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An intelligent cross layer security based fuzzy trust calculation mechanism (CLS-FTCM) for securing wireless sensor network - 2021

An intelligent cross layer security based fuzzy trust calculation mechanism (CLS-FTCM) for securing wireless sensor network

Research Area:  Wireless Sensor Networks


Wireless sensor networks (WSNs) have a significant number of sensing nodes with computing and communication resources for data transmission. Many essential aspects such as computational power, storage capability, and energy consumption must be taken into account to use WSNs for data transmission. Previous research work developed a cross layer security based intrusion detection system (CLS-IDS) framework that has improved network performance, minimal energy consumption and to prolong network life, meeting QoS requirements. However, this CLS-IDS method cannot detect the faults happening at various places at the same time. So, this research work proposes the cross layer security based fuzzy trust calculation mechanism (CLS-FTCM) and also least overhead monitoring for WSNs by means of memory and energy demands to resolve the above issue. This cross-layer security was designed with the aid of a fuzzy logic-based calculation method for a protocol called cross-layer protocol, uses multiple parameters extracted by the exchange of inter-layer information in order to alleviate the effects of security hazards to the network of wireless sensors. In order to identify malicious nodes on the network, the fault monitoring system is carried out using enhanced convolutional neural network (ECNN) classifier and the confidential value is determined for the trust values. The proposed cross layer security based fuzzy trust mechanism (CLS-FLTCM) is highly efficient technique to ensure optimum safety in wireless sensor environment when compared to the previous techniques. The simulation results conducted using the NS-2 simulator and testing the safety performance of the proposed system demonstrates that this approach is better than the current detection precision techniques.


Author(s) Name:   M. S. Sumalatha & V. Nandalal

Journal name:  Journal of Ambient Intelligence and Humanized Computing

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s12652-020-01834-1

Volume Information:  volume 12, pages 4559–4573 (2021)