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15:24, 16 December 2025
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Russia Develops Smart Environmental Monitoring System

The new platform is designed to make environmental monitoring simpler, faster and more reliable.

Researchers in Belgorod have developed Russia’s first intelligent system for geo-environmental monitoring. The platform uses artificial intelligence and adaptive networks to collect environmental data with minimal information loss and high transmission speeds.

An Intelligent Routing Protocol

The system, created by scientists at the V. G. Shukhov Belgorod State Technological University, is delivered as a ready-to-use software simulator. Its core feature is an intelligent routing protocol that allows sensor networks to automatically adapt to changing conditions such as interference, overloaded communication channels or sensor failures, while optimizing energy use and minimizing data loss, the developers told TASS.

Unlike existing solutions that require deep technical expertise and lengthy configuration, the new software offers a user-friendly graphical interface with preset options. This makes it a practical tool for environmental scientists and geologists who do not need programming skills to work with the system.

No Complex Setup Required

“The solution stands out because of its specialization and completeness. It is a practical tool that allows professionals to get started immediately, without going through a complex setup phase,” said one of the project’s authors, Dmitry Surzhik, Doctor of Technical Sciences.

The introduction of such intelligent systems marks an important step in the digital transformation of environmental monitoring. The platform enables more efficient and reliable observation of soil, water and air conditions, which is critical for early detection of environmental threats and the sustainable development of regions.

Future development of the project will focus on expanding the module library for complex scenarios, improving artificial intelligence and deep-learning algorithms, and enhancing predictive capabilities. This will allow the system not only to assess current conditions, but also to forecast changes in monitoring networks more accurately and recommend optimal real-time responses.

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