A variable weight based fuzzy data fusion algorithm for WSN

Abstract

Due to the limited energy, storage space and computing ability, data fusion is very necessary in Wireless Sensor Networks (WSN). In this paper, a new variable weight based fuzzy data fusion algorithm for WSN is proposed to improve the accuracy and reliability of the global data fusion. In this algorithm, the weight of each cluster head node in global fusion is not fixed. Time delay, data amount and trustworthiness of each cluster head will all affect the final fusion weight. We get the fusion weights by variable weight based fuzzy comprehensive evaluation or fuzzy reasoning. In the variable weight based fuzzy comprehensive evaluation, by increasing the weight of the factor with too low value, we can give prominence to deficiency and the clusters with too long time delay or too small amount or too low trustworthiness will get smaller weights in data fusion. And therefore, the cluster head node with deficiency will have a small influence in global fusion. Simulation shows that this algorithm can obtain a more accurate and reliable fusion results especially when there are data undetected or compromised nodes compared with traditional algorithms. © 2011 Springer-Verlag.