Algorithms for the visualization and simulation of mobile ad hoc and ...

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39Eccentricity.The Eccentricity metric provides some interesting results. This metricdepends on the underlying connectivity of the original graph. We can observe thisby looking at the weight-range-to-node-count ratios. For both graphs this ratio weextremely low, 0.00088 : 1 for the inet graph and 0.00878 : 1 for the citation graph.The actual range of weight values for the inet graph was from 0 to 4. This meansthat the longest shortest path within this graph is length 4. This implies one of twothings, that the graph is very well connected or it reveals a radial graph structurethat flows into a few central nodes.Figures 3.6 show an example of the visual result generated by applying the Eccentricitymetric on the Inet graph. Figures 3.7 show the result of applying this metricto the Citation graph.The distribution of these weight values is also highly dependent on the underlyingstructure of the graph.We see that for the citation graph this metric provides arelatively even drop off rate while the Inet graph has a very sharp drop off over themiddle weight values in its range.Shortest Distance to Center Node.Since this metric is based on the graph’seccentricity, it displays many of the same properties as the Eccentricity metric. Itmaintains very low weight-range-to-node-count ratios, 0.0011 : 1 and 0.0087 : 1 forthe Inet and citation graphs respectively, and the distribution is again dependent onthe structure of the graph being processed.Figures 3.8 show an example of the visual result generated by applying the ShortestDistance to Center Node metric on the Inet graph. Figures 3.9 show the result ofapplying this metric to the Citation graph.While this metric and Eccentricity have some similarities, there are some differencesin the resulting weights. In the Eccentricity metric a node relatively close to acenter node may have a lower metric value because of the variety of paths available
40(a)(b)(c)(d)Figure 3.6: Inet graph with 4500 Nodes: Eccentricity metric with threshold values(a)7, (b)8, (c)10, (d)12.to reach all other nodes. While that same node may have a high Center Node metricvalue simply because it is near a center node. In a network analogy a given node maynot need to reach the edges of the network directly. Instead it connects to a morecentrally located node which handles the distribution. In this case the Distance toCenter metric will capture this property.Shortest Path to Leaf Node.The Shortest Path to a Leaf Node metric is anotherthat is highly dependent on the topological structure of the original graph. Firstly, itassumes that the given graph does contain leaf nodes. If it does not then this metric
Page 2 and 3: c○ Copyright 2009Daniel Hennessey
Page 4 and 5: iiiTable of ContentsList of Tables
Page 6 and 7: vList of Tables3.1 This table lists
Page 8 and 9: vii2.12 Figures showing OMAN networ
Page 10: ixAbstractAlgorithms for the Visual
Page 13 and 14: 2provement of the simulations. Chap
Page 15 and 16: 4on the clarity, quality, and richn
Page 17 and 18: 6(a)(b)Figure 2.1: Signal radiation
Page 19 and 20: 8(a)(b)Figure 2.3: Acceptable SINR
Page 21 and 22: 10Terrain ClassThe Terrain class is
Page 23 and 24: 12hull operation. In Figure 2.7 the
Page 25 and 26: 14TerrainResolutionChanger ClassDif
Page 27 and 28: 16objects are collapsed together in
Page 29 and 30: 18its results. An example of this w
Page 31 and 32: 20(a)(b)(c)(d)Figure 2.14: Figures
Page 33 and 34: 22library that was implemented in s
Page 35 and 36: 24connected at the outset, we ensur
Page 37 and 38: 26types of graph data. Our goal is
Page 39 and 40: 28metric is similar to the shortest
Page 41 and 42: 30cause the graph to fragment. In o
Page 43 and 44: 323.5 Metric Performance CriteriaOn
Page 45 and 46: # Nodes200 600 1000 Citation 800 12
Page 47 and 48: 36(a)(b)(c)(d)Figure 3.3: Citation
Page 49: 38(a)(b)(c)(d)Figure 3.5: Citation
Page 53 and 54: 42(a)(b)(c)(d)Figure 3.8: Inet grap
Page 55 and 56: 44(a)(b)(c)(d)Figure 3.10: Inet gra
Page 57 and 58: 46(a)(b)(c)(d)Figure 3.12: Inet gra
Page 59 and 60: 48sensing algorithms.This chapter i
Page 61 and 62: 50Figure 4.1: Basic diagrams compar
Page 63 and 64: 52Figure 4.3: Graph showing a compa
Page 65 and 66: 54part to the lack of a capable hig
Page 67 and 68: 56in [44]. This method breaks the s
Page 69 and 70: 58L ≤ N ′ /4 in order to provid
Page 71 and 72: 604.5.2 Test DataIn testing the imp
Page 73 and 74: 62MatlabM 512 1024 2048 409625% Ove
Page 75 and 76: 644.6.2 FFT Accumulation MethodThe
Page 77 and 78: 66CUDAN 1024 2048 4096 8192Memory T
Page 79 and 80: 684.7 ConclusionThe computational c
Page 81 and 82: 70Bibliography[1] Ravindra K. Ahuja
Page 83 and 84: 72[25] M.Y. Kao, N. Occhiogrosso, a

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