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

arXiv: https://arxiv.org/abs/2502.09624 · HTML: https://mlsanity.com/paper/2502.09624/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 43 references that are in the map.
- First posted on arXiv 26 Jan 2025.

## Builds on (most cited first, 16 of 16)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Benchmarking Graph Neural Networks](https://mlsanity.com/paper/2003.00982/) (2020)
- [Attention, Learn to Solve Routing Problems!](https://mlsanity.com/paper/1803.08475/) (2018)
- [DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization](https://mlsanity.com/paper/2302.08224/) (2023)
- [EmbodiedScan: A Holistic Multi-Modal 3D Perception Suite Towards Embodied AI](https://mlsanity.com/paper/2312.16170/) (2023)
- [Autoregressive Diffusion Model for Graph Generation](https://mlsanity.com/paper/2307.08849/) (2023)
- [Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation](https://mlsanity.com/paper/2301.00427/) (2023)
- [DiffKG: Knowledge Graph Diffusion Model for Recommendation](https://mlsanity.com/paper/2312.16890/) (2023)
- [A Tutorial on 5G NR V2X Communications](https://mlsanity.com/paper/2102.04538/) (2021)
- [Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling](https://mlsanity.com/paper/2211.10936/) (2022)
- [Embodied AI-empowered Low Altitude Economy: Integrated Sensing, Communications, Computation, and Control (ISC3)](https://mlsanity.com/paper/2412.19996/) (2024)
- [Optimal AoI-based Block Propagation and Incentive Mechanism for Blockchain Networks in Web 3.0](https://mlsanity.com/paper/2403.12807/) (2024)
- [Graph Attention Network-based Block Propagation with Optimal AoI and Reputation in Web 3.0](https://mlsanity.com/paper/2403.13237/) (2024)
- [Embodied AI in Mobile Robots: Coverage Path Planning with Large Language Models](https://mlsanity.com/paper/2407.02220/) (2024)
- [Embodied AI-Enhanced Vehicular Networks: An Integrated Large Language Models and Reinforcement Learning Method](https://mlsanity.com/paper/2501.01141/) (2025)
- [An Intelligent Trust Cloud Management Method for Secure Clustering in 5G enabled Internet of Medical Things](https://mlsanity.com/paper/2207.09057/) (2022)

## Abstract (arXiv)

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.