Mobile ad hoc networks enable communica­tions between clouds of mobile devices without the need for a preexisting infrastructure. One of their most interesting evolutions are opportunis­tic networks, whose goal is to also enable com­munication in disconnected environments, where the general absence of an end-to-end path between the sender and the receiver impairs communication when legacy MANET networking protocols are used. The key idea of OppNets is that the mobility of nodes helps the delivery of messages, because it may connect, asynchronous­ly in time, otherwise disconnected subnetworks. This is especially true for networks whose nodes are mobile devices (e.g., smartphones and tablets) carried by human users, which is the typical Opp­Nets scenario. In such a network where the move­ments of the communicating devices mirror those of their owners, finding a route between two dis­connected devices implies uncovering habits in human movements and patterns in their connec­tivity (frequencies of meetings, average duration of a contact, etc.) and exploiting them to predict future encounters. Therefore, there is a challenge in studying human mobility, specifically in its application to OppNets research. In this article we review the state of the art in the field of human mobility analysis and present a survey of mobility models. We start by reviewing the most considerable findings regarding the nature of human movements, which we classify along the spatial, temporal, and social dimensions of mobil­ity. We discuss the shortcomings of the existing knowledge about human movements and extend it with the notion of predictability and patterns. We then survey existing approaches to mobility modeling and fit them into a taxonomy that pro­vides the basis for a discussion on open problems and further directions for research on modeling human mobility.

Recent analyses of the mobile phone market revealed an incredible penetration of pocket devices among the global population, estimating more than 5.3 billion people worldwide using cell phones, with the smartphone market hitting

3 million devices sold to end users in 2010, accounting for 19 percent of total mobile com­munications device sales. Added to the number of cameras installed on the streets, computing systems embedded into vehicles and equipped with wireless communication capabilities (e.g., Wi-Fi, Bluetooth, cellular, WiMax), they define an all-pervasive nature of opportunistic contacts between pairs of devices and thus give a strong background for developing opportunistic net­works (OppNets).

A thorough study of individual movements became possible only recently with the availabili­ty of real-life mobility traces collected by cell phone operators and academic experiments , and Internet communities . The most accurate data come from systems that are direct­ly designed to track location by either exploring satellites (e.g., GPS) or a system of radio emit­ters (e.g., RIPS). Another approach is based on utilizing nodes of communication systems such as GSM base stations or WLAN access points.

in the field of human mobility analysis and a sur­vey of human mobility models. Following the logic introduced above, we start by reviewing the most considerable findings in the physics of human mobility and from studying connectivity patterns in mobile ad hoc networks (MANETs). We discuss the shortcoming of the existing statis­tical knowledge on human movements, and extend it with the notion of predictability and patterns. We then introduce our approach to systematize repetitive tendencies in human movements in social, temporal, and spatial dimensions. This provides us with a taxonomy for surveying existing approaches to modeling mobility. Finally, we conclude with a discussion of open research directions.

Utilizing Social Graph

The most recent and most rapidly evolving trend in modeling human mobility is based on incorpo­rating complex network theory and considering human relations as the main cause of individu­als' movements. Social relations are described as a graph, where nodes represent individuals and weighted edges the degree of the social connec­tion between them. Complex network analysis revealed a number of significant properties in the structure of social graphs. .These findings, along with the intuitive idea that our movements are highly influenced by the need for social interactions, led to the emer­gence of a new class of mobility models. The main idea is that the destination for the next move of a user depends on the position of peo­ple with whom the user shares social ties

Conclusion

The first observation comes from the three-dimensional nature of human movements. Start­ing from the analysis of recent works in the field, we highlighted the focus on spatial, temporal, and social aspects in studying human mobility traces.

The Working Day Movement model  is a lightweight and scalable approach, organized in the extensible framework of different types of activities. Additionally, this model incorporates some sense of hierarchy and distinguishes between interbuilding and intrabuilding move­ments. Hence, the authors introduce home, office, evening activities, and different transport submodels (walking, car, bus, etc.). An office model, for example, reproduces a kind of star­like trajectory around the desk of the person at the selected coordinates inside the office build­ing, while a home model is just a sojourn in a particular point of a home location. Different social patterns are introduced in the model. For example, the evening activity submodel reflects a meeting with friends after work by modeling a random walk of a group along the streets. Being more flexible in its application to different sce­narios than the model by Zheng et al. , this model, however, suffers from the same complex­ity problems.