Enhance Capacity Region of Multi-Class IIoT Network by Applying Entanglement Assistance Protocol

In the Industrial Internet of Things (IIoT) context, heterogeneous IIoT nodes need diverse performance requirements, including throughput and quality of service (QoS). These IIoT nodes transmit data over a common shared communication medium. The existing critical challenge arises in efficiently scheduling access to this shared medium among a large number of connected IIoT nodes. To address the challenge of random access in IIoT networks, the power of the entanglement-assisted (EA) protocol was exploited to expand the capacity region boundaries of the shared communication medium, thereby enhancing the throughput and quality-of-service (QoS) requirements of the heterogeneous IIoT network. In the literature, IIoT networks are mainly categorised into two types: centralised and distributed. In this paper, we proposed two distinct models: (1) a centralised multi-class IIoT network based on EA protocol and (2) a distributed multi-class IIoT network based on EA protocol. Next, the authors analytically demonstrated that integrating the EA protocol into both proposed types of multi-class IIoT networks significantly increases the capacity region boundaries compared to the classical reference model, namely slotted ALOHA (SA). Finally, the network performance boundaries were evaluated by analysing the throughput values for different network classes and varying numbers of IIoT nodes. The results demonstrate that, for both proposed models (1) and (2), the transmitted load generated by the IIoT nodes over the shared medium achieves dramatically higher throughput compared to the reference IIoT network based on SA.