A trust-based fuzzy neural network for smart data fusion in internet of things. (January 2021)

• Record Type: Journal Article
• Authors: Malchi, Sunil Kumar; Kallam, Suresh; Al-Turjman, Fadi; Patan, Rizwan
• Is Part Of: Computers & electrical engineering. Volume 89(2021)
• Publication Date: 2021-01
• Subjects: Internet of things -- Elfes probability -- Fuzzy -- Neural networks -- Trust mechanism -- Data storage -- Coverage fraction -- Defuzzification; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106901
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Towards improvement of the smart data fusion using fuzzy neural network for IoT. Proposed a trust-based neural network model for solving the IoT data storage problems. It enhances the and storage efficiency with minimum energy consumption. By distributing data to nodes without the defuzzification process. Performed an adequate data storage via Trust Mechanism. Abstract: Internet of Things (IoT) devices generates a vast amount of data from extensive applications. Maintaining the sensed data with low energy consumption, delay time, and adaptive coverage fraction rate proportionally influences the storage capacity. To maintain a trade-off between above-listed factors, we proposed an Elfes Sugeno Fuzzy and Trust-based Neural Networks (ESF-TNN) approach enables 3-algorithms. First, Elfes Probability Sensing (EPS) Model addresses the coverage fraction of each IoT sensor. Second, Sugeno Fuzzy Processing model regulates the energy consumption by proportionately distributing data to nodes without the defuzzification process. Third, Trust-based Neural Data Storage algorithm enriches an adequate data storage capacity by considering the average classification ratio while processing regenerated data packets to pertain each interaction information via Trust Mechanism. Simulation results show that our proposed method effectively covers the monitored area with 15 Joules of energy consumption and 1-ms delay time along with sufficient storage capacity. Graphical abstract: Image, graphical abstract