Due to restricted computational power and energy assets, the aggregation of information from numerous sensor nodes is performed at the aggregating node and is typically done by using basic techniques, for example by averaging. Node compromising attacks more likely occur after such sort of aggregations of data. As wireless sensor networks are generally unattended and do not use any tamper resistant equipment, they are extremely vulnerable to compromising attacks. Therefore, determining the trustworthiness of information and the reputation of sensor hubs is vital for wireless sensor networks. As the execution of low power processors drastically enhances, future aggregator nodes will be equipped for performing more refined information aggregation algorithms, in this way making WSN less vulnerable. WSN stands for Wireless Sensor Networks. For this reason, Iterative algorithms hold high value. These algorithms take the data aggregated from different sources and give a trust appraisal of these sources, generally in the form of comparing weight variables which are given to information obtained from every source. In this paper, we show that few existing iterative filtering calculations, while altogether more vigorous against collusion attacks than the basic averaging methods, are in fact susceptive to a novel refined collusion attack which we launch. To address this security issue, we propose a change for iterative filtering procedures by giving an underlying estimation to such algorithms which make them collusion resistant as well as more precise and faster for merging purposes
