LQMPCS: Design of a Low-Complexity Q-Learning Model based on Proof-of-Context Consensus for Scalable Side Chains

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Published: Sep 20, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i8.7922
Keywords:
Blockchain, Sharding, Mining, PoC, Consensus, Q-Learning, LSTM, CNN, Intents

Main Article Content

Anil Kumar Pandey
Department Of Information Technology, National Institute Of Technology Raipur, Raipur, India
Ashish Kumar Misal
Department Of Information Technology, National Institute Of Technology Raipur, Raipur, India

Abstract

Single-chained blockchains are being rapidly replaced by sidechains (or sharded chains), due to their high QoS (Quality of Service), and low complexity characteristics. Existing sidechaining models use context-specific machine-learning optimization techniques, which limits their scalability when applied to real-time use cases. Moreover, these models are also highly complex and require constant reconfigurations when applied to dynamic deployment scenarios. To overcome these issues, this text proposes design of a novel low-complexity Q-Learning Model based on Proof-of-Context (PoC) consensus for scalable sidechains. The proposed model initially describes a Q-Learning method for sidechain formation, which assists in maintaining high scalability even under large-scale traffic scenarios. This model is cascaded with a novel Proof-of-Context based consensus that is capable of representing input data into context-independent formats. These formats assist in providing high-speed consensus, which is uses intent of data, instead of the data samples. To estimate this intent, a set of context-based classification models are used, which assist in representing input data samples into distinctive categories. These models include feature representation via Long-Short-Term-Memory (LSTM), and classification via 1D Convolutional Neural Networks (CNNs), that can be used for heterogeneous application scenarios. Due to representation of input data samples into context-based categories, the proposed model is able to reduce mining delay by 8.3%, reduce energy needed for mining by 2.9%, while maintaining higher throughput, and lower mining jitters when compared with standard sidechaining techniques under similar use cases.

Article Details

How to Cite
Pandey, A. K. ., & Misal, A. K. . (2023). LQMPCS: Design of a Low-Complexity Q-Learning Model based on Proof-of-Context Consensus for Scalable Side Chains. International Journal on Recent and Innovation Trends in Computing and Communication, 11(8), 36–43. https://doi.org/10.17762/ijritcc.v11i8.7922
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Vol. 11 No. 8 (2023)
Section
Articles

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