Digital twin (DT) has emerged as a promising solution to enhance
manufacturing efficiency in industrial Internet of Things (IIoT) networks. To
promote the efficiency and trustworthiness of DT for wireless IIoT networks, we
propose a blockchain-enabled DT (B-DT) framework that employs deep neural
network (DNN) partitioning technique and reputation-based consensus mechanism,
wherein the DTs maintained at the gateway side execute DNN inference tasks
using the data collected from their associated IIoT devices. First, we employ
DNN partitioning technique to offload the top-layer DNN inference tasks to the
access point (AP) side, which alleviates the computation burden at the gateway
side and thereby improves the efficiency of DNN inference. Second, we propose a
reputation-based consensus mechanism that integrates Proof of Work (PoW) and
Proof of Stake (PoS). Specifically, the proposed consensus mechanism evaluates
the off-chain reputation of each AP according to its computation resource
contributions to the DNN inference tasks, and utilizes the off-chain reputation
as a stake to adjust the block generation difficulty. Third, we formulate a
stochastic optimization problem of communication resource (i.e., partition
point) and computation resource allocation (i.e., computation frequency of APs
for top-layer DNN inference and block generation) to minimize system latency
under the time-varying channel state and long-term constraints of off-chain
reputation, and solve the problem using Lyapunov optimization method.
Experimental results show that the proposed dynamic DNN partitioning and
resource allocation (DPRA) algorithm outperforms the baselines in terms of
reducing the overall latency while guaranteeing the trustworthiness of the B-DT
system.

数字孪生（DT）已成为增强工业物联网（IIoT）网络中制造效率的有前途的解决方案。为了提高无线 IIoT 网络中 DT 的效率和可靠性，我们提出了一种区块链启用的 DT（B-DT）框架，采用深度神经网络分区技术和基于声誉的共识机制，其中在网关端维护的 DT 使用从关联 IIoT 设备收集的数据执行 DNN 推理任务。通过将顶层 DNN 推理任务卸载到接入点（AP）端，我们首先采用 DNN 分区技术，减轻了网关端的计算负担，从而提高了 DNN 推理的效率。其次，我们提出了一种基于声誉的共识机制，将工作量证明（PoW）和权益证明（PoS）进行融合。具体而言，所提出的共识机制根据每个 AP 的计算资源对 DNN 推理任务的贡献评估其链下声誉，并将链下声誉作为权益来调整区块生成的难度。第三，我们制定了一种通信资源（即分区点）和计算资源分配（即 AP 的顶层 DNN 推理和区块生成的计算频率）的随机优化问题，以在时变信道状态和链下声誉的长期约束条件下最小化系统延迟，并使用李雅普诺夫优化方法解决问题。实验结果表明，所提出的动态 DNN 分区和资源分配（DPRA）算法在减少总体延迟同时保证 B-DT 系统的可信度方面优于基线算法。