ENERGY-SAVING METHOD IN WIRELESS SENSOR NETWORKS

Abstract

Relevance. WSNs are characterized by many autonomous nodes that are expected to operate for extended periods without battery replacement or recharging. This poses a significant challenge for researchers and developers to find effective energy-saving methods that can substantially extend the autonomous operating time of the nodes and ensure the stable and reliable functioning of the entire network. The object of research is the processes of energy consumption and functioning of Wireless Sensor Networks. Purpose of the article is the development and investigation of an energy-saving method in Wireless Sensor Networks based on the use of machine learning algorithms, particularly artificial neural networks of the Kohonen map type and their modifications, in order to ensure the most efficient utilization of node energy resources, optimize data transmission and processing processes, and increase the duration of autonomous operation and the reliability of sensor network functioning under variable operating conditions. Research results. An energy-saving method based on modified Kohonen maps has been developed. The proposed approach involves multifactor clustering of nodes considering their energy parameters, adaptive selection of transmission routes, regulation of node activity modes, and online retraining of the map. Conclusions. The energy consumption of nodes depends not only on the hardware configuration but also on the method of data exchange organization, the chosen network topology, transmission frequency, and environmental conditions. The issue of energy depletion in individual nodes is critical, as it can lead to network fragmentation or complete network failure. Therefore, there is a need for dynamic, intelligent management that considers both local and global characteristics of the network.