Yanliang Liu

Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Computer Science


Computer Science and Engineering

First Advisor

Yonghe Liu


The swift growth and popularization of wireless technology and mobile smart phones have unfolded various opportunities and challenges before researchers' eyes. Opportunistic networks is one of the interesting and challenging topics. Generally speaking, opportunistic networks exploit the potential capability of existing mobile devices carried by people to provide pervasive computing service, such as data forwarding, without pre-planted infrastructures. The movement of mobile devices introduced by human activity plays a crucial role in the functionality of opportunistic networks, since it influences the contacts between different mobile devices. It is wellknown that human movement presents these place-centered features: intermittent hops between places and stops at places. And people spend most of their time in different places every day, and their movement inside places are more stable than movement between places. The above relatively stable human activity in each place could provide longer contact time and higher contact possibility between mobile devices. Based on this observation, we propose a new opportunistic network scenario named place based opportunistic networks. In this new type of networks, data forwarding is assumed only to take place in each place to cope with above features. The purpose of this work is to discuss and study various topics in the proposed place based opportunistic networks, from basic theoretical understanding of place based opportunistic networks to potential applications operating upon these networks. We mainly focused on the following topics: 1) Localization in Place Based Opportunistic Networks. Localization is a required functionality in place based opportunistic networks. It identifies the location of each mobile devices. We proposed a new localization scheme named COAL that takes advantage of surrounding context information in order to reduce the energy consumed by localization service without losing accuracy. 2) Capacity of Place Based Opportunistic Networks. Capacity is a classic and important topic of every network. It indicates the amount of data could be served by the network. Briefly speaking, we proposed a two-layer model to represent this network and solved a maximum flow problem on this network to obtain capacity. 3) Routing in Place Based Opportunistic Networks. Besides capacity, routing is another important topic in network research field and attracts attention from plenty of researchers. Based on the inherent features of place based opportunistic networks, we designed two routing schemes based on popularity and congestion information separately, and setup experiments to compare the performance of several routing schemes. 4)Application Recommendation System in Place Based Opportunistic Networks. As current high penetration of mobile phones, a huge data pool could be built up based on the data sensed and stored by each mobile phone. We proposed to build up an application recommendation system based on these data pool. Mathematical models have been proposed to relate applications and places as well as quantify the attention reward gained by executing each application. An approximate greedy heuristic algorithm and a dynamic algorithm have been designed to compute application recommendation lists. Both simulation and field study showed the feasibility of our proposed recommendation system.


Computer Sciences | Physical Sciences and Mathematics


Degree granted by The University of Texas at Arlington