Laying The Theoretical Groundwork for A Seamless Transition to An Edge Intelligence-Driven Web3.0 Environment

This study introduces a well-defined method for Web3.0 edge intelligence improvement. Autonomous computing and modern algorithms and protocols make the system more dependable, secure, private, and efficient, while also ensuring data accuracy and network latency. Performance studies reveal that this strategy optimises energy economy and data accuracy better than others, which are crucial for dynamic edge decision-making. Strong security and privacy technology protects sensitive data, adhering to regulatory requirements. The ablation research examines all method components to see how they impact performance and propose improvements. According to the research, this strategy is superior to others since it handles problems, uses resources effectively, and saves money. This system advances edge computing. It allows long-term real-time data processing and analysis in dispersed environments. Future research may improve the ablation study and make it compatible with edge computing. This would improve Web 3.0 autonomous computing systems.