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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
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Outline Introduction to Compression in Sensor Networks Problem Formulation NP-Completeness Approximation Algorithms Numerical Simulations Conclusion
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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
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Slepian–Wolf coding
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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 paper Find an optimal transmission structure ? (Minimum Cost Data Gathering Tree Problem)
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Problem Formulation (Minimum Cost Data Gathering Tree Problem) Let G(V, E) be a weighted graph, where each edge e i E has a weight w i. Minimum Cost Data Gathering Tree Problem – Given a weighted graph G, find a spanning tree T of G that minimizes
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Assumptions Assume the coding rates of internal nodes are i i No side information with side information R r + R+2r r r R constant
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Assumptions i X i is only correlated with the nearest node X j r + R+2r r r R
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Examples
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Problem Formulation
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Case 1: =0 – Independent data – Shortest path tree Case 2: =1 – Maximal correlated data – K-TSP problem (multiple traveling salesman) NP-hard
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NP-Completeness
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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
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Simulated Annealing
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Heuristic Approximation Algorithms 4.Balanced SPT/TSP Tree 5.Leaves Deletion Approximation 6.Shallow Light Tree (SLT) [2][5] -- A spanning tree that approximates both the MST and TSP for a given node.
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Balanced SPT/TSP Tree
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Optimal Radius
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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.
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Leaves Deletion Algorithm
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Shallow Light Tree (SLT) Given a graph G(V, E) and a positive number The SLT has two properties:
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Numerical Simulations Leaves Deletion(LD) vs. SPT N=200 = 0.9
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Numerical Simulations N=100 = 0.5
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Numerical Simulations N=200 = 0.2 SPT LD SPT/TSP
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Numerical Simulations
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N=100 = 0.8
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Numerical Simulations C SLT / C SPT/TSP
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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.
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