Consensus-Aware Resource Collaboration for Blockchain-Empowered Immersive Digital Cousin Services in IIoT

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Abstract

With the rapid development of the Industrial Internet of Things (IIoT) and virtual reality (VR), immersive industrial services are increasingly required to provide real-time visualization, interactive perception, and trustworthy data management. Immersive Digital Cousin (DC) services provide a lightweight alternative to Digital Twin (DT)-based industrial applications, but VR-enabled DC (VR-DC) systems still require joint optimization of communication, computing, rendering, and consensus resources. In this article, a consensus-aware secure distributed resource collaborative optimization framework is developed for immersive VR-DC services in the IIoT. A unified system model is established to characterize uplink (UL)/downlink (DL) transmission, DC construction and update, collaborative rendering, block generation, and blockchain consensus. Based on this model, a quality of service (QoS) optimization problem is formulated by jointly considering service delay and blockchain transaction throughput. To solve the resulting hybrid discrete-continuous optimization problem, a consensus-aware fully decentralized multi-agent compound-action actor–critic (CAAC) algorithm, termed CA-FDMA-CAAC, is proposed. Here, compound-action refers to a coupled action representation that contains both discrete channel/subframe decisions and continuous rendering-workload-partition, computingallocation, and block-size decisions. The algorithm jointly optimizes channel assignment, subframe configuration, rendering workload partition, computing allocation, and block-size adjustment while mitigating multi-agent non-stationarity through an auxiliary learning mechanism. Simulation results show that the proposed algorithm converges within approximately 60–65 episodes and achieves a QoS performance gap of only about 2% comparedwith the centralizedMA-CAAC benchmark. Moreover, under a 3% processing-rate deviation, the DC-based framework reduces service delay by approximately 1.84% compared with the DT-based counterpart, demonstrating its effectiveness and robustness for immersive industrial services.

Keywords

Digital Cousin / Industrial Internet of Things / virtual reality / blockchain / consensus awareness / distributed resource optimization / multi-agent deep reinforcement learning.

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A Correction to this paper has been published:
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The Retraction Note was updated to reflect reception of statements of disagreement from all authors.

This article has been retracted.Please see the Retraction Notice for more detail:
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