Framework for assessing data quality resembles factor in large-scale wireless sensor network - Prathiba, B.; Sankar, Dr. K. Jaya; Sumalatha, Dr. V. (International Journal of Engineering and Technology, 2026)
The usage of sensors has undergone a significant revolution right from the wireless sensor network (WSN) to Internet-of-Things (IoT). Existing research towards clustering protocols shows that their inclination is more on solving common issues and not more on data redundancy that should be controlled to enhance data quality. The closer relationship between the redundancy and data quality in WSN is less investigated for a practical solution. Therefore, we present a framework called as DQRF, i.e., Data Quality Resembles Factor which is exclusively meant for carrying out clustering operation for increasing data quality. The framework is supported by four sequential algorithms, which are designed to find the node that carries significant redundant information. The idea is to capture global details of all the nodes in the short run of clustering to identify and eliminate all possible errors. The proposed system offers approximately 93% of enhancement in data in contrast to the existing system.