2010 Poster Sessions : Mobile Data Delivery through Mobility Pattern Prediction in Sensor Networks

Student Name : HyungJune Lee
Advisor : Leo Guibas
Research Areas: Computer Systems, Graphics/HCI
Abstract:
With the advent of ubiquitous wireless networks, supporting mobility of users for network applications has become more crucial in network research. However, algorithms for wireless networking face some fundamental challenges on supporting mobility. Mobility makes wireless links much more volatile than stationary networks, and the current connectivity status may be outdated even after a few seconds, and hence data delivery to mobile sinks often fails and needs a number of retransmissions for a successful delivery. Although mobile pattern and mobility behavior modeling have been widely researched in the fields of cellular and Wi-Fi networks, no much investigation on a sophisticated dynamic modeling of mobile trajectories taken by mobile users with wireless connections, considering variability from mobility and wireless dynamics, as well as impact of the knowledge of users' mobile behavior on routing performance has been relatively conducted.

In this poster, we present a routing scheme that exploits knowledge about the behavior of mobile sinks within a network of data sources to minimize energy consumption and network congestion. For network applications with their own delay constraint, we propose to route data not to the sink directly, but to send it instead to a relay node along an announced or predicted path of the mobile node that is close to the data source. The relay node will "stash" the information until the mobile node passes by and picks up the data. We use linear programming to find optimal relay nodes that minimize the number of necessary transmissions while guaranteeing robustness against link and node failures, as well as trajectory uncertainty. We show that this technique can drastically reduce the number of transmissions necessary to deliver data to mobile sinks. We derive mobility and association models from real-world data traces and evaluate our data stashing technique in simulations. We examine the influence of uncertainty in the trajectory prediction on the performance and robustness of the routing scheme.

Bio:
HyungJune Lee is a Ph.D. candidate in the department of Electrical Engineering at Stanford University, working with Professor Leonidas Guibas. His research focuses on efficient mobile routing and localization algorithms, and distributed network algorithms by game theoretic approaches in wireless networks. His thesis contribution is on mobility pattern modeling from wireless connections and optimal routing strategy for mobile users through mobility pattern prediction in wireless mesh networks. He received his M.S. in Electrical Engineering from Stanford University, and his B.S. in Electrical Engineering from Seoul National University, South Korea.