A Blockchain-Based Architecture of Web3.0: A Comprehensive Decentralized Model with Relay Nodes, Unique IDs and P2P

The advent of the internet’s Web2.0 era provided users with vast data and services but relied heavily on centralized servers, leading to data ownership limitations, privacy concerns, and security vulnerabilities such as data breaches and service disruptions. As Web3.0 emerges with a focus on decentralization, there is a growing need for efficient architectures that ensure scalability, security, and low-latency communication. This paper introduces a decentralized architecture for Web3.0, leveraging blockchain, relay nodes, unique IDs, and P2P communication. The proposed model integrates a blockchain-based unique ID management system for secure node identification and employs a multistep TPM-based verification algorithm to validate node authenticity in real time. Additionally, the model introduces a TPM+Reputation system that combines hardware-backed node attestation with blockchain-stored historical reputation scores, ensuring robust trust evaluations. This dual-layered approach mitigates risks associated with reputation inflation, identity manipulation, and malicious node behavior. When faced with malicious requests, the system significantly reduces verification latency to an average of 0.0106 ms, ensuring rapid rejection and preserving network stability, while legitimate requests are processed efficiently with an average latency of 0.4125 ms.Experiments comparing latency-based and random-based relay selection methods across various network configurations (public-public, public-private, private-private) demonstrate that latency-based selection optimizes connection setup times in low-latency environments, while random-based selection provides robust performance in complex network scenarios. The integration of the reputation system further enhances the reliability of relay selection by prioritizing trustworthy nodes and dynamically penalizing malicious ones. In experimental evaluations, the system demonstrated a detection rate improvement of up to 1.9 times faster compared to traditional models in identifying and isolating malicious nodes, while maintaining consistent trust evaluations for legitimate nodes. These findings highlight the model’s adaptability and efficiency, offering a secure, scalable, and resilient solution for decentralized networks while addressing critical challenges in trust and reputation management.