Here is a list of how-to solutions for problems that we have encountered while doing research in the i-NRG Lab. … Read More
Mobility Modeling in Wireless Networks
In this project 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 started 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 showed 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. To the best of our knowledge, this is the first time that these phenomena have been reported. Based on these observations, we proposed 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 compared our results to models whose steady-state do not preserve original non-homogeneous density distribution and showed that network performance under such regimes deviates from performance under real trace mobility.
Currently, we are interested in further characterizing and describing human mobility in WiFi Networks. Based on previous findings, we wish to identify possible communities within WiFi traces by applying data exploration and machine learning techniques. Our ultimate goal is to create a mobility model capable of realistically describing the human movement in both WiFi, GPS and cellular networks.