Summary of Trustworthy Dnn Partition For Blockchain-enabled Digital Twin in Wireless Iiot Networks, by Xiumei Deng et al.
Trustworthy DNN Partition for Blockchain-enabled Digital Twin in Wireless IIoT Networks
by Xiumei Deng, Jun Li, Long Shi, Kang Wei, Ming Ding, Yumeng Shao, Wen Chen, Shi Jin
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a blockchain-enabled digital twin (B-DT) framework to enhance manufacturing efficiency in industrial Internet of Things (IIoT) networks. The B-DT employs deep neural network (DNN) partitioning and reputation-based consensus mechanisms to improve the efficiency and trustworthiness of DNN inference tasks on IIoT devices. The authors first offload top-layer DNN inference tasks to access points using DNN partitioning, reducing computation burden at gateways. They then propose a reputation-based consensus mechanism that integrates Proof of Work (PoW) and Proof of Stake (PoS), evaluating APs’ reputations based on their computation resource contributions. The authors also formulate a stochastic optimization problem for communication and computation resource allocation to minimize system latency under time-varying channel states and long-term constraints. Experimental results show the proposed algorithm outperforms baselines in reducing overall latency while guaranteeing trustworthiness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a special kind of digital twin that uses blockchain technology to make it more efficient and trustworthy for use in industrial Internet of Things (IIoT) networks. This helps devices work together better, which is important for things like manufacturing and supply chain management. The authors used a technique called deep neural network partitioning to make the process more efficient and then developed a special way to decide who gets to add new information to the blockchain based on how well they did their job. |
Keywords
* Artificial intelligence * Inference * Neural network * Optimization
