当前位置: X-MOL 学术Comput. Intell. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Light chain consensus reinforcement machine learning: An effective blockchain model for Internet of Things using for its advancement and challenges
Computational Intelligence ( IF 2.8 ) Pub Date : 2020-08-27 , DOI: 10.1111/coin.12395
K. Priyadharshini 1 , R. Aroul Canessane 2
Affiliation  

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%.

中文翻译:

轻链共识强化机器学习:一种有效的物联网区块链模型,用于其进步和挑战

最近,与物联网(IoT)相交的区块链为工业、医疗诊断和教育领域等不同应用提供了整合机会。在交叉路口出现了一些冲突,解决区块链的巨大资源利用、海量物联网的效率和安全问题的目的在目前的情况下没有得到解决。目前,Ruff-chain、区块链联盟基础、移动云区块链(MCBC)、探测物联网以及为克服区块链交叉物联网的缺点而部署的工作量证明需要高资源利用率和功耗。为了解决这个问题,开发了一种轻链共识强化机器学习(LCC-RML)方法来优化区块链有效交叉的物联网系统,并从资源利用、数据安全去中心化、可扩展性和延迟等方面协助提供学习方法。在 LCC-RML 中,在不影响去中心化系统、安全性和延迟的情况下,通过底层区块链方法提高了可扩展性。在这里,专为区块链交叉物联网平台设计了一个更轻量级的模型,它包含优化的学习过程、减小的区块大小、轻量级的共识数据结构以及相关的有效区块间隔,以简化数据处理。实验分析已经在学习框架中进行了评估,以提高区块链交叉物联网系统的性能,计算速度提高了 84.89%,资源利用率降低了 85.88%。在功耗方面进一步降低了 57.55%,计算成本为 29.55%,可扩展性为 86.88%。
更新日期:2020-08-27
down
wechat
bug