Models and methods for improving the efficiency of software component development processes for intelligent Internet of Things systems
DOI:
https://doi.org/10.34121/1028-9763-2026-3-60-71Keywords:
Internet of Things, artificial intelligence, model-driven development processes, machine learning, microservicesAbstract
The Internet of Things is evolving from isolated sensor solutions to distributed systems in which logic, data, and intelligent solutions move between devices, peripheral nodes, and the cloud. Modern IoT systems increasingly have the characteristics of intelligent cyber-physical environments in which data processing, decision-making, and control are distributed. Instead of monolithic applications, ecosystems of software components are formed. They interact via the network and run on devices with different resource capabilities — at the periphery and in the cloud. Such an environment is naturally described as the IoT–Edge–Cloud continuum. This increases the level of requirements for the efficiency of software component development, since integration, testing, deployment, and maintenance of such systems in heterogeneous environments become more difficult. The article systematizes models and methods for increasing the efficiency of developing components of intelligent IoT systems within the IoT-Edge-Cloud continuum. Modern architectural approaches are considered, in particular microservices and serverless solutions, as well as continuum principles of component placement. Particular attention is paid to requirements engineering, model-driven approaches, dataflow-oriented ML pipelines, IoT system testing, containerization, and data management tools in the continuum. An integrated performance improvement model is formulated that combines requirements, architecture, artifact automation, test strategies, deployment practices, and security mechanisms, including firmware auditing and federated learning for IDS. Key gaps and areas for further research related to test reproducibility, component placement in the continuum, Edge-AI lifecycle, and operational complexity of federated approaches are identified. Figs.: 2. Refs.: 22 titles.
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