ENDANGERED INFORMATION COMBINATION TECHNIQUE FOR WIRELESS SENSOR NETWORKS IN THE OCCURRENCE OF COLLUSION ATTACKS

Abstract

Iterative filtering algorithms hold great promise for this sort of purpose. Because of limited computational power and sources, aggregation of understanding from multiple sensor nodes finished in the aggregating node is generally accomplished by simple way of example averaging. During this paper we show several existing iterative filtering algorithms, while considerably greater quality against collusion attacks in comparison with simple averaging methods, are nonetheless susceptive having a novel sophisticated collusion attack we introduce. However such aggregation is called highly vulnerable to node compromising attacks. Because the performance of small power processors dramatically improves, future aggregator nodes will have a way to performing modern-day data aggregation algorithms, thus making WSN less vulnerable. Thus, ascertaining standing of knowledge and standing of sensor nodes is important for WSN. Such algorithms concurrently aggregate data from multiple sources and provide trust assessment of people sources, usually in a kind of corresponding weight factors utilized on data supplied by each source. To handle this security issue, we advise an apparent difference for iterative filtering techniques by providing a preliminary approximation for such algorithms which makes them not just collusion robust, but in addition better and faster converging.