Huaping Shen

Graduation Semester and Year

2005

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Computer Science

Department

Computer Science and Engineering

First Advisor

Sajal Das

Abstract

Along with the technology advancements in mobile and wireless networks, ubiquitous information service is becoming a reality in which users can access the information anytime anywhere. However, the user mobility, network heterogeneity and resource constraints impose significant challenges to provide ubiquitous information services. In this dissertation, a utility based resource aware framework is proposed to enhance ubiquitous information availability to mobile users through data caching and peer-to-peer sharing. The framework considers the constrained resources of mobile and distributed environments and provides flexible, efficient and scalable data access services to the mobile users. The major contributions of this framework are as follows. First, we introduce a novel energy and bandwidth efficient data caching mechanism, called GreedyDual Least Utility (GD-LU), to enhance dynamic data availability to mobile users cellular networks. Based on the utility function derived from a utility based analytical model, we propose an efficient algorithm for cache replacement. Moreover, based on the utility model, we propose a passive prefetching scheme to further reduce the data access latency of each mobile user. Second, we introduce a novel scheme called Energy efficient Peer-to-peer Caching with Optimal Radius (EPCOR) to enable peer-to-peer information sharing in multi-hop hybrid networks. In EPCOR, a peer-to-peer (P2P) overlay network is built among the mobile users to facilitate cooperative sharing of data based on network proximity and data preference. Third, we investigate location-aided information retrieval in large-scale mobile peer-to-peer (MP2P) networks. A novel scheme, called Proximity Regions for Caching in Cooperative MP2P Networks (PReCinCt) is designed to utilize location information to support scalable data retrieval. In the PReCinCt scheme, the network topology is divided into geographical regions where each region is responsible for a set of keys representing the data. Each key is then mapped to a region location based on a geographical hash function. We evaluate and validate the proposed framework both analytically and experimentally. We have conducted extensive experiments using large scale simulations to evaluate the performance of proposed framework. All analytical and experimental results show that the proposed framework can efficiently provide ubiquitous information services in mobile and distributed environments.

Disciplines

Computer Sciences | Physical Sciences and Mathematics

Comments

Degree granted by The University of Texas at Arlington

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