## 1 Introduction  
With the continued advancement of the 5 th generation and the industry internet of things (IIoT), their applications have gained increasing attention. The world is expected to reach 15.9 billion in 2023 to more than 32.1 billion IoT devices in 2030 [1]. Hence, as one of the three scenarios of 5G, massive machine type of communication (mMTC) can handle a strong supply of the development of the IIoT devices [2], and efficient use of networking with IIoT could be achieved, thereby managing the resources for upcoming new service-driven industries [3] economically. It is considered one of the vital driving factors that contribute to achieving Industry 5.0 [4, 5]. Hence, IIoT devices are exposed to networking and state-of-the-art features that increase the volume of highly demanding resources. On the other hand, IIoT devices (e.g., sensors and actuators) [5] are accessed and connected to network servers in the core via a fronthaul physical network [6]. However,IIoT needs a complementary relationship between network computing and communication in terms of satisfaction with quality of service (QoS) and quality of experience (QoE). IIoT may continue to generate different types of tasks, with a large increase in processing time, computation resources, battery lifetime, and efficiency [7]. The core of utility resources, leveraging mobile edge computing (MEC), is typically demonstrated to offer resource sufficiency to affordable computing tasks of IIoT devices [8]. The open radio access network (O-RAN) revolutionizes the convention monoline structure, disaggregating base stations functionalized into distinct [9; 10]. O-RAN offers a new architecture and standard to support the multi-vendor forms factor that allows the use of multiple technologies integrated into 5G antennas and next-generation. Meanwhile, MEC is deployed into O-RAN [9; 10; 11; 12], which allows the closest user experience and addresses the energy-efficient demands of modern telecommunication and network systems. O-RAN is significantly orchestrated to MEC architecture, which ensures delivery service computation and allows the deployment of network connectivity, an effective way to break bottlenecks and latency packet delivery ratios. Within MEC, illustrating resource provisioning is the crucial technique for adjusting computing, which is one of the challenges. Software-defined network (SDN) and network function virtualization (NFV) are accomplished to archive the network target by providing virtualization and softwarization [13; 14; 15]. When MEC-enabled SDN and NFV are the accommodation of new experiences, they enhance the potential computing service for IIoT devices, providing low-latency computation capabilities and ensuring the network is compatible with scalability and reliability [16; 17]. MEC improves bandwidth utilization to enhance the user experience and overall network performance. However, tasks generated by IIoT devices incur an additional transmission to the controller that conflicts with delay (queuing) and energy consumption. Additionally, properly prioritizing and demanding resource management at the MEC server will influence task execution. Therefore, studies have been investigating MEC in terms of managing communication and computing resource capabilities. MEC can be ostensibly affordable in terms of resource utilization and computing capabilities, which are suitable for offering computing tasks in terms of offloading decisions, resource allocation, and resource prioritization. Despite management resource flexibility and dynamic adjustments, there have been crucial impacts in increasing computation time and energy efficiency. Thereby, artificial intelligence-driven (AI-driven) IIoT devices show opportunities to customize intelligent edge computing with heterogeneous hardware, which has good energy efficiency in processing specific AI-based tasks in network performance.