INTELLECTUAL DECISIONS TO COMPRESSIVE SENSING FOR EFFICIENT DATA ACQUISITION IN WIRELESS SENSOR NETWORKS - Shyshkanova, G. A.; Zaytseva, T. A.; Zhyr, S. I.; Korotunova, O. V. (Systems and Technologies, 2026)
This paper considers the application of linear algebra methods to Compressive Sensing technology for wireless sensor networks (WSNs) specialized in temperature monitoring in “smart” greenhouses. It is shown that the traditional approach, which involves the transmission of full data streams from each individual sensor, leads to high energy consumption and generates information redundancy, which is a problematic factor for autonomous battery-powered systems. As an alternative, a special architecture of compressive sensing is proposed, within which not the original data are transmitted, but their compressed linear combinations. This radically reduces traffic, but, thanks to mathematical transformations, retains the possibility of full and accurate restoration of the entire temperature field on the receiver side. The methodological basis is the use of the fundamental property of sparsity of temperature signals when they are presented in certain bases, in particular, the basis of the discrete cosine transform. Modern optimization and iterative linear-algebraic algorithms are used to reconstruct the original data from compressive sensing. The practical effectiveness is illustrated by a model example of a network with six sensors, where compressing sensing allowed to reduce the amount of transmitted information by 50 % and simultaneously detect anomalies in the operation of a faulty sensor, confirming the stability of the system. The proposed architecture provides comprehensive energy efficiency, reliability and scalability of the monitoring system, supports the dynamic addition of new sensors and can be successfully integrated into modern Internet of Things systems, industrial complexes and «smart» cities. Thus, the work clearly demonstrates the powerful synergy between mathematical rigor and practical efficiency of compressing sensing for creating intelligent agricultural systems of the next generation.