UC Santa Cruz

n this work we study mobile wireless networks by looking at mobility management and analysis of human mobility, focusing on the main goal of understanding human mobility and applying our findings on developing new realistic mobility models for simulations. In our work, we start by analyzing Wireless Local Area Networks (WLAN) and GPS traces that record mobility in a variety of network environments. We observe that from a macroscopic level, human mobility is symmetric. We also study the direction of movement which also exhibits symmetric behavior in both real-- as well as synthetic mobility. Moreover, motivated by the symmetric behavior identified, we continued our investigation on real mobility characteristics, by focusing on node spatial density in real applications. We show that human mobility exhibits ``persistent'' behavior in terms of the spatial density distribution of the mobile nodes over time. By using real mobility traces, we observe that the original non-homogeneous node spatial density distribution, where some regions may be quite dense while others may be completely deserted, is maintained at different instants of time.

We also show that mobility models that select the next node position based on the position of other nodes, a la ``preferential attachment'', do not preserve the original spatial node density distribution and lead to behavior similar to random mobility as exemplified by the Random Waypoint model. Based on these observations, we propose a simple mobility model that preserves the desired spatial density distribution. We found that performance results expressed by a number of network metrics also match closely results obtained under mobility governed by real traces. We also Introduce a modeling framework to analyze spatial node density in mobile networks under "waypoint"-like mobility regimes. The proposed framework is based on a set of first order ordinary differential equations (ODEs) that take as parameters (1) the probability of going from one subregion of the mobility domain to another and (2) the rate at which a node decides to leave a given subregion.

Since the social structure usually guides the choice of new destination in real human mobility, we introduce a user mobility modeling framework that accounts for both the users' social structure as well as the geographic diversity of the region of interest. SAGA, or Socially-- and Geography-Aware mobility model, captures social features through the use of communities which cluster users with similar features such as average time in a cell, average speed, and pause time. SAGA accounts for geographic diversity by considering that different communities exhibit different interests for different locales. Besides introducing SAGA, the contributions of this work includes a model calibration approach based on formal statistical procedures to extract social structures and geographical diversity from real traces and set SAGA's parameters.