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International Journal of Distributed Sensor Networks
Volume 2012 (2012), Article ID 135054, 9 pages
http://dx.doi.org/10.1155/2012/135054
Research Article

Holes Detection in Anisotropic Sensornets: Topological Methods

1School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
2College of Management Science, Chengdu University of Technology, Chengdu 610059, China
3National School of Software, Xidian University, Xi’an 710071, China
4College of Science, Xi’an University of Science and Technology, Xi’an 710054, China

Received 11 May 2012; Revised 2 September 2012; Accepted 12 September 2012

Academic Editor: Chuan Foh

Copyright © 2012 Wei Wei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Wireless sensor networks (WSNs) are tightly linked with the practical environment in which the sensors are deployed. Sensor positioning is a pivotal part of main location-dependent applications that utilize sensornets. The global topology of the network is important to both sensor network applications and the implementation of networking functionalities. This paper studies the topology discovery with an emphasis on boundary recognition in a sensor network. A large mass of sensor nodes are supposed to scatter in a geometric region, with nearby nodes communicating with each other directly. This paper is thus designed to detect the holes in the topological architecture of sensornets only by connectivity information. Existent edges determination methods hold the high costs as assumptions. Without the help of a large amount of uniformly deployed seed nodes, those schemes fail in anisotropic WSNs with possible holes. To address this issue, we propose a solution, named PPA based on Poincare-Perelman Theorem, to judge whether there are holes in WSNs-monitored areas. Our solution can properly detect holes on the topological surfaces and connect them into meaningful boundary cycles. The judging method has also been rigorously proved to be appropriate for continuous geometric domains as well as discrete domains. Extensive simulations have been shown that the algorithm even enables networks with low density to produce good results.