Crypto Currency Compensation Model to Detect Optimal Channel of Internet of Things Through Blockchain

Shahan Yamin Siddiqui; Muhammad Toseef Javaid; Nusratullah Tauheed; Shakeel Abbas; Nabeela Yaqoob; Shahid Khalid

Authors

Shahan Yamin Siddiqui Department of Computing, NASTP Institute of Information Technology. Lahore, 53400, Pakistan.
Muhammad Toseef Javaid Department of Computer Science, University of South Asia, Lahore, 53000, Pakistan
Nusratullah Tauheed Department of Computer Science, University of South Asia, Lahore, 53000, Pakistan.
Shakeel Abbas Department of Computer Science, University of South Asia, Lahore, 53000, Pakistan.
Nabeela Yaqoob Department of Computer Science, University of South Asia, Lahore, 53000, Pakistan.
Shahid Khalid Department of Computer Science, University of South Asia, Lahore, 53000, Pakistan.

Keywords:

Blockchain, Internet of Things, Cryptocurrency, Optimal Automated Multi Resolution

Abstract

The ever-growing number of belongings of internet (IoT) devices in civilization creates a reliable, accessible, and safe infrastructure for processing the calculated data. One-point failures result from the prevalent IoT version's use of an imperative cloud server approach. Because Blockchain uses a distributed community, IoT is integrated with Blockchain generation to avoid this. Consequently, this study has developed a fully autonomous and self-regulating learning system that can accurately operate channel time/spectral characteristics to communicate multi-user statistics. The future system is distinct in that it uses community metrics as its primary basis to recognize and adjust to increasing community density. Following the extraction of those capabilities, the projected protocol efficiently selects the appropriate channel for incoming nodes based on its interval features, recognizes and allocates the idle spectrum of nearby channels, and provides the optimal and appropriate channel utilization through an article called multilevel Gaussian radial and a multilayer non-linear assist vector machine (SVM) type model. The value consumption rate of the secure network and its functionalities is calculated in order to assess the performance of the proposed system. Future and conventional systems are compared. Associated to the prior model, the accuracy of the current model is 95.6%.

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