Greedy Routing on Hierarchical Clusters

Greedy geometric routing is one of the most appealing options for sensor and ad-hoc networks due to its simplicity, scalability and mainly stateless operation. However, its performance may vary when a low-dimensional embedding results in a graph with void regions and local minima locations. In this paper, we advocate the benefits of clustering for improving the performance of greedy routing over an existing embedding. Our contributions include: (a) an examination of the effects of clustering on influencing routing near void regions and overcoming problems with local minima, (b) a generic framework for enabling a cluster view for greedy routes, that can work with any low-dimensional embedding, and (c) a quantitative evaluation of the performance improvement we achieve, using some reference clustering approaches, over a number of virtual coordinate systems for geometric routing.