Novel Framework forEnhancing Data Quality using Data Correlation Factor in Wireless Sensor Network

Abstract

Sensors are randomly deployed in Wireless Sensor Network (WSN)and it is observed that the sensor density is increasing rapidly. Owing to which there ispresence of similar forms of singular event data captured by different sensors. This problem of data correlation is closely associated with cluster formation process, in whichenergy efficiencyis emphasized compared to data quality.Existing reviews show that clustering approaches requiremajor revisionsin order to ensure betterdata quality. Therefore, the proposed system introduces a clustering mechanism that uses data correlation as the essential parameter for the selection of clusterhead,in order to control the transmission oferror-prone data packetsduring the process of data aggregation. Using analytical research methodology, the proposed system introduces three sequential clustering algorithms for ensuring better selection of clusterheadon the basis of best data correlation value. The simulated outcome of the proposed study shows that,it offers better data quality in contrast totheexisting system.