Network correlated data gathering with explicit communication: NP-completeness and algorithms

  • Authors:
  • Razvan Cristescu;Baltasar Beferull-Lozano;Martin Vetterli;Roger Wattenhofer

  • Affiliations:
  • Center for the Mathematics of Information, California Institute of Technology, Pasadena, CA;Laboratory for Audio-Visual Communications (LCAV), Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland;Laboratory for Audio-Visual Communications (LCAV), Swiss Federal Institute of Technology (EPFL), Lausanne Switzerland and Department of Electrical Engineering and Computer Science, University of C ...;Distributed Computing Group, Department of Computer Science, ETH Zurich, Zurich, Switzerland

  • Venue:
  • IEEE/ACM Transactions on Networking (TON)
  • Year:
  • 2006

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Abstract

We consider the problem of correlated data gathering by a network with a sink node and a tree-based communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. For source coding of correlated data, we consider a joint entropy-based coding model with explicit communication where coding is simple and the transmission structure optimization is difficult. We first formulate the optimization problem definition in the general case and then we study further a network setting where the entropy conditioning at nodes does not depend on the amount of side information, but only on its availability. We prove that even in this simple case, the optimization problem is NP-hard. We propose some efficient, scalable, and distributed heuristic approximation algorithms for solving this problem and show by numerical simulations that the total transmission cost can be significantly improved over direct transmission or the shortest path tree. We also present an approximation algorithm that provides a tree transmission structure with total cost within a constant factor from the optimal.