  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.
