Principal Component Analysis with Missing Data and Its Application to Polyhedral Object Modeling
IEEE Transactions on Pattern Analysis and Machine Intelligence
User Mobility for Opportunistic Ad-Hoc Networking
WMCSA '04 Proceedings of the Sixth IEEE Workshop on Mobile Computing Systems and Applications
An Agenda Based Mobility Model21
ANSS '06 Proceedings of the 39th annual Symposium on Simulation
Real-world environment models for mobile network evaluation
IEEE Journal on Selected Areas in Communications
User satisfaction evaluation of GPRS network using PCA
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
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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