Cluster-Based Blockchain Systems for Multi-access Edge Computing

The computing power and storage requirements of the Internet of Things (IoT) are likely to increase substantially in the future years. Because of the rapid development of both machine learning (ML) and the Internet of Things (IoT), vast volumes of data created by edge devices such as smartphones, laptops, and artificial intelligence (AI) speakers have been widely used to train ML models. In this study, we used a cluster-based Blockchain method in the Multi-Access Edge Computing (MEC, also known as Mobile Edge Computing) for markets and technological services. We describe a generalized stochastic block model (SBM) for edge computing applications based on the proposed taxonomy. These mobile edge wireless devices (WD) provide efficient resource allocation in mobile network situations. In our studies, we compared the approximate solutions obtained by the SBM to those generated by the cluster-based Blockchain algorithm. However, the high latency and low scalability of traditional blockchain systems limit mobile transactions on the public blockchain. To reduce the consumption of competitive mobile transactions created by linear sequencing blocks, reconstructed blockchain systems have been developed. This study’s use of cluster-based blockchain systems provides speedy confirmation and great scalability without significantly compromising security.