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.

No abstract

This study presents a new method for enhancing fingerprint image. The process of the enhancement is divided into two phases: fingerprint is first enhanced using Gabor filtering and then the enhanced fingerprint can be further enhanced by using sparse representation with the priori information of ridge pattern based on classification dictionaries learning. In the second stage, first, the orientations of fingerprint patches are estimated by the weighted linear projection analysis and the quality of patches are evaluated by the coherence of point orientations. Second, the training patches are classified into eight groups based on their own orientations, and the training samples of each class are selected from candidate patches by their own quality. The corresponding classification dictionaries are learned in frequency domain. Finally, the fingerprint image is enhanced based on spectra diffusion by using classification dictionaries learning. The experiments are carried out using various fingerprint enhancement methods. The experiments show that the proposed method achieves better results in comparison with other methods, and can significantly improve the performance of automatic fingerprint identification system.

Wireless sensor networks are often deployed in unattended and hostile environments. Due to the resource limitations and multihop communication in WSN, selective forwarding attacks launched by compromised insider nodes are a serious threat. A trust-based scheme for identifying and isolating malicious nodes is proposed and a mixed strategy and a continuous strategy Monitor-Forward game between the sender node and its one-hop neighboring node is constructed to mitigate the selective dropping attacks in WSN. The continuous game will mitigate false positives on packet dropping detection on unreliable wireless communication channel. Simulation results demonstrate that continuous Monitor-Forward game based selective forwarding solution is an efficient approach to identifying the selective forwarding attacks in WSN.

No abstract