Simulation Environment

DIS_RAD was implemented using an NS-2 simulator. To ensure that the simulation results were comparable to others, the simulation environment was modeled on that in [1]. The simulation network contained 100 nodes placed randomly in a map of L×L units, where a unit is the length of communication radius set to 250 meters. The random waypoint model for mobility patterns was adopted. The topologies were generated randomly by the “setdest” program supported by NS-2.

The moving speed was randomly distributed from 0 to 20 (m/s), and the pause time was set to 0. Each node in the network made a broadcast request exactly once during the 200 seconds of the simulation period. The MAC layer was constructed using the IEEE 802.11 standard, which is implemented in NS-2. The simulation results were averaged by the results of 15 simulation runs. The following two performance metrics were considered:

．RE – the percentage of nodes which can be REached as compared with blind flooding.

．SRB – the percentage of Saved ReBroadcasts as compared with blind flooding.

3.2 results of DISCOUNT and DISCOUNT-RS schemes

Figure 10 shows the performance of RE and SRB when the counter threshold ICth equals 2. We can observe that the performance of RE can only be satisfied (larger than 90%) in dense networks such as 3x3 and 5x5 maps. This is because a small number of rebroadcasts from border nodes are enough to cover the most part of the network area.

When the network density becomes low, the nodes which are critical in maintaining a good RE are easily suppressed if the counter threshold is not big enough. Thus, we

can observe that the RE performance is unacceptable under 7x7 and 9x9 maps.

However, when the counter threshold ICth is increased to 3 and 4, as shown in Figure 11 and 12, it is obvious that the overall performance of RE is relatively satisfactory.

Even when the networks are sparse, we also have about 90% of RE when ICth is 3.

When the ICth increases to 4, the RE is almost perfect in most cases; however, we can observe the performance degradation of SRB is about 10% when the ICth increases from 3 to 4. It is straightforward that the smaller the ICth is, the better the SRB will be.

However, it is important to keep both RE and SRB in acceptable values. To keep good balance between RE and SRB, we recommend that Dth should be set around 200 (80%

of transmission radius) and the ICth should be set to 3, where the optimal balance of RE and SRB performance can be observed. We can also observe that if Dth is set to a small value, RE decreases sharply. It is reasonable that if Dth is set too low (say smaller than 72 meters), the EAC of the border node may not be large enough to cause our algorithm to work almost the same as Counter-Based scheme. It’s obvious that the DISCOUNT will be degenerated into a Counter-Based scheme when Dth is set to 250. From Figures 10, 11 and 12, we can find that DISCOUNT provides much better performance in RE in comparison with Counter-Based scheme. The detail comparison between Counter-Based scheme and DISCOUNT will be described later.

From the figures above, we can observe that when the networks are dense, it is easy to keep the RE high. When the networks are sparse, we cannot keep a good RE in all situations. Therefore, it is necessary to give a more detailed analysis under a sparse network. As shown in Figure 13, we compare the Counter-Based scheme with DISCOUNT scheme under 7x7 and 9x9 maps. The Dth is set to 225. SRB7x7C denotes the SRB under 7x7 map of Counter-Based scheme, and SRB7x7DC denotes the SRB under 7x7 map of DISCOUNT scheme. In an attempt to get the RE higher than 90%, Counter-Based scheme should adjust Cth to 4 or 5 for 7x7 map and 5 or 6

Figure 10.The performance of DISCOUNT scheme with Cth = 2

Figure 11.The performance of DISCOUNT scheme with Cth = 3

Figure 12.The performance of DISCOUNT scheme with Cth = 4

for 9x9 map respectively. However, when Cth increases to 5, the SRB of Counter-Based scheme is only about 11%. When Cth is 6, the SRB of Counter-Based scheme remains even less than 5%. On the contrary, DISCOUNT scheme can keep at least 90% of RE in both network topologies with Cth equals to 3. It also provides a much better SRB when compared with Counter-Based scheme. The SRB is 30% and 21% when Cth is 3 under 7x7 and 9x9 maps respectively. It is very clear that DISCOUNT outperforms Counter-Based scheme. We can also observe that DISCOUNT is not sensitive to the value of Cth. In Counter-Based scheme, one way to keep a good balance between RE and SRB for different network densities is to adjust

th

C dynamically [4]; however, it is not practical since the network topology is not predictable. DISCOUNT maintains stable performance of RE under fix C .

