Proficiency enrichment of Iterative Filtering Algorithm by Simulative Investigation

Abstract

Wireless Sensor Networks are setup in detrimental and exposed areas, so they are liable to be invaded by extraneous entities leading to malfunctioning nodes resulting in insertion of forged data. So, highly effective techniques for data aggregation are necessary. One of the techniques used for data aggregation is Iterative Filtering (IF) algorithm which is an excellent option to execute secure data aggregation by attributing weights to the nodes in accordance with their authenticity. The Iterative Filtering algorithm is executed with the aid of discriminant functions whose proper selection is of utmost importance for computation of weights of nodes as different convergence rates for the Iterative Filtering algorithm are imparted by different discriminant functions. In this paper, the convergence rates of Iterative Filtering algorithm is investigated and improved by using different exponential discriminant functions.