# XTC: A Practical Topology Control Algorithm for Ad-Hoc Networks

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XTC: A Practical Topology Control Algorithm for Ad-Hoc Networks. Roger Wattenhofer Aaron Zollinger. Overview. What is Topology Control? Context – related work XTC algorithm XTC analysis Worst case Average case Conclusions. - PowerPoint PPT Presentation

### Text of XTC: A Practical Topology Control Algorithm for Ad-Hoc Networks

• XTC: A Practical Topology Control Algorithm for Ad-Hoc NetworksRoger WattenhoferAaron Zollinger

• Overview

What is Topology Control?

Context related work

XTC algorithm

XTC analysisWorst caseAverage case

Conclusions

• Topology Control

Drop long-range neighbors: Reduces interference and energy!But still stay connected (or even spanner)Sometimes also clustering, Dominating Set construction Not in this presentation

• Overview

What is Topology Control?

Context related work

XTC algorithm

XTC analysisWorst caseAverage case

Conclusions

• Context Previous Work

Mid-Eighties: randomly distributed nodes [Takagi & Kleinrock 1984, Hou & Li 1986]

Second Wave: constructions from computational geometry, Delaunay Triangulation [Hu 1993], Minimum Spanning Tree [Ramanathan & Rosales-Hain INFOCOM 2000], Gabriel Graph [Rodoplu & Meng J.Sel.Ar.Com 1999]

Cone-Based Topology Control [Wattenhofer et al. INFOCOM 2000]; explicitly prove several properties (energy spanner, sparse graph)

Collecting more and more properties [Li et al. PODC 2001, Jia et al. SPAA 2003, Li et al. INFOCOM 2002] (e.g. local, planar, distance and energy spanner, constant node degree [Wang & Li DIALM-POMC 2003])But: exact node positions knownOnly neighbor direction and relative distance

• K-Neigh (Blough, Leoncini, Resta, Santi @ MobiHoc 2003)

Connect to k closest neighbors!Very simple algorithm.On average as good as others

Tough question: What should k be?[Thanks to P. Santi]

• Percolation

Node density such that the graph is just about to become connected (about 5 nodes per unit disk).

Whats the value for k at percolation?!? (Tough question?)too sparsetoo densecritical density

• K-Neigh and the Worst Case?

What if the network looks like this:

Does a typical/average network (or parts of an average network) really look like this? Probably not butStill, cool simulation and analysis results by Blough et al.For example: energy to compute K-Neigh topology is much smaller than CBTC topology (figure right)

k+1 nodesk+1 nodes

Chart1

3.63211266113.04976735886.24957523371

4.39599796873.49298859818.718083241

4.61920940153.3486145589.23985109631

4.75069695393.26936203979.4774024981

4.95149041023.2296994969.78968253971

5.2614159743.702636858710.39045332951

5.5401694323.685908711210.93382608051

5.59819880793.67690975910.96890829461

5.62191737233.67290142410.96397805051

5.78646021843.668673418411.25064873441

6.00987614853.657636874711.60053041441

6.1814515663.661168927111.81724712281

8.16683518923.633256923613.86665754311

11.64319549463.626659639617.8222825831

14.5845286774.095464257720.84036671141

18.29310682064.094746904325.20087232041

Homogen

K-Neigh

CBTC 2/3

MST

n

Energy cost - Phase 1 only

alpha=2

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