Efficient and Trustworthy Block Propagation for Blockchain-enabled Mobile Embodied AI Networks: A Graph Resfusion Approach

TL;DR

The paper tackles the problem of efficient and trustworthy block propagation in consortium blockchain-enabled MEANETs by introducing a cloud-model-based trust calculation that fuses reputation, trustworthiness, and risk, ensuring miners with high TS are selected. It then proposes a graph Resfusion framework that leverages diffusion-based residuals and a GatedGNN to generate optimal block propagation trajectories, reducing propagation time while maintaining trust constraints. Empirical results on real-world vehicle datasets demonstrate higher trust scores, shorter propagation times, and strong adaptability to dynamic MEANET environments, outperforming several baselines. This approach provides a practical path toward reliable, scalable blockchain-assisted MEANETs with enhanced security and performance in rapidly changing mobile networks.

Abstract

By synergistically integrating mobile networks and embodied artificial intelligence (AI), Mobile Embodied AI Networks (MEANETs) represent an advanced paradigm that facilitates autonomous, context-aware, and interactive behaviors within dynamic environments. Nevertheless, the rapid development of MEANETs is accompanied by challenges in trustworthiness and operational efficiency. Fortunately, blockchain technology, with its decentralized and immutable characteristics, offers promising solutions for MEANETs. However, existing block propagation mechanisms suffer from challenges such as low propagation efficiency and weak security for block propagation, which results in delayed transmission of vehicular messages or vulnerability to malicious tampering, potentially causing severe traffic accidents in blockchain-enabled MEANETs. Moreover, current block propagation strategies cannot effectively adapt to real-time changes of dynamic topology in MEANETs. Therefore, in this paper, we propose a graph Resfusion model-based trustworthy block propagation optimization framework for consortium blockchain-enabled MEANETs. Specifically, we propose an innovative trust calculation mechanism based on the trust cloud model, which comprehensively accounts for randomness and fuzziness in the miner trust evaluation. Furthermore, by leveraging the strengths of graph neural networks and diffusion models, we develop a graph Resfusion model to effectively and adaptively generate the optimal block propagation trajectory. Simulation results demonstrate that the proposed model outperforms other routing mechanisms in terms of block propagation efficiency and trustworthiness. Additionally, the results highlight its strong adaptability to dynamic environments, making it particularly suitable for rapidly changing MEANETs.

Figures (11)

• Figure 1: A graph Resfusion model-based trustworthy block propagation optimization framework for consortium blockchain-enabled MEANETs.
• Figure 2: A one-dimensional normal cloud model.
• Figure 3: The comprehensive architecture of the graph Resfusion model. This model generates the optimal block propagation trajectory by inputting the easily available degraded block propagation trajectory under the corresponding miner coordinates, thereby empowering the consortium blockchain-enabled MEANETs shown in Fig. \ref{['system_model']}.
• Figure 4: The architecture of the gated graph neural network (GatedGNN) for the graph Resfusion model.
• Figure 5: The total trust score of miners corresponding to different numbers of embodied AI vehicles.