## 1 Introduction  
The industrial wireless sensor networks represented by WirelessHART [1] are a type of industrial Internet and are used in a variety of industries, including manufacturing, mining, and agriculture [2]. This network collects information on various devices and environments used in each field and makes decisions that increase profits, improve efficiency and productivity, and reduce costs in each industry [3, 4]. For example, in the case of a monitoring system [5], by continuously monitoring the condition of the current operating equipment, damage and failure of the equipment can be predicted in advance to prevent quality or productivity deterioration due to equipment faults. In the case of an inventory management system [6], it is possible to identify the amount of raw materials and product loading needed to prevent shortages of raw materials and overloading of products. In addition, cost savings can be achieved by automating processes using information sensed by nearby sensors. To utilize IWSNs in various fields, as described above, data collection based on low delays and a high reliability of data transmission is required [7,8]. However, in contrast to traditional wired networks [9,10], wireless networks are vulnerable to collisions and interference between transmissions [11]. Therefore, to address collision and interference issues, IWSNs perform graph construction and resource allocation through a network manager. That is, the network manager collects information about all of the devices that comprise the network to construct a directed graph for the entire network topology. Thereafter, resource allocation is performed using time-slotted channel hopping (TSCH) based on the constructed graph. This determines at what time each device delivers data to which device and via which channel. This process solves the problem of collisions and interference between data transmissions among devices. However, IWSNs have been designed with a focus on alleviating the disadvantages of wired systems, such as difficulties in installation and wiring and facility protection [12]. Therefore, they do not adequately respond to topology changes caused by the movement of workers and mobile devices [13,14]. For example, when collecting worker information to ensure worker safety, such as in the FASyS project, network topology changes will occur due to the movement of workers. However, it is difficult for existing schemes to cope with network topology changes. In other words, the graph construction and resource allocation needed to cope with the topology changes have to be performed again, and communication between the mobile devices and the network are lost during this process. In addition, as devices that deliver packets through mobile devices are also affected, the overall network performance is degraded, which could cause fatal problems in various industrial applications. Therefore, we propose a mobility support scheme based on decision tree-based pattern classification and regression. First, the decision tree is exploited to classify the diverse mobility patterns of mobile devices. By analyzing mobility data, the decision tree categorizes movement patterns into linear, cyclic, or random movements. The proposed scheme utilizes a decision tree for mobility pattern classification due to its interpretability, computational efficiency, and suitability for real-time applications in industrial wireless sensor networks (IWSNs). Unlike more complex models, such as neural networks, decision trees provide clear and interpretable decision boundaries, which are crucial for resource allocation in dynamic industrial environments. While neural networks may offer higher accuracy in some scenarios, their reliance on large datasets and high computational requirements makes them less practical for resource-constrained environments like IWSNs. Once the mobility pattern is identified, each pattern is assigned to the most suitable predictive model.