Finding Facilities Fast

  • Authors:
  • Saurav Pandit;Sriram V. Pemmaraju

  • Affiliations:
  • University of Iowa, Iowa City, USA IA 52242-1419;University of Iowa, Iowa City, USA IA 52242-1419

  • Venue:
  • ICDCN '09 Proceedings of the 10th International Conference on Distributed Computing and Networking
  • Year:
  • 2009

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Abstract

Clustering can play a critical role in increasing the performance and lifetime of wireless networks. The facility location problem is a general abstraction of the clustering problem and this paper presents the first constant-factor approximation algorithm for the facility location problem on unit disk graphs (UDGs), a commonly used model for wireless networks. In this version of the problem, connection costs are not metric , i.e., they do not satisfy the triangle inequality, because connecting to a non-neighbor costs ***. In non-metric settings the best approximation algorithms guarantee an O (logn )-factor approximation, but we are able to use structural properties of UDGs to obtain a constant-factor approximation. Our approach combines ideas from the primal-dual algorithm for facility location due to Jain and Vazirani (JACM , 2001) with recent results on the weighted minimum dominating set problem for UDGs (Huang et al., J. Comb. Opt. , 2008). We then show that the facility location problem on UDGs is inherently local and one can solve local subproblems independently and combine the solutions in a simple way to obtain a good solution to the overall problem. This leads to a distributed version of our algorithm in the $\mathcal{LOCAL}$ model that runs in constant rounds and still yields a constant-factor approximation. Even if the UDG is specified without geometry, we are able to combine recent results on maximal independent sets and clique partitioning of UDGs, to obtain an O (logn )-approximation that runs in O (log* n ) rounds.