Aggregation has been known to be majorly vulnerable to node compromising attacks. Since WSN are usually unattended, they are highly susceptible to such attacks. Thus, determining trustworthiness of data and reputation of sensor nodes has become crucially important for WSN. Iterative filtering algorithms deem great promise for such a purpose. Such algorithms concurrently aggregate data from multiple sources & provide trust assessment of these sources, usually in a form of similar weight factors

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... llotted to data provided by each source. In concern with the security, we proposed improvement for iterative filtering techniques by rendering an initial approximation for such algorithms which makes them not only collusion robust, but also faster converging. It is conceived that so modified iterative filtering algorithms have a great latent for implementation in the future WSN. We extended the IF algorithms with a novel approach for collusion detection and revocation based on an initial approximation of the aggregate values as well as distribution of differences of each sensor readings.