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

More documents

Recommendations

Info

33Citation GraphInet4500Metric Weight Range Ratio Weight Range Ratio1-Ring 1 to 75 0.0721 1 to 942 0.2093num-short 2 to 380 0.3687 2 to 3585 0.796673-Ring 3 to 763 0.7414 3 to 4432 0.9842Shortest Path 0 to 85284 83.2039 0 to 12926842 2872.6315Dist to Leaf 0 to 7 0.0068 0 to 4 0.0008Dist to Center 1 to 10 0.0087 1 to 6 0.0011Eccentricity 9 to 17 0.0087 5 to 9 0.0008Table 3.1: This table lists the Weight Range to # of Nodes ratios for each metric andeach graph they were run against.generated by taking the range of the weight values of each metric on a given graphand dividing it into 18 equal increments. The simplification process was then run ateach increment, and the number of nodes remaining after the simplification was thenrecorded.N-ring Neighbors.For N-Ring neighbors we implemented three variations of themetric. We tested one, two, and three ring neighbors as metrics. Overall the N-ringneighbors metric generates reasonable weight ranges. A comparison of the weightrange-to-node-countratios for each of the three variations can be seen in Table 3.1.We can see that as we increase N, the ratios approach 1 : 1. These metrics of courseare dependent on the connectivity of the original graph; but since we are primarilyfocusing on large complex graphs, these metrics provide good ranges for these typesof graphs.The node drop off rates for these metrics also showed a similar relationship as Nwas increased. In Figure 3.1 the three N-Ring metrics start off with a quick dropoff at the beginning of the weight range and as N increases, the node drop off ratebecomes approximately linear. This indicates that the distribution of weight values
# Nodes200 600 1000 Citation 800 1200 Graph: Node Drop Off Rates 1-Ring 2-Ring Shortest Path 3-RingLeaf Dist to Center Eccentricity Iteration 16 14 12 10 8 6 4 2 0 400034Figure 3.1: Two graphs showing the node drop off rate of each metric against theCitation graph [7] (top) and an Inet graph (bottom). The min and max weight weredetermined for each metric on the particular graph. This range was then divided intoequal increments and the simplification process was run at each increment with thethreshold value being equal to each increment. The number of nodes remaining ateach increment was recorded.1500 2500 3500 4500 3000 5000 4000 12 14 16 18 20 # Nodes Inet4500: Node Drop Off Rate 1-Ring 2-Ring 3-RingIteration Shortest PathLeaf Dist to Center Eccentricity 10 8 6 4 2 0 2000 10000
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: 323.5 Metric Performance CriteriaOn
Page 47 and 48: 36(a)(b)(c)(d)Figure 3.3: Citation
Page 49 and 50: 38(a)(b)(c)(d)Figure 3.5: Citation
Page 51 and 52: 40(a)(b)(c)(d)Figure 3.6: Inet grap
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

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

Create successful ePaper yourself

Delete template?

Save as template?