As shown in Figure 14, we compare the Distance-Based scheme with DISCOUNT scheme under 7x7 and 9x9 maps. The C of DISCOUNT scheme is set to 3.

SRB7x7D denotes the SRB under 7x7 map of Distance-Based scheme, and SRB7x7DC denotes the SRB under 7x7 map of DISCOUNT scheme. In an attempt to get the RE higher than 90%, Distance-Based scheme should adjust D to less than 72 meters for 7x7 map. While under 9x9 map, the RE cannot reach the 90% in all conditions. However, when D is set to less than 72 meters, the SRB of Distance-Based scheme is only about 17%. On the contrary, DISCOUNT scheme can keep at least 90% of RE in both network topologies with D about 225 meters. It also provides about 30% SRB under 7x7 map. It is obvious that DISCOUNT outperforms Distance-Based scheme. Figure 15 declares the reason why DISCOUNT scheme performs better than Distance-Based Scheme. PD denotes the rebroadcasts that caused by pure Distance-Based scheme and PC denotes the rebroadcasts that transmitted by the effect of counter threshold. When D is set to 225 meters, only 18% of rebroadcasts are transmitted by distance concept, and the other 82% of rebroadcasts is

provided by counter concept. As we discussed before, the distance concept provides good EAC, and counter concept ensure critical nodes have the chance to rebroadcast.

Figure 16 shows the performance of DISCOUNT-RS scheme with Cth = 3. It shows that when Dth is bigger than 147 in 3x3 map, the SRB of DISCOUNT-RS achieves 75% which is higher than original DISCOUNT scheme in the same condition about 7%. DISCOUNT-RS achieves 10% higher in 5x5 and 7x7 maps, and almost 20% in 9x9 map. By the way, DISCOUNT-RS keeps good RE (larger than 90%) in 3x3, 5x5, and 7x7 maps but degrades rapidly in 9x9 map. When Dth is smaller than 175, we have only about 85% of RE which is not able to satisfy our request (at least 90%) but if Dth is set smaller than 175, the SRB is too low. As we said in the last section, DISCOUNT-RS is proposed to save more rebroadcast but may cause some digression to RE. From the simulation results, we find that DISCOUNT-RS keeps good performance when topology is not extremely sparse. It keeps almost the same RE but gets a higher SRB in these situations. Therefore, we will give a more detailed analysis under these situations.

As shown in Figure 17, we compare the DISCOUNT-RS with the DISCOUNT scheme under 3x3, 5x5, and 7x7 maps of different Cth with Dth=225. It shows that DISCOUNT-RS keeps almost the same RE comparing with original DISCOUNT scheme. The difference of SRB between DISCOUNT-RS and DISCOUNT scheme, however, grows with the increasing of Cth. When Cth =2, the difference is only 2%. As Cth =4, the difference grows to 9% in all maps. The results make us believe that DISCOUNT-RS is able to replace DISCOUNT scheme when the network topology is not extremely sparse.

