Author

Jixiang Jiang

Date of Award

8-2009

Level of Access

Open-Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Spatial Information Science and Engineering

Advisor

Michael F. Worboys

Second Committee Member

M. Kate Beard-Tisdale

Third Committee Member

Max J. Egenhofer

Comments

Topological changes to regions, such as merging, splitting, hole formation and elimination, are significant events in the evolution of regions. Wireless sensor network technology, which provides real-time information about the environment, can play an important role in detecting and reporting such topological changes.

This thesis provides theoretical foundations and algorithmic solutions to topological change detection using sensor networks. Two models, the morphism-based model and the local tree model, are developed, providing formal semantics of topological changes. The morphism-based model represents dynamic topological properties of continuously evolving areal objects, in which basic and complex topological changes are represented and classified using trees and structure-preserving mappings between them. Based on this model, this work constructs a normal form and proves that it is the simplest form that could represent all the changes under consideration. The local tree model represents discrete and incremental changes of the areal objects based on selected components and relations between them. It allows us to specify different kinds of topological changes using information within the locality of the change. Based on the local tree model, we develop two decentralized and energy-efficient approaches, the transient group-based (TG-based) and the adaptive group-based (AG-based) approaches, to topological change detection using sensor networks. The TG-based approach employs the boundary group framework, which reduces the communication cost by reporting only the group level data instead of data from each individual node. The AG-based approach further reduces the communication cost by reusing the time-invariant information. Experimental results show that when the configurations of sensor networks satisfy certain density and communication constraints, the proposed approaches are able to generate correct reports on the topological changes, and at the same time reduce the communication cost to a level much lower than that of a basic boundary-based data collection approach.

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