Optimal AoI-based Block Propagation and Incentive Mechanism for Blockchain Networks in Web 3.0

TL;DR

This paper tackles block freshness in Web 3.0, introducing Age of Block Information (AoBI) to quantify the timeliness of blocks and proposing a freshness-aware propagation framework. It combines a five-state epidemic-style block propagation model with an evolutionary-game incentive (BPIM) to drive rational miner forwarding and reduce AoBI. Theoretical analysis derives mean-field dynamics and steady-state consensus conditions, and numerical results show BPIM yields higher forwarding probabilities and faster convergence than baseline mechanisms, while lowering the minimum AoBI. The work advances blockchain performance and reliability in Web 3.0 and offers a foundation for future energy-aware, decentralized, and DRL-enhanced propagation strategies with practical impact on block propagation efficiency and data freshness.

Abstract

Web 3.0 is regarded as a revolutionary paradigm that enables users to securely manage data without a centralized authority. Blockchains, which enable data to be managed in a decentralized and transparent manner, are key technologies for achieving Web 3.0 goals. However, Web 3.0 based on blockchains is still in its infancy, such as ensuring block freshness and optimizing block propagation for improving blockchain performance. In this paper, we develop a freshness-aware block propagation optimization framework for Web 3.0. We first propose a novel metric called Age of Block Information (AoBI) based on the concept of age of information to quantify block freshness. AoBI measures the time elapsed from the freshest transaction generation to the completion of block consensus. To make block propagation optimization tractable, we classify miners into five different states and propose a block propagation model for public blockchains inspired by epidemic models. Moreover, considering that the miners are bounded rational, we propose an incentive mechanism based on the evolutionary game for block propagation to improve block propagation efficiency. Numerical results demonstrate that compared with other block propagation mechanisms in public blockchains, the proposed scheme has a higher block forwarding probability, which improves block propagation efficiency and decreases the minimum value of average AoBI.

Figures (13)

• Figure 1: A freshness-aware block propagation optimization framework for Web 3.0.
• Figure 2: Age of Block Information for public blockchains.
• Figure 3: The state transition diagram of the block propagation model for public blockchains.
• Figure 4: Consensus level $r(\infty)$ variations under the other two probabilities when one probability is fixed.
• Figure 5: Block propagation strategies of miners corresponding to different network conditions.

Theorems & Definitions (14)

• Proposition 1
• proof
• Proposition 2
• proof
• Theorem 1
• proof
• Theorem 2
• proof
• Theorem 3
• proof