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Variational Onsager Neural Networks based Fair proof-of- reputation consensus for blockchain with Transaction Prioritization for Smart Cities

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Abstract Smart cities are next frontier of technology in today's technology-driven world, striving to improve the quality of people's lives. Numerous research projects concentrate on future smart cities, taking comprehensive method to smart city growth, achieving an overall smart city vision. The Variational Onsager Neural Networks based Fair proof-of-reputation consensus for block chain with Transaction Prioritization for Smart Cities (VONN-FPORC-TP-SC) is proposed for transaction prioritization in smart cities. Block chain, as a decentralised immutable ledger, has potential to boost smart city growth by ensuring transparency, data safety, dependability, efficacy, interoperability, privacy, making it promising match for smart cities. Such instance, if existing fee-depend or first-come-first-served processing is applied, emergency events delayed owing to competition, risking people's lives. Here use Variational Onsager Neural Networks (VONN) approach to accomplish efficient leader election and build a Weighted Byzantine Fault Tolerance consensus algorithm mechanism in this consensus protocol. A peer-prediction based verification technique is also presented to verify that followers' assessments of the leaders' created blocks are honest. Also, because leader will assure transaction prioritisation though creating blocks, leader rotation, and correct election method become critical for transaction prioritisation process to taken place honestly, quickly on FPoR: fair proof-of-reputation consensus for block chain. The proposed VONN-FPORC-TP-SC method is implemented on MATLAB R2019b. Then performance of proposed method is analysed with other existing techniques. The proposed method attains 28.86%, 24.47% and 31.79% higher accuracy, 18.25%, 32.27% and 26.89% higher efficiency, and 23.21%, 17.36% and 32.35% higher Robustness comparing with the existing methods such as a ML-improved block chain consensus with transaction prioritization for smart cities (ML-BC-TP-SC), block chain and smart contracts to secure property transactions in smart cities (BCS-TP-SC), presented an Edge TC-a PBFT block chain-depend ETC system for smart cities (PBFT-ETC-TP-SC) respectively.
Title: Variational Onsager Neural Networks based Fair proof-of- reputation consensus for blockchain with Transaction Prioritization for Smart Cities
Description:
Abstract Smart cities are next frontier of technology in today's technology-driven world, striving to improve the quality of people's lives.
Numerous research projects concentrate on future smart cities, taking comprehensive method to smart city growth, achieving an overall smart city vision.
The Variational Onsager Neural Networks based Fair proof-of-reputation consensus for block chain with Transaction Prioritization for Smart Cities (VONN-FPORC-TP-SC) is proposed for transaction prioritization in smart cities.
Block chain, as a decentralised immutable ledger, has potential to boost smart city growth by ensuring transparency, data safety, dependability, efficacy, interoperability, privacy, making it promising match for smart cities.
Such instance, if existing fee-depend or first-come-first-served processing is applied, emergency events delayed owing to competition, risking people's lives.
Here use Variational Onsager Neural Networks (VONN) approach to accomplish efficient leader election and build a Weighted Byzantine Fault Tolerance consensus algorithm mechanism in this consensus protocol.
A peer-prediction based verification technique is also presented to verify that followers' assessments of the leaders' created blocks are honest.
Also, because leader will assure transaction prioritisation though creating blocks, leader rotation, and correct election method become critical for transaction prioritisation process to taken place honestly, quickly on FPoR: fair proof-of-reputation consensus for block chain.
The proposed VONN-FPORC-TP-SC method is implemented on MATLAB R2019b.
Then performance of proposed method is analysed with other existing techniques.
The proposed method attains 28.
86%, 24.
47% and 31.
79% higher accuracy, 18.
25%, 32.
27% and 26.
89% higher efficiency, and 23.
21%, 17.
36% and 32.
35% higher Robustness comparing with the existing methods such as a ML-improved block chain consensus with transaction prioritization for smart cities (ML-BC-TP-SC), block chain and smart contracts to secure property transactions in smart cities (BCS-TP-SC), presented an Edge TC-a PBFT block chain-depend ETC system for smart cities (PBFT-ETC-TP-SC) respectively.

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