On the utility of concave nodes in geometric processing of large-scale sensor networks

MPhil Thesis Defence


Title: "On the utility of concave nodes in geometric processing of
large-scale sensor networks"

By

Mr. Shengkai ZHANG


Abstract

As a sensor network grows large, it may become increasingly complex in 
topology due to its close ties to the surrounding environment. Previous 
work has shown that proper geometric processing of the network (e.g., 
boundary detection and localization) can provide very helpful information 
for applications to optimize their performance. To that end, numerous 
algorithms have been developed, providing a variety of inspiring 
solutions, yet exhibiting an ad hoc style in principle and implementation. 
In this thesis we show that the crux of solving many of the problems 
caused by complex topology is to identify the concave nodes, nodes that 
are located at concave network corners, where the boundary has an inner 
angle greater than π. The knowledge of such nodes makes several important 
tasks, namely geometric embedding, full localization, convex segmentation, 
and boundary detection, relatively easier or perform significantly better, 
as confirmed by simulations. These findings suggest that concave nodes can 
serve as a basic supporting structure for general geometric processing 
tasks and geometry-related applications in sensor networks.


Date:			Monday, 20 October 2014

time:			2:00pm - 4:00pm

Venue:			Room 3501
 			Lifts 25/26

Committee Members:	Dr. Pan Hui (Supervisor)
 			Prof. Bo Li (Supervisor)
 			Dr. Kai Chen (Chairperson)
 			Dr. Lin Gu


**** ALL are Welcome ****