A Predictive Framework for Personal Mobility Management
in Wireless Infrastructure Networks

Professor Sajal K. Das, Director
Center for Research in Wireless Mobility and Networking (CReWMaN)
Department of Computer Science & Engineering
The University of Texas at Arlington
Arlington, TX 76019-0015, USA

E-mail: das@cse.uta.edu


The global convergence of wired/wireless telecommunication networks and
IP-based data networks to form a seamless global personal communication
services (PCS) has set up the stage for a network independent universal
location service. Symbolic representation of "location" space in terms
of cells is the key to its viability. In this talk we will nvestigate the
efficacy and efficiency of reporting this symbolic location information
for predicting future locations with high accuracy. Since a good prediction
calls for a good model, one needs to figure out how to profile personal
mobility patterns of users. When the complexity of this mobility tracking
problem is characterized under an information-theoretic framework, it becomes
apparent that continuous ``path-update'' is a lot more informative as compared
to intermittent ``position-update.'' Thus, by capturing and maintaining a
dictionary of path-updates made by a mobile terminal, it is possible to
learn the underlying user mobility profile. We will propose such an adaptive
reporting strategy caled ``LeZi-update'', evolving out of the acclaimed LZ78
compression algorithms. While the compressibility of the variable-to-fixed
length LZ78 encoding is responsible for the efficiency of LeZi-update, the
predictive power comes out of the symbol-wise context model preserved in the
parse-tree created by the LZ78 incremental parsing. Under this framework,
universal learning, estimation and prediction of personal moobility profile
is possible for well-behaved users having (piecewise) stationarity in movement
patterns, in spite of the fact no single mobility model works for the diverse
set of wireless infrastructures. Simulating user movements based on realistic
activities, we evaluate the performance of LeZi-update in terms of its
location reporting activity as well as accuracy of its prediction.


Dr. Sajal K. Das received B.Tech. degree in 1983 from Calcutta University,
M.S. degree in 1984 from Indian Institute of Science, Bangalore, and PhD
degree in 1988 from the University of Central Florida, Orlando, all in
Computer Science. Currently he is a Professor of Computer Science and
Engineering and also the Founding Director of the Center for Research
in Wireless Mobility and Networking (CReWMaN) at the University of Texas
at Arlington (UTA). Dr. Das is a recipient of the Student Association's
Honor Professor Award in 1991 and 1997 for best teaching and scholarly
research; and UTA's Outstanding Senior Faculty Research Award in Computer
Science in 2001. He has visited numerous universities, research organizations,
government and industry research labs worldwide for collaborative research
and delivering invited seminar talks. He is frequently invited as a speaker
at international conferences and symposia. His current research interests
include resource and mobility management in wireless networks, mobile and
pervasive computing, wireless multimedia and QoS provisioning, sensor networks,
mobile Internet protocols, distributed processing and grid computing. He has
published over 200 research papers in these areas, directed numerous funded
projects, and filed 5 US patents in wireless mobile networks. He received
the Best Paper Awards in ACM MobiCom'99, ICOIN-2001, ACM MSWIM-2000,
and ACM/IEEE PADS'97. Dr. Das serves on the Editorial Boards of Computer
Networks, Journal of Parallel and Distributed Computing, Parallel Processing
Letters, Journal of Parallel Algorithms and Applications. He served as
General Chair of IEEE MASCOTS-2002 and ACM WoWMoM 2000-2002; General Vice
Chair of IEEE PerCom-2003, ACM MobiCom-2000 and HiPC-2001; Program Chair
of WoWMoM'98-'99; and as TPC member of numerous IEEE and ACM conferences.
He is a member of the IEEE TCPP Executive Committee and Advisory Boards
of several cutting-edge companies.