Paper Info
Title
Light chainconsensus reinforcement machine learning: An effective blockchain model for Internet of Things using for its advancement and challenges
Abstract
Recently, blockchain intersected the Internet of Things (IoT) has come up with an integrated opportunity for different applications such as industries, medical diagnosis, and the education sector. Several conflicts have risen during the intersection, where the purpose of addressing the enormous resource utilization of blockchain, efficiency, and security issues of massive IoT has not been tackled in the present scenario. Presently, Ruff-chain, blockchain consortium basis, mobile cloud blockchain (MCBC), probed IoT, and proof of work deployed to overcome the drawback of blockchain intersected IoT demands high resource utilization and power consumption. To address this issue, a light chain consensus reinforcement machine learning (LCC-RML) method has been developed to optimize the blockchain effectively intersected IoT system and it assists in providing a learning methodology from the aspects of resource utilization, data security decentralization, scalability, and latency. In LCC-RML, without affecting the decentralization system, security, and latency, scalability has been improved with the underlying blockchain approach. Here, a lighter model is designed especially for the blockchain intersected IoT platform, which contains optimized learning procedures, reduced block size, lightweight consensus data structure, and related effective block interval to streamline the data processing. The experimental analysis has been evaluated in the learning framework to improve the performance of the blockchain intersected IoT system with a computational speed of 84.89% and resource utilization reduction of 85.88%. Further in the power consumption has been reduced up to 57.55% with the computation cost of 29.55% with the scalability ratio of 86.88%.
Year
DOI
Venue
2021
10.1111/coin.12395
COMPUTATIONAL INTELLIGENCE
Keywords
DocType
Volume
blockchain, industrial Internet of Things, light chain, reinforcement learning
Journal
37
Issue
ISSN
Citations 
4
0824-7935
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Priyadharshini Kaliyamoorthy100.34
Aroul Canessane Ramalingam200.34