@ARTICLE{Pasek_Przemysław_Energy-efficient_2024,
 author={Pasek, Przemysław and Kaniewski, Piotr},
 volume={vol. 31},
 number={No 3},
 journal={Metrology and Measurement Systems},
 pages={465-480},
 howpublished={online},
 year={2024},
 publisher={Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation},
 abstract={The paper introduces and assesses the Eigenvalue Covariance Intersection (EVCI) algorithm for data fusion in Wireless Sensor Networks. The EVCI aims to enhance information fusion efficiency, reduce transmitted data, and potentially extend network lifespan. By conducting the eigendecomposition of covariance matrices, the EVCI evaluates the utility of eigenvectors and strategically employs only those positively impacting estimate accuracy. Through simulations and comparisons with the Covariance Intersection (CI) algorithm, the study demonstrates EVCI’s ability to maintain accuracy alongside with significant energy savings. The paper provides insights into popular data fusion algorithms, the concept of the EVCI, used formulas, and selected simulation results.},
 type={Article},
 title={Energy-efficient distributed estimation algorithm for wireless sensor networks based on covariance intersection with eigendecomposition},
 URL={http://journals.pan.pl/Content/133159/02_1k.pdf},
 doi={10.24425/mms.2024.150290},
 keywords={wireless sensor networks, data fusion, state estimation, covariance intersection, energy efficiency, data reduction},
}