Figure 13. The performance comparison between counter-based and DISCOUNT scheme with Dth = 225

Figure 14. The performance comparison between distance-based and DISCOUNT scheme with Cth = 3

Figure 15. The proportion that rebroadcast protects by counter with Cth = 3 in 7x7map

Figure 16. The performance of DISCOUNT-RS scheme with Cth = 3

Figure 17. The performance comparison between DISCOUNT and DISCOUNT-RS scheme with Dth = 225

3.3 results of DIS_RAD scheme

Figure 18 shows the performance of RE and SRB when the counter threshold Cth

= 2. The figure shows that the RE only performs well (larger than 95%) in dense networks such as 3×3 and 5×5 maps, since a small number of rebroadcasts from border nodes are sufficient to cover most of the network area. When the network density becomes low, the nodes, which are vital to maintaining a good RE, are easily suppressed if the counter threshold is not big enough. Hence, we can observe that the RE performance was found to be unacceptable under 7×7 and 9×9 maps.

However, when the counter threshold Cth was increased to 3 and 4, as shown in Figs. 19 and 20, the overall performance of RE was satisfactory. Even when the networks were sparse, RE was around 90% when Cth = 3. When Cth was increased to 4, the RE was almost perfect in most cases, but the performance degradation of SRB was about 10% when Cth increased from 3 to 4. Clearly a smaller Cth leads to a better SRB. However, both RE and SRB have to be kept in an acceptable range. To maintain

a good balance between RE and SRB, Dth should be set around 200 (80% of the transmission radius), and Cth should be set to 3, where balance between RE and SRB was found to be optimal. Because, if Dth is set to a small value, RE decreases sharply.

Hence, Dth is set too low (e.g., smaller than 100), then the EAC of the border node may not be sufficiently large for the proposed algorithm to work well. The extreme case is when Dth = 0. On the other hand, even a large Dth can yield a high EAC, and hence a high RE. However, when Dth is too large (e.g. greater than 200), the probability that a node is located in the border annulus is also small, especially in sparse networks. In other words, the number of nodes with good EAC values is too low to improve the performance. The RE decreases when Dth is set too high. The extreme case is when Dth = 250. Clearly, DIS_RAD is degenerated into a counter-based scheme when Dth is set to 0 and 250. Figures 18, 19 and 20 demonstrate that DIS_RAD always provides much better performance in RE than the counter-based scheme, and in some situations yields a better SRB. The counter-based scheme and DIS_RAD are compared in detail later.

The analysis above shows that RE remains high in dense networks. However, a good RE cannot always be maintained in a sparse network. Therefore, a detailed analysis is necessary for sparse networks. As shown in Figure 21, the counter-based scheme was compared with DIS_RAD for 7×7 and 9×9 maps, with Dth set to 200. The term SRB7×7C denotes the SRB using a 7×7 map in the counter-based scheme, and SRB7*7DR represents the SRB using a 7×7 map in DIS_RAD. To increase RE above 95%, a counter-based scheme should alter Cth to 4 or 5 for a 7×7 map, and 5 or 6 for a 9×9 map, respectively. However, when Cth increases to 5, the SRB of counter-based scheme is only about 11%. When Cth = 6, the SRB in the counter-based scheme remains below 5%. By contrast, DIS_RAD can keep RE above 95% of RE in both

network topologies when Cth = 3, and yields a much better SRB than does the counter-based scheme. The SRB is 38% and 27% when Cth is 3 under 7×7 and 9×9 maps respectively. Clearly, DIS_RAD outperforms the counter-based scheme, and is not sensitive to the value of Cth. In the counter-based scheme, a good RE for different network densities can be maintained by adjusting Cth dynamically [4]; however, this approach is not practical since the network topology is not predictable. DIS_RAD maintains stable performance of RE under a fixed Cth.

To verify the analytical model of DIS_RAD rebroadcast probability, Figs. 6, 7, 8, and 9 were compared with rebroadcast probability obtained from the simulation results in Figs. 22, 23, 24 and 25. The X-axis represents the value of Dth, and the Y-axis represents the probability of rebroadcasting derived through simulations. The values of Pb are computed from the number of rebroadcasts made by the border nodes over all new distinct rebroadcast messages received by border nodes during the entire simulation period, and the values of Pi are computed in the same way. The parameter P_c represents the value of rebroadcast probability of the counter-based scheme. The probability trends from the simulation results were found to be similar to those from the analysis results. Pb approximates Pc when Dth approaches 0, and Pi approximates Pc when Dth is set to 250 (the value of R). In the simulation, the value of Pb does not exist when Dth is set to 250, because no border nodes exist. The same condition occurs with Pi when Dth = 0, because no interior nodes exist. Significantly, the analytical probability curves are lower than the simulation probability curves. This finding would be expected because the analysis did not consider the impact of the MAC layer.

