A Novel Clustering Algorithm for Leveraging Data Quality in Wireless Sensor Network

Till date, the research work in Wireless Sensor Network is mainly inclined towards rectifying the problem associated with the nodes and protocol associated with it, e.g., energy problems, clustering issue, security loopholes, uncertain traffic, etc. However, there is less emphasis towards the user’s demand, i.e., data quality. As wireless nodes undergo various forms of adverse wireless condition in order to carry out data aggregation, it is quite inevitable that an aggregated data forwarded may not have a good data quality. Therefore, we present a novel clustering technique that concentrates on achieving the lowest possible error. With an aid of analytical modeling, a novel clustering technique is formulated using probability theory that targets the node with higher retention of redundant information so that it can be mitigated effectively. The study outcome shows better data quality of the proposed system.