Network Correlated Data Gathering With Explicit Communication : NP-Completeness and Algorithms

Network Correlated Data Gathering With Explicit Communication: NP-

Completeness and Algorithms

R˘azvan Cristescu, Member, IEEE, Baltasar Beferull-Lozano, Member, IEEE, Martin Vetterli, Fellow, IEEE, Roger Wattenhofer

IEEE Transactions on Networking, Feb. 2006

Outline

• Introduction to Compression in Sensor Networks

• Problem Formulation• NP-Completeness• Approximation Algorithms• Numerical Simulations• Conclusion

Introduction

• Independent encoding/decoding• Low coding gain• Optimal transmission structure: Shortest path tree

• Distributed source coding: Slepian–Wolf coding– Allow nodes to use joint coding of correlated data without

explicit communication• Lossless• Assume global network structure and correlation structure• Without explicit communication (Independent encoding)

– Node can exploit data correlation among nodes without explicit communication.

• Optimal transmission structure: Shortest path tree

Slepian–Wolf coding

Introduction

• Encoding with explicit communication– Nodes can exploit the data correlation only when the data

of other nodes is locally at them).– Without knowing the correlation among nodes a priori.

The objective of this paperFind an optimal transmission structure?(Minimum Cost Data Gathering Tree Problem)

Problem Formulation(Minimum Cost Data Gathering Tree Problem)

• Let G(V, E) be a weighted graph, where each edge ei E has a weight wi.

• Minimum Cost Data Gathering Tree Problem– Given a weighted graph G, find a spanning tree T

of G that minimizes

Assumptions

• Assume the coding rates of internal nodes are

i

i

No side information

with side information

R

r + R+2r r

r

R

otherwise link incoming no if

rR

constant

Assumptions

i

Xi is only correlated with the nearest node Xj

r + R+2r r

r

R

)|(),...,,|( 1 jikjji XXHXXXXH

Examples

Problem Formulation

• Case 1: =0– Independent data– Shortest path tree

• Case 2: =1– Maximal correlated data– K-TSP problem (multiple traveling salesman)• NP-hard

NP-Completeness

Heuristic Approximation Algorithms

1. Shortest path tree– If data is near independent, this approach is good.

2. Greedy algorithm– Start from an initial subtree containing only the sink.– Add successively, to the existing subtree, the

node whose addition results in the minimum cost increment.

3. Simulated Annealing– A provably optimal but computationally heavy optimization

method

Simulated Annealing

Heuristic Approximation Algorithms

4. Balanced SPT/TSP Tree5. Leaves Deletion Approximation6. Shallow Light Tree (SLT) [2][5]

-- A spanning tree that approximates both the MST and TSP for a given node.

Balanced SPT/TSP Tree

Optimal Radius

Leaves Deletion Algorithm

• Step 1: construct the global SPT.• Step 2: make the leaf nodes change their parent node to some other leaf node in their neighborhood if this change reduces the total cost.

Leaves Deletion Algorithm

Shallow Light Tree (SLT)

• Given a graph G(V, E) and a positive number The SLT has two properties:

Numerical SimulationsLeaves Deletion(LD) vs. SPT

N=200= 0.9

Numerical Simulations

N=100= 0.5


N=200= 0.2

SPT LD

SPT/TSP


N=100= 0.8


CSLT / CSPT/TSP

Conclusions• This paper formulates the network correlated data

gathering tree problem with coding by explicit communication.

• This paper proved that the minimum cost data gathering tree Problem is NP-hard, even for scenarios with several simplifying assumptions.

• Several approximation algorithms are proposed and shown to have significant gains over the shortest path tree.

Network Correlated Data Gathering With Explicit Communication : NP-Completeness and Algorithms

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