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Figure 3.10. Coverage (cluster deployment, controlled drop).
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Figure 3.11. Overhead (cluster deployment, controlled drop).
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has a high probability of terminating the NWB propagation. As the network density increases, this probability is lowered. By the same logic, if the clusters were made larger, with more nodes, the coverage results would increase proportionally. 3.5.5 Mobile Topologies This section presents the coverage and overhead studies for various NWB protocols in a few different mobile environments. Topologies containing nodes within a 1000-m by 1000-m area were used for these studies. Random Waypoint. The Random Waypoint Mobility Model is a model commonly used in MANET research. In this mobility model, nodes move in varying directions and speeds, with a pause time between each movement and direction change. Figure 3.12 shows the tests using the random waypoint model with a pause time of 1 s between movements and a maximum speed of 15 m per second. This gure represents the coverage achieved for different NWB protocols. Similar to the static scenarios, ooding and LBA do better than the neighbor knowledge protocols. This behavior is noticed even more in a mobile environment, because the mobility causes neighbor knowledge to become stale. When Figure 3.12 is contrasted with the static 30-node results shown previously in Figure 3.6, the staleness of the neighbor knowledge data can easily be seen. Figure 3.13 shows the overhead of the protocols in a 30-node environment. The trends seen here are similar to those seen in the static scenarios. Figures 3.14 and 3.15 show the coverage and overhead obtained in a 60-node environment, using the random waypoint model (1-s pause time, 15 m/s maximum
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Figure 3.12. Coverage (RW, controlled drop, 30 nodes).
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movement). These results again show that dense networks are more robust and resilient to losses. The overall coverage increases, and the overhead goes down, as more nodes are reached with the broadcasts that are sent. 3.5.6 Probabilistic Random Walk The probabilistic version of the random walk model uses a set of probabilities to determine the next position of a node. This model also uses random directions and speeds, but makes use of a matrix in order to determine the node position for the next
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Figure 3.13. Overhead (RW, controlled drop, 30 nodes).
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Figure 3.14. Coverage (RW, controlled drop, 60 nodes).
timeslot. Since the movements are probabilistic, the movements of nodes tend to be more realistic than purely random movements [29]. An interval of 0.5 was chosen for these studies. This mobility model was chosen for study since there have been studies that have shown that the random waypoint mobility model is not always an accurate representation of mobility [30]. Their studies have shown that the general speed of the network slows over time. Bettstetter and Wagner [31] have also shown that the nodes tend to converge on the center of the network over time, when using the random waypoint model.
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Figure 3.15. Overhead (RW, controlled drop, 60 nodes).
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Figure 3.16. Coverage (PRW, controlled drop, 30 nodes).
Figure 3.16 shows the coverage obtained with the probabilistic random walk in a 30-node environment. The dropoff is much sharper, when compared with the random waypoint graphs presented earlier (Figure 3.12). This is due to the effects mentioned earlier namely, that the random waypoint mobility model tends to slow down over time and converge toward the middle. As the network converges, and the neighbor information data is less likely to be out of date, the redundancy of the network is increased. Figure 3.17 shows the overhead for the probabilistic random walk in a 30-node environment. Again, overall overhead is higher with this model, when compared with the random waypoint data (Figure 3.13).
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