Describing MANETs: Principal Component Analysis for Sparse Mobility Traces

Data collected in realistic mobility traces for mobile ad hoc networks (MANETS) is intrinsically high dimensional. Principal Component Analysis (PCA) is a good tool for reducing the data dimemsion by extracting important features of the data. We propose a method for computing principal components using iterative regression for high dimensional matricies with missing values with an application to node degree time series. We expand this method to handle an additional dimension of information for a defined neighborhood ancestry of node degree, exposing patterns when they exist. We test our methodology on node degree data from a simulated university campus model (Pedsims) and real campus data. Results indicate that in both cases, the student’s major field of study along with class schedule are strong factors to differentiate mobile node degree time series. The ability to detect differences is a powerful tool for application specific network management, allowing for: optimal placement of routers, design of specialized protocols for various user populations and lending insight to gauging the energy/bandwidth needs of mobile devices. Supported in part from NSF, grant number ANI-0338856, Texas ATP, project number 0036040036-2003 and Los Alamos National Laboratory, (CCS-5)