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Figure 3.4. Coverage of Flooding (Grid Deployment, Controlled Drop).
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CHARACTERIZING NWB UNRELIABILITY
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Figure 3.5. Coverage (random deployment, controlled drop, ooding).
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Controlled Drop. Figure 3.5 charts the coverage obtained for ooding in different densities of networks. Similar to the density study done for grid scenarios (Figure 3.4), as the density increases, the overall coverage increases. This is because the redundancy built into the network has increased. Figures 3.6 and 3.7 plot the coverage and overhead for various NWB protocols in a sparse 30-node environment. As the loss rate is increased, the overall coverage of the protocols decreases. This is particularly true for CDS-based approaches, where the loss of one packet in the CDS tree can stop the progression of the NWB.
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Flooding AHBP SBA DCB
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Figure 3.6. Coverage (random deployment, controlled drop, 30 nodes).
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ROBUSTNESS CONTROL FOR NETWORK-WIDE BROADCAST
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Figure 3.7. Overhead (random deployment, controlled drop, 30 nodes).
The coverage of ooding and LBA is very similar. However, LBA optimizes the number of rebroadcasts needed based on location, resulting in a slightly lower overhead. SBA, being a dynamic CDS algorithm, adapts to losses in the network, resulting in coverage near ooding. Furthermore, as the loss rate increases in the network, the number of broadcasts performed by SBA increases as well. This demonstrates the adaptive nature of the SBA protocol. For the static CDS approaches, DCB has a higher coverage level than AHBP. This behavior is expected, as each node is covered twice, as presented earlier. The controlled drop experiments were repeated in a denser, 60-node environment (Figures 3.8 and 3.9). As the network is more dense, the protocols are generally more tolerant to losses in the network, resulting in higher coverage. This is especially true of ooding and LBA. The CDS-based approaches make a sparse network out of a dense network, and therefore they lose some of the built-in redundancy of a dense network. At high loss levels, the static approaches of AHBP and DCB drop off when compared to the dynamic approach of SBA and the ooding algorithms. In terms of overhead, since more nodes are covered per broadcast, the overall normalized overhead tends to be less than that of a sparser network. The gain of using LBA over ooding is larger in a dense network, since fewer nodes rebroadcast per region of the network. Losses Due to Self-Interference. As nodes propagate an NWB packet, there is a chance that the reception of that packet will coincide with the reception of another packet. If both packets are for the same NWB, those losses are considered to be self-interference. To measure levels of self-interference, the studies from above were repeated with the Two-Ray Ground Propagation Model, with no controlled drop. All resulting losses were due to collisions with other NWB data packets.
CHARACTERIZING NWB UNRELIABILITY
0.8 Coverage
Flooding Location Based AHBP SBA DCB
Drop Probability
Figure 3.8. Coverage (random deployment, controlled drop, 60 nodes).
Examining Table 3.1, it can be seen that the ooding-based approaches have a higher level of self-interference than the neighbor knowledge protocols. This is as expected, since the neighbor knowledge protocols attempt to build a smaller set of rebroadcasting nodes. In addition, the dynamic approach of SBA has a higher level of self-interference, due to its higher overhead. AHBP, SBA, and DCB also all try to randomize when a packet is retransmitted by introducing some jitter. The jitter algorithm used in DCB is signi cantly more randomized than AHBP and SBA, because very little self-contention exists in the protocol.
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Flooding Location Based AHBP SBA DCB
Overhead
Drop Probability
Figure 3.9. Overhead (random deployment, controlled drop, 60 nodes).
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TABLE 3.1. Average Number of Packet Drops per NWB Due to Collisions Protocol Flooding Location-based AHBP SBA DCB 30 Nodes 12.021 7.24 1.151 1.427 0.118 60 Nodes 87.569 69.228 10.065 24.961 0.82
3.5.4 Cluster Topologies Cluster topologies are networks that have groupings of nodes in speci c areas, rather than being randomly scattered throughout the network. A cluster scenario was studied with four clusters equidistant from each other, connected by a cluster of four nodes. The resulting shape was that of a plus. Controlled Drop. Figure 3.10 plots the coverage achieved by the different protocols in the cluster scenarios. The results roughly mirror the results of the random topologies, with ooding achieving the highest level of coverage. Figure 3.11 plots the overhead of the different protocols. The results of the cluster experiments have lines that are not as smooth, because a loss of a NWB transmission has a high chance of preventing the rest of the network from receiving the broadcast. The results are somewhat similar to the results found in the 30-node random deployment environment (Figures 3.6 and 3.7). This is because a 30-node environment in a 1000-m2 area is very sparse and loosely connected. The loss of a transmission along one of the links