###### Abstract  
The fast expansion of ICT (information and communications technology) has provided rich sources of data for the analysis, modeling, and interpretation of human mobility patterns. Many researchers have already introduced behavior-aware protocols for a better understanding of architecture and realistic modeling of behavioral characteristics, similarities, and aggregation of mobile users. We are introducing the similarity analytical framework for the mobile encountering analysis to allow for more direct integration between the physical world and cyber-based systems. In this research, we propose a method for finding the similarity behavior of users' mobility patterns based on location and time. This research was conducted to develop a technique for producing co-occurrence matrices of users based on their similar behaviors to determine their encounters. Our approach, named SAA (similarity analysis approach), makes use of the device info i.e., IP (internet protocol) and MAC (media access control) address, providing an in-depth analysis of similarity behaviors on a daily basis. We analyzed the similarity distributions of users on different days of the week for different locations based on their real movements. The results show similar characteristics of users with common mobility behaviors based on location and time to showcase the efficacy. The results show that the proposed SAA approach is 33% more accurate in terms of recognizing the user's similarity as compared to the existing similarity approach. 202  
for contact and sharing knowledge [7]. An individual typically carries a mobile phone throughout the day. For this reason, the data on mobile phones have attracted the attention of researchers interested in studying the patterns of human mobility [8; 9; 10]. Handheld agent assemblies with identical characteristics of mobile communities have established trends in wireless networks. This comes as no shock as these features often have significant effects on device behaviors. Researchers have been researching ways to derive and test certain features. One significant point is that periodic re-emergences occur at some sites, which shows the relations between related instances. Therefore, individuals with common concepts of conduct connect; this provides a significant dimension in which pairing of the device position may be used to recognize resemblance trends of smartphone apps. So, to quantify similarity characteristics among cell phones for this study, we used their interests in spatial, temporal, and preferred word attachments to the sites and the travel frequencies and lengths of those areas [11; 12]. The existing approaches allow for the use of values dependent on the venue. Such ideals have common perspectives of sites at somewhat remote positions, although those sites can share the same network in most situations. Even some existing strategies have used the associated matrix and multiple access points for each building, so the building's doors and rooms are distinct locations, and people at such locations are assumed to have different mobility behaviors rather than identical mobility behaviors [5]. In this paper, we used the co-occurrence matrix to capture human mobility preferences. In the co-occurrence matrix, we used the location and time. We consider the location as a single access point for each building. We used device info, i.e., IP and MAC addresses, to identify the user's time and location. When different people are in the same building and are slightly apart, i.e., at different doors of a building, then the proposed approach regards these devices as having the same mobility behavior; instead of using different networks for data transmission, the same network is used to show similar mobility behavior. Our suggested approach involves examining correlation patterns in detail on a regular basis. The remainder of the paper is structured as follows. Section 2 presents the literature review.