# Mobility-Assisted Digital Twin Network Optimization over Industrial Internet of Things
## 1 Introduction  
The evolution of information and communication technology has introduced various emerging technologies, such as the Industrial Internet of Things (IIoT), into smart factories [1]. In the context of intelligent manufacturing for smart factories, the application of IIoT-based devices has given many advantages including operation, scalability, and reliability [2]. Intelligent manufacturing should guarantee re-configurable manufacturing units and production lines for data acquisition because a lot of data are generated from IIoT-based devices and collected to use for real-time operation management [3]. The IIoT integrates a wide variety of devices, including sensors, actuators, and robots, into industrial environments, generating a massive amount of real-time data [4]. These devices must support stringent requirements for timeliness and reliability to ensure efficient and intelligent operations within smart factories [5]. However, the mobility of these devices introduces additional challenges, such as unstable routing and frequent handoffs, which must be addressed to maintain reliable communication and data transmission. Digital twins (DTs) can be deployed to improve the monitoring and control of physical systems for the intelligent factory [6]. A DT is a network system that distributes functions and wireless connectivity between smart physical devices, providing sensing and actuating. Furthermore, the DT paradigm is proposed to connect physical machines with virtual systems to optimize manufacturing processes and is a state-of-the-art Industry 4.0 revolution facilitated by advanced data analytics and IIoT connectivity. Furthermore, a digital twin network (DTN) is an emerging network that uses DT technology to design the virtual twins of physical objects [7]. A DTN is defined as a virtual representation of a physical network that can analyze, simulate, and control the physical network based on data andmodels to establish an efficient mapping between the IIoT and digital systems [8]. A DTN models a group of devices with multiple interactions and allows network operators to design network optimization solutions. The stochastic communication latency and the continuously growing data in the IIoT network make it hard for edge servers to collect and analyze data, including the channel state information, from IIoT devices [9]. IIoT networks also bring a lot of challenges to be solved, such as mobility issues. Although mobility is a critical feature of IIoT devices, current research on IIoT routing cannot effectively support the mobility issues in intelligent manufacturing systems because the number of IIoT devices connected is extremely large [10]. With such a tremendous number of IIoT devices, a lot of data are generated and transmitted in the network systems, and then, it is easy for congestion to happen, which may significantly affect the network performance. The mobility of IIoT devices exacerbates network congestion and introduces frequent changes in topology, leading to difficulties in maintaining stable communication [11]. These challenges require the development of new frameworks to ensure real-time communication and reliable data transmission in dynamic industrial environments [12]. Therefore, resource allocation for mobile devices is necessary to support real-time communication [13]. To address these challenges, e.g., computing is leveraged to reduce communication latency by processing data closer to the source rather than relying on centralized cloud services. This approach not only improves real-time decision making but also enhances scalability by distributing the computational load across multiple edge servers. Device-to-device (D2D) communication enables IIoT devices to support sensing and collecting data. Collected data from different mobile devices can improve quality management, product planning, and resource prediction for manufacturing cells. Therefore, the mobility problem can be solved using D2D communication between IIoT devices.