Kai Peng;Tongxin Liao;Xi Liao;Jiangshan Xie;Bo Xu;Tianping Deng;Menglan Hu
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引用次数: 0
摘要
随着区块链技术在移动物联网(Mobile Internet of Things, MIoT)网络中的广泛应用,区块链数据量的增长显著增加了对端节点的存储压力。协作存储是一种很有前途的解决方案,它将区块链数据分布在集群中的各个节点上。然而,移动节点的频繁移动破坏了集群结构,现有的静态解决方案无法解决这种动态特性,使其无效。为了解决这一问题,我们提出了一种基于动态集群的移动节点迁移方案(DCMM),该方案包括两个关键部分:新集群选择和块重新分配。动态节点同步算法(dsa)优化集群选择,动态块分配算法(DBAA)管理高效的块重新分配。与五种基线方法的比较分析表明,在考虑访问成本、迁移成本和停留时间的加权优化目标中,DCMM将性能提高了16.69%以上。这些结果表明,与基线算法相比,我们的方法显着优化了网络成本。
DCMM: Dynamic Cluster-Based Mobile Node Migration Scheme for Blockchain Collaborative Storage in Mobile IoT Networks
As blockchain technology becomes widely adopted in Mobile Internet of Things (MIoT) networks, the growing volume of blockchain data significantly increases storage pressure on peer nodes. Collaborative storage, which distributes blockchain data across nodes in cluster, offers a promising solution. However, the frequent movement of mobile nodes disrupts cluster structures, and existing static solutions fail to address this dynamic nature, rendering them ineffective. To address this issue, we propose a Dynamic Cluster-based Mobile Node Migration Scheme (DCMM), comprising two key components: new cluster selection and block redistribution. The Dynamic Node Synchronization Algorithm (DNSA) optimizes cluster selection, and the Dynamic Block Allocation Algorithm (DBAA) manages efficient block redistribution. Comparative analysis with five baseline approaches shows that DCMM improves performance by over 16.69% in the weighted optimization objective, which considers access costs, migration costs, and dwell times. These results demonstrate that our approach significantly optimizes network costs compared to baseline algorithm.
期刊介绍:
The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.