A Many-Objective Optimized Sharding Scheme for Blockchain Performance Improvement in End–Edge-Enabled Internet of Things
Zhihua Cui, Zhaoyu Xue, Yanan Ma, Xingjuan Cai, Jinjun Chen
Abstract
This article establishes an edge computing architecture based on blockchain sharding to apply blockchain technology at the edge layer. The tasks offloaded to the edge layer are performed by a set of edge server nodes for consensus operations, which improves the security of offloading. At the same time, the nodes and offloaded tasks are divided into multiple shards, and the typical practical Byzantine fault tolerance (PBFT) consensus is performed in each shard to improve scalability. In order to avoid the impact of malicious nodes in an unreasonable sharding scheme on network security and performance, we model the many-objective optimization sharding problems, which includes reducing consensus latency, energy consumption, sharding failure probability, and improving sharding throughput. Moreover, we design a many-objective evolutionary algorithm based on information entropy (MaOEA-IE) to solve the model. In the process of population environment selection, the distribution information of individuals is mapped to a Gaussian function, and we aggregate the distribution functions of all individuals and calculate the information entropy to measure the distribution of the population. The simulation results of benchmark test problems and model solving problems show that the solution set of MaOEA-IE has the best convergence, diversity, and comprehensive performance. This article provides an idea for the formulation of blockchain sharding scheme.