Identifier Data Layout Strategy in Identifier Resolution System of Internet of Data
The Internet of Data(IoD)is a new information infrastructure proposed to address low transmission efficiency,high coordination costs,and challenging security controls in current internet data resource sharing and interactions.The key to enabling data circulation lies in the identification resolution system.However,as identification registration and resolution rapidly increase,resolution nodes encounter numerous high-concurrency resolution requests,making it difficult to ensure service quality.To tackle this issue,we propose a novel approach,the adaptive Discrete Particle Swarm Optimization algorithm based on Genetic Algorithm(DPSO-GA),considering the node architecture's characteristics in IoD's identification resolution system.This method aims to identify layouts effectively to reduce resolution latency.It comprehensively considers factors such as bandwidth,identifier count,and node capacity's impacts on resolution latency between nodes.Additionally,it introduces crossover and mutation operations from a Genetic Algorithm(GA),employs an adaptive strategy for the inertial weight factor of the Particle Swarm Optimization(PSO)algorithm,and utilizes a linear increasing/decreasing strategy for the learning factor.Experimental results demonstrate that compared to traditional PSO and GA,DPSO-GA improves optimization by 48.9％and 19.9％,respectively.This enhances population evolution diversity,expands search range,and reduces algorithm time complexity and the likelihood of falling into a local optimum,thereby consistently reducing identification resolution delays.
Internet of Data(IoD)identifier resolutiondata layoutheuristic algorithmGenetic Algorithm(GA)Particle Swarm Optimization(PSO)algorithm
万方数据
维普
