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

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19along the right side of the arena.Figure 2.13: Terrain visualization in 3D, overhead viewThe use of the available interactivity emphasizes the usefulness of this type ofvisualization.In this visualization the user has complete control and freedom ofmovement with the camera. Figures 2.14(a) and 2.14(b) show two different perspectivesof portions of the terrain from Figure 2.13.These views are much easier tounderstand and comprehend than a 2d height map visualized under the plot of agraph. The features of the terrain are easily distinguishable and the effects they haveon network connectivity can be easily seen when we zoom into the environment.An example of these effects is more clearly illustrated in Figures 2.14(c) and2.14(d).In Figure 2.14(c) we can see nodes 2 and 3 are hidden behind elements
20(a)(b)(c)(d)Figure 2.14: Figures showing a zoomed in view of portions of 3D terrain with portionsof a physical graph visualized on top of them.of the terrain which may cause trouble in their connections with node 1.We seea similar situation in Figure 2.14(d). These situations are not as easily identifiablewhen visualized in 2D. Although one can zoom into a static 2d image, it is moredifficult to interpret the exact values on a height map and try and draw conclusionsthat way. Seeing these situations in 3D makes it quick and easy to directly see theresult.One of the only troubles we encounter in the 3D view of this data is that whenlooking at the terrain as a whole it is easy for the nodes and edges to get lost in thedetails of the terrain. To alleviate this problem, nodes and edges can be scaled up
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: 18its results. An example of this w
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 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
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