A profile based scheme for security in clustered wireless sensor networks

Data aggregation in WSN is usually done by simple methods such as averaging; these methods are vulnerable to certain attacks. To make trust of data and reputation of sensor nodes will be capable of performing more sophisticated data aggregation algorithm, thus making less vulnerable. Iterative filtering algorithm holds great promise for this purpose. To protect WSN from security issue, we introduce an improved iterative filtering technique. This technique makes them not only collusion robust, but also more accurate and faster converging. Trust and reputation systems have a significant role in supporting the operation of a wide range of distributed systems, from wireless sensor networks and e-commerce infrastructure to social networks, by providing an assessment of trustworthiness of participants in a distributed system. We assume that the stochastic components of sensor errors are independent random variables with a Gaussian distribution; however, our experiments show that our method works quite well for other types of errors without any modification. Moreover, if the error distribution of sensors is either known or estimated, our algorithms can be adapted to other distributions to achieve an optimal performance. In the first stage we provide an initial estimate of two noise parameters for sensor nodes, bias and variance; details of the computations for estimating bias and variance of sensors. We provide an initial estimate of the reputation vector calculated using the MLE, the detailed computation operations. In the third stage of the proposed framework, the initial reputation vector provided in the second stage is used to estimate the trustworthiness of each sensor based on the distance of sensor readings to such initial reputation vector.