## 2 Related Work  
Human behavior involves common perspectives of sites in somewhat remote positions; those sites can share the same network in most situations. Even some existing strategies have used the associate matrix and use multiple access points for each building, the building's doors and rooms are taken as distinct locations even if they are at the same location [5]. To study human behavior, researchers have employed various data resources, including bank notes, tweets, and GPS devices [13]. These data can be useful for traffic control [1], city planning [14], mobile marketing [15], and much more. The data obtained from these sources can also help in empirically driven studies on human mobility. However, these resources show limitations in scale, data resolution, and adaption. To overcome the constraints in behavioral analysis resources, mobile phones have emerged as more efficient resources [16]. The use of smartphones is increasing; smartphones have become essential parts of our lives. The mobile phone is mostly used for communication and information exchange. People typically carry mobile phones around. The data used by a mobile phone is another way of representing the mobility preferences of a mobile user. Hence, it is a useful tool for identifying human mobility patterns. For this reason, mobile phone data are attracting researchers who are interested in studying human mobility patterns. As with mobile data, a large number of users can be tracked in the long term at a low cost. Among mobile users, some show similarities in mobility behaviors while others are dissimilar to one group but similar to another. This is what we call similarity in mobility behavior. Before discussing the similarities in mobility behaviors, first, we shall focus on the existing models for mobility modeling. Mobility modeling provides a medium for simulating mobility in wireless ad hoc networks [2; 17], urban planning, and disaster response systems. Mobility models use mobility traces to understand the nature of mobility. In mobility modeling, we usually collect the real-time data of mobile users carrying a mobile phone with them during traveling, shopping, in the office, or university, etc. The mobility traces are the results of human mobility. This mobility is a result of repeated movements of a user to a specific place, time-based human activities, mobility, and mobility based on social relationships [2]. The mobility of mobile users may be at a large scale, e.g., traveling from country to country, or by airplanes and trains. Mobility at a smaller scale can involve, for example, traveling within a city or a university, or from home to an office. After the collection of traces, the data are altered and the daily life environment can be modeled. Moreover, mobility patterns can be used to model people's decisions based on their behaviors. Through mobility modeling, various operations of a network, i.e., resource management, routing, handover, and even a better independent deployment of connectivity models, can be simulated. The simulation is possible due to the possibility of understanding human movement patterns and regularities [14]. Modeling human mobility, at first, understanding the mobility preferences of people is required. Mobility traces gathered through GPS devices, social media accounts, and mobile phone networks can be used to analyze human movements deeply at large scales. Moreover, there lies a high degree of predictability in human mobility as most people tend to spend most of their time at specific locations. The time to visit a particular location is also mostly fixed [17]. Although preliminary research suggests a connection between mobility, activity data, and clinical phenomenology, no further conclusions can be drawn given the limitations of the methods that are currently used. For instance, a 2015 study found a correlation between the severity of depressive symptoms and GPS-derived metrics, such as normalized entropy and location variance [18].