Therefore, packet collision, contention and delay could prevent some packets from reaching the network layer before a node’s RAD expires. Consequently, the analytical rebroadcast probability may be too conservative. The same augment has been made in [7] for the counter-based scheme analytical rebroadcast probability (Pc).

As mentioned in section III, DIS_RAD separates the LRAD from the SRAD, where the LRAD is always longer than the SRAD. When the LRAD and SRAD are initiated, they fall into two different non-overlapping time slot ranges. The performance impacts on different ratio of the LRAD and SRAD ranges are discussed as follows. In previous simulations, equal numbers of time slots were set for both LRAD and SRAD ranges (ratio = 1). The RE and SBR performance were compared for three different ratios of LRAD to SRAD ranges, 0.5, 1 and 2. As shown in Fig. 26, the REs were almost the same for all ratios, but SRBs were slightly different, particularly for small Dth. At a ratio of 2, the SRB degraded by about 4−5%

comparing a ratio of 1. Conversely, when the ratio was 0.5, the SBR improved by about 2–3% compared with a ratio of 1, because if the range of SRAD is too small, a node-assigned SRAD does not have enough time to collect sufficient neighbor information before making a rebroadcast decision, causing some unnecessary rebroadcasts to be sent. This performance impact can also be observed in Fig. 27, which plots the rebroadcast probabilities of border and interior nodes against the distance threshold Dth. The rebroadcast probability Pb when ratio = 2 is much higher than that when ratio = 1. This finding confirms the SBR performance degradation when the ratio is greater than 1. Similarly, the highest value of Pi occurs when the ratio is 0.5. However, as shown in Fig. 27, the difference between values of Pi under different ratios of LRAD and SRAD ranges is smaller than the difference between values of Pb. This finding is reasonable because most interior nodes are suppressed by border nodes when SRADs expire. Therefore, the performance influences from the interior nodes are minor compared with effects from the border nodes.

Figure 18. RE and SRB vs. Dth with Cth = 2

Figure 19. RE and SRB vs. Dth with Cth = 3

Figure 20. RE and SRB vs. Dth with Cth = 4

Figure 21. RE and SRB vs. Cth with Dth = 200

Figure 22. Pb, Pi and Pc obtained from simulation vs. Dth with Cth=3 in 3×3 map

Figure 23. Pb, Pi, and Pc obtained from simulation vs. Dth with Cth=3 in 5×5 map

Figure 24. Pb, Pi and Pc obtained from simulation vs. Dth with Cth=3 in 7×7 map

Figure 25. Pb, Pi and Pc obtained from simulation vs. Dth with Cth=3 in 9×9 map

Figure 26. RE and SRB vs. Dth with different ratios of time slots in LRAD and SRAD ranges under Cth=3 in 7×7 map

Figure 27. Pb, Pi and Pc obtained from simulation vs. Dth under different ratios of LRAD and SRAD ranges with Cth=3 in 7×7 map

Chapter 4: Conclusions and Future Works

This study proposed a distributed approach to resolve the broadcast storm problem. The counter-based scheme does not consider the locations of the nodes in the network. The proposed scheme addresses the distance concept by adding a Dth

threshold to distinguish the interior circle from the border annulus. Border nodes, which have higher EAC, determine whether to rebroadcast prior to interior nodes.

Nodes with higher EAC values are not suppressed by nodes with lower EAC values thus maintaining a high coverage. The number of rebroadcasts can also be minimized, since the interior nodes may be blocked by border nodes. The simulation results in Fig.

