS buildings suggested in the study allows to create new or modify existing software services quickly. Thus, for example, the proposed architecture can be used to create modules for data collection and transmission, as well as equipment management by scenario in a large number of users and personalized configurations. The research is carried out within the framework of the priority science development direction of the Perm branch of the National Research University Higher School of Economics «Research of control methods in cyber-physical systems». Read more...

Technology for the soft implementing a digital twin into the IoT HVAC control loop

The development of application software for cyber-physical systems of buildings involves the widespread use of Internet of Things (IoT) integration platforms. In practice, the flexible functionality of IoT platforms often leads to additional costs for software enhancement of existing and connection of new units, in particular digital twins. The paper proposes a technological solution for the implementation of a digital twin of the ventilation process in the IoT control loop of heating, ventilation and air conditioning (HVAC) systems for buildings and industrial facilities. The implementation and execution of the digital twin in the form of a dynamic simulation model in the object-oriented modelling language Modelica in the OpenModelica environment is considered. The IoT platform InfluxData, based on the TICK stack, is considered as an example of an integration environment. It is a horizontally-oriented IoT platform that contains the mechanism for collecting data from devices and the InfluxDB time-series database for storing metrics. To integrate simulation models on Modelica with InfluxDB, an OMPython server is proposed. In this case, the integration scripts are executed in the Python language, which as a result extends the traditional capabilities of the IoT platform significantly to the level of a digitally twinned control system. This HVAC control involves adapting control loops by taking into account the dynamics of the air distribution process over the ventilation network, evaluating and compensating for process inertia. The publication was prepared within the framework of the Academic Fund Program at the HSE University in 2020–2021 (grant № 21-04-039). Read more...

Predictive models integration with an environmental monitoring IoT platform

The research focuses on the development of applied software systems for automated environmental monitoring. The task of developing and integrating applied software, in particular calculation and analytical models based on machine learning (ML) methods, with an IoT platform of digital eco-monitoring for industrial enterprises is considered. Such a platform is used to create software and hardware systems of CEMS – Continuous Emissions Monitoring System class, designed for continuous monitoring of pollutant emissions into the atmospheric air at production facilities. Use of ML tools integrated with the platform allows to expand significantly the functionality of the existing CEMS, in particular to quickly build new SaaS services for forecasting the dynamics of pollution distribution. Given the high requirements for industrial systems, there is a need to create a specialized software product – an analytical server that implements the management of connected predictive analytical ML models with the required level of service quality, including automatic initialization of new analytical scripts as classes, isolation of individual components, automatic recovery after failures, data security and safety. The paper proposes a scheme of functional and algorithmic interaction between the IoT platform of digital eco- monitoring and the analytical server. The proposed implementation of the analytical server has a hierarchical structure, at the top of which is an application capable of accepting high-level REST requests to initialize calculations in real time. This approach minimizes the impact of one analytical script (class) on another, as well as extending the functionality of the platform in "hot" mode, that is, without stopping or reloading. Results demonstrating automatic initialization and connection of basic ML models for predicting pollutant concentrations are presented. Read more...