PhD Defense - Zengbin Zhang

Tuesday, August 26, 2014 - 10:00am
Harold Frank Hall 1132
Improving Location Accuracy and Network Capacity in Mobile Computing
Heather Zheng (Chair), Ben Zhao, Amr El Abbadi


Today’s mobile computing must support a wide variety of applications such as location-based services, navigation, HD media streaming and augmented reality.  Providing such services requires large network bandwidth and precise localization mechanisms, which face significant challenges.  First, new (real-time) localization mechanisms are needed to locate neighboring devices/objects with high accuracy under tight environment constraints, e.g. without infrastructure support. Second, mobile networks need to deliver orders of magnitude more bandwidth to support the exponentially increasing traffic demand, and to adapt resource usage to user mobility.  

In this dissertation, I build effective and practical solutions to address these challenges. Specifically, I propose new localization mechanisms that utilize the rich set of sensors on smartphones to implement highly accurate localization systems; develop novel 60GHz outdoor picocells to significantly boost network capacity; and instrument a data-driven approach to measure and extract user mobility at scale.  In this defense, I will present two of my recent works on improving network capacity and capturing user mobility patterns. In the first work, we propose a drastically new outdoor picocell design that leverages millimeter wave 60GHz transmissions to provide multi-Gbps bandwidth for mobile users. Using extensive measurements on off-the-shelf 60GHz radios, we explore the feasibility of 60GHz picocells by characterizing range, attenuation due to reflections, sensitivity to movement and blockage, and interference in typical urban environments. Our results dispel some common myths on 60GHz, and show that 60GHz outdoor picocells are indeed a feasible approach for delivering orders of magnitude increase in network capacity.  In the second work, we explore a new direction on extracting large-scale human mobility traces through geosocial datasets. Specifically, by comparing raw GPS traces against Foursquare checkins, we analyze the value of using geosocial datasets as representative traces of human mobility. We then develop techniques to both “sanitize” and “repopulate” geosocial traces, thus producing detailed mobility traces more indicative of actual human movement and suitable for mobile network design. Finally, I will conclude and discuss future directions.

Everyone Welcome!