12 shows that when RE reaches 95%, DIS_RAD improved the SRB from 23% in the counter-based scheme to 37.5% for the 7×7 map, and from 3.9% in the counter-based scheme to 26% for the 9×9 map. Additionally, the proposed algorithm is easy to implement, and has some advantages applying to all network topologies can be observed. When Dth is set to about 200 meters, and the counter threshold is set to 3, the proposed scheme can keep good balance between reachability and rebroadcast efficiency in various network densities. However, the counter-based scheme assumes that the counter threshold can be adjusted dynamically to guarantee a good RE performance. Conversely, the proposed scheme is not sensitive to network topologies.

This feature is likely to be essential for real world network implementations.

Reference:

[1] S-Y Ni, Y-C Tseng, Y-S Chen, and J-P Sheu,“The Broadcast Storm Problem in a Mobile Ad Hoc Network”, IEEE MobiCom, 1999

[2] H. Lim and C. Kim. “Multicast tree construction and flooding in wireless ad hoc networks”. In Proceedings of the ACM International Workshop on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWIM), 2000

[3] Chunhui Zhu, Myung J. Lee and Tarek Saadawi,“A border-aware Broadcast Scheme for Wireless Ad Hoc Network”, Mobile and Wireless Communications Network, pp.125–129, 2002

[4] Y-C Tseng, S-Y Ni and E-Y Shin,“Adaptive Approaches to Relieving Broadcast Storms in a Wireless Multihop Mobile Ad Hoc Network”, IEEE Transaction on Computers, Vol.52, NO.5, May 2003

[5] B. Williams and T. Camp,“Comparison of broadcasting techniques for mobile ad hoc networks”, in Proceedings of the Third ACM International Symposium on Mobile Ad Hoc Networking & Computing, pp. 194–205, 2002

[6] Xiaohu Chen, Faloutsos, M. Krishnamurthy, S., “Distance ADaptive (DAD) broadcasting for ad hoc networks”, IEEE MILCOM, Volume: 2, Pages:879 – 883, Oct. 2002

[7] Brad Williams, Dinesh P. Mehta, Tracy Camp, William Navidi, “Predictive Models to Rebroadcast in Mobile Ad Hoc Networks”, Mobile Computing, IEEE Transactions, Volume: 3, Issue: 3, pp. 295 – 303, July 2004

[8] Min-Te Sun, Wuchi Feng, Ten-Hwang Lai, “Location aided broadcast in wireless ad hoc networks”, IEEE GLOBECOM, pp. 2842 - 2846 vol.5, Nov. 2001

[9] K. Fall and K. Varadhan, “ns Notes and Documents,” The VINT Project. UC Berkeley, LBL, USC/ISI, and Xerox PARC, February 2000, Available at http://www.isi.edu/nsnam/ns/ns-documentation.html.

[10] W. Peng and X. Lu, “AHBP: An efficient broadcast protocol for mobile ad hoc networks. Journal of Science and Technology”, Beijing, China, 2002

[11] N. Nahata, P. Pamu, S. Garg, A. Helmy, “Efficient Resource Discovery for Large-Scale Ad hoc Networks Using Contacts”, ACM SIGCOMM Computer Communication Review, Volume 32, Issue 3, pp. 32 - 32, 2002

[12] Y.-J. Yi, M. Gerla, T.-J. Kwon, “Efficient Flooding in Ad hoc Networks: A Comparative Performance Study”, IEEE ICC, Volume 2, pp. 11-15, May 2003 [13] S.-J. Kim, W.-J. Kim, Y.-J. Suh, “Efficient Broadcast Schemes with

Transmission power control in mobile ad hoc networks”, IEEE ICC, Symposium 9, 2004

[14] A. Helmy, “TRANSFER: Transaction Routing for Ad hoc NetworkS with

[14] A. Helmy, “TRANSFER: Transaction Routing for Ad hoc NetworkS with