Concept of a Multilevel Network Infrastructure for Monitoring Agricultural Facilities Based on Wireless Sensor Networks

Introduction. In the context of digitalization of the agricultural sector, precision farming becomes a key driver of sustainability: wireless sensor networks (WSN) provide continuous monitoring of edaphoclimatic parameters and plant health, supporting yield forecasting and resource optimization while reducing operational risks. Despite significant progress in research on energy efficiency, routing, and topologies of WSN, the issue of their systemic reliability in real agricultural scenarios has been addressed only fragmentarily. Existing theoretical approaches rely on graph theory, Markov and quasideterministic models to assess connectivity and fault tolerance but do not sufficiently account for battery degradation, radio channel variability, and external factors (microclimate, interference), as well as their combined effects. The objective of this article is to develop a methodological approach to enhance the reliability of WSN for monitoring agricultural objects through a multilevel model that integrates network parameters, hardware properties, and external actions.

Materials and Methods. To develop the model, methods of system analysis were used, including analysis and synthesis of previously known models and algorithms for controlling the WSN for various levels of network interaction. At the first stage, analytical models of each level were examined: operating conditions of radio devices; physical channels with interference and hardware distortions; energy losses of nodes in channels with variable environmental characteristics; linear WSN with heterogeneous radio communication segments and clustering of WSN. At the second stage, an analysis of WSN control algorithms was conducted: selection of transmission modes with minimal signal distortion; optimization of signal structure with minimal Bit Error Rate (BER); control of data packet length and transmitter power; balancing of energy losses in relay nodes, as well as routing with minimal time and energy losses. At the third stage, the synthesis of the obtained results was performed, presenting a hierarchical monitoring infrastructure for the agricultural object that considered all levels of WSN interaction, parameters of sensor nodes, and the external actions.

Results. A methodological multilevel approach to increasing the reliability of WSN for monitoring agricultural facilities has been proposed and substantiated. This approach integrates network parameters, equipment properties, and external actions. It is validated by modeling the improvement of energy efficiency, reduction of delays, and increase in fault tolerance. Within this framework, a five-tier hierarchical concept of multilevel network infrastructure for monitoring agroindustrial objects based on WSN has been developed. It incorporates models and algorithms at the levels of: devices, physical channels, data transmission channels, linear routes, and networks. Single-level and inter-level dependences linking performance indicators, destabilizing factors, and controllable parameters have been established.

Discussion. The presented approach addresses the gap between energy models and the consideration of dynamic/information constraints of nodes, while also taking into account the actual operating condition of modems, and the thermal dependence of power sources. The multilevel integration of criteria (from signal shape correlation indicators to network probabilistic metrics of WSN integrity) allows for the alignment of local optimization and system goals, reducing the risk of conflicts between levels. The principle of level matching and external augmentation provides iterative adjustments of requirements and parameters, which increases the robustness of decision-making to environmental uncertainty and channel heterogeneity. Constraints of the current work include the need to calibrate models for specific hardware profiles, the dependence of efficiency on available PHY/MAC modes and ARQ protocols, and sensitivity to the accuracy of interference environment and temperature assessments.

Conclusion. The developed models and algorithms across five levels provide the specified metrics of interference resilience, delivery time and energy consumption with the minimum required involvement of resources, which increases the survivability and service life of the WSN. The proposed approach creates the basis for the transition to systemically designed, reproducible solutions in precision agriculture. It reduces resource costs and environmental impact, and also increases the sustainability and profitability of agricultural production. Scaling requires field testing and publication of reference configurations and codes for reproducibility.

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