## 1 Introduction  
Smart devices such as mobile phones and laptops have gradually become everyday commodities in recent years due to their enhanced computing power. The processing and sensing capabilities of these smart devices have profoundly impacted various aspects of society [1]. They play a key role in expanding communication system coverage, becoming an integral part of the infrastructure through internet connectivity. The proliferation of billions of smart devices has generated a vast amount of data, presenting challenges in handling and transmission, commonly referred to as big data [2]. The transmission of such data has been a significant research area, with many studies focusing on developing various architectures for reliable, efficient, and resourceful designs. Among these architectures, the opportunistic network (Opp-Nets) stands out. Opp-Nets exploits time-delay-bounded file transmission using device-to-device (D2D) communication, rather than relying solely on the traditional internet infrastructure. In Opp-Nets, communities are established to route data from one node to another by defining the role of each node within the community [3]. These roles are assigned to various parameters, such as node popularity and centrality. The study of smart and connected communities lies at the intersection of civil infrastructures and cyber systems, offering a rich field for further research. There is existing research on and technologies involving algorithms such as the Gaussian process. These algorithms provide useful information and insights into the pattern of user movements. However, they are not fully accurate in real-life uses and complex cases. Complex cases reflects the trends and patterns in human mobility. In contrast to urban traffic, which has a hierarchical structure, human mobility possesses a high-level mobility trend comprising spatial, temporal, individual, and regional features [4] such as high-dynamic-mobility cases. The objective of this study is to develop an improved predictive model that can forecast human mobility interactions accurately and provide energy efficiency and improved network performance. The rapid transformation driven by innovations in intelligent sensors is now embedded in nearly every physical device and system. The results of such advancements can be seen in several areas, including travel, energy [5], emergency management [6,7], and healthcare [8]. A network of mobility devices exhibits uncertain behavioral patterns, which can be a critical factor in optimizing the data routing process [9]. Users equipped with smart and mobile devices demonstrate almost identical activities in their daily routines. These identical activities can be beneficial in forecasting the possible interaction of these smart devices, eventually representing the encounter of users utilizing these devices [10], as illustrated in Figure 1.  
A university campus environment is depicted in Figure 1, where users frequently visit various locations such as campuses, residences, hospitals, and related facilities. Typically, the mobility of these users can be described at the micro-level. For instance, a student or professor might visit the campuses, residences, and sports facilities as part of their daily routine, spending a specific amount of time at each location. A student or professor generally visits the campus from 9 AM to 5 PM, then moves to a sports area from 5 PM to 8 PM, and finally returns to their residence area for the rest of the evening, repeating this routine the next day. Moreover, while on campus, they follow specific routes, such as attending classrooms, delivering or taking lectures, and meeting colleagues. During these activities, they may encounter various communication devices that facilitate connectivity and file sharing, particularly over an Opp-Nets, which is suitable for delay-tolerant files [11,12]. This routine-based mobility information is valuable for predicting user mobility and can be leveraged for file sharing over the network, potentially reducing energy consumption.