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- W2983856474 abstract "Much advancement in Wireless Sensor Network (WSN) occurred in the recent years forces a higher level of security in data transmission. Cryptographic keys are utilized for confidentiality, authentication, approval, and data integrity. Various research works were carried out to deal with key management issues in WSNs. Currently, a novel Self-managing Volatile Key Scheme (Self-VKS) for WSNs is initiated in which prime numbers are utilized for group key generation. It will accurately examine node security for limited amount of nodes. So, novel methodology is required to model network which can examine node with secure data transmission for more amount of nodes devoid of limitation. In this research paper, Fuzzy Fingerprint Biometric based Key Security (FFBKS) scheme is introduced by utilizing feature extraction. Extracted feature vectors securely produces private key for user. This key is sent to every sensor node, then private key among sensor nodes are produced by pseudo random number and user key. Then, Adaptive Possibilistic C-means Clustering (APCMC) is initiated for nodes grouping based on distance and identifier among nodes. Here group key is produced based on fuzzy membership function from prime numbers and it is utilized for estimation of security. After grouping is formed, data transmission is carried out among group key by fuzzy membership and sensor nodes are carried out by biometric based private key.Cluster group keys are diverse from one cluster to another. At last, recreation is carried out on platform of MATLAB simulator. The experimental results show that the proposed FFBKS scheme achieves better performance compared with the existing system in terms of simulation time, energy consumption, delay and attack detection rate." @default.
- W2983856474 created "2019-11-22" @default.
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- W2983856474 date "2020-01-01" @default.
- W2983856474 modified "2023-09-30" @default.
- W2983856474 title "FFBKS: Fuzzy Fingerprint Biometric Key Based Security Schema for Wireless Sensor Networks" @default.
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- W2983856474 doi "https://doi.org/10.1016/j.comcom.2019.11.007" @default.
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