4.8 Simulations in NS-2
4.8.3 Simulation Results
We compare our route cache scheme to simple DSR without a route cache expiration mechanism (T =∞), as shown in Fig. 4.12 and Fig. 4.13, for the Random Waypoint model, and Fig. 4.14 for the Random Walk model. The node density is varied in Fig. 4.12, by using 1000m×1000m and 750m×750m simulation areas, respectively. The mean node speed and traffic load are 10m/s and 20 sources, respecitively, in Fig. 4.12 and Fig. 4.14, and 5m/s and 50 sources, respectively, in Fig. 4.13. In each figure, the performance of standard DSR is indicated by a horizontal line, and it can be seen that the illustrated performance measures approach this value when timeout ≈10R(h) (i.e., γ = 10). If the timeout is chosen too small (e.g., γ = 0.1), performance is much worse than standard DSR, in general.
Effects on End-To-End Delay We first consider the end-to-end delay perfor-
mance measure, which is the latency between the generation of a route request at a source node and the successful receipt of the corresponding data packet at the des- tination node. Note that the end-to-end delay includes the routing delay and other sundry delays, such as, transmission delay, propagation delay, and queuing delay, which widely exist in all types of networks. We employ our route cache scheme to reduce the routing delay, which is of particular concern in ad hoc on-demand routing protocols. Therefore, once the routing delay is minimised, relatively low
4.8 Simulations in NS-2 85 10−1 100 101 0 10 20 30 40 50 60 70 80 90 100
Timeout scaling factor γ
Packet delivery ratio (%)
Random Waypoint, mean v = 10 m/s, 20 sources, 50 MNs in (750m)2 and (1000m)2
Timeout = γ R(h), high node density DSR, high node density
Timeout = γ R(h), low node density DSR, low node density
(a) 10−1 100 101 0 1 2 3 4 5 6 7 8
Timeout scaling factor γ
End−to−end delay (seconds)
Random Waypoint, mean v = 10 m/s, 20 sources, 50 MNs in (750m)2 and (1000m)2 Timeout = γ R(h), high node density DSR, high node density
Timeout = γ R(h), low node density DSR, low node density
(b) 10−1 100 101 0 20 40 60 80 100 120 140
Timeout scaling factor γ
Routing overhead (thousand packets)
Random Waypoint, mean v = 10 m/s, 20 sources, 50 MNs in (750m)2 and (1000m)2
Timeout = γ R(h), high node density DSR, high node density
Timeout = γ R(h), low node density DSR, low node density
(c) 10−1 100 101 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
Timeout scaling factor γ
Average path length (hops)
Random Waypoint, mean v = 10 m/s, 20 sources, 50 MNs in (750m)2 and (1000m)2
Timeout = γ R(h), high node density DSR, high node density
Timeout = γ R(h), low node density DSR, low node density
(d)
Figure 4.12: Comparison of network performance between modified DSR using
γR(h) and the original DSR with no route cache expiration for the Random Way-
point Mobility model. High node density: 50 MNs in 750m×750m; low node
density: 50 MNs in 1000m×1000m. Traffic load: 20 sources. Mean node speed:
86 Choice of Timeout for Route Caching 10−1 100 101 0 10 20 30 40 50 60 70 80 90 100
Timeout scaling factor γ
Packet delivery ratio (%)
Random Waypoint, mean v = 5 m/s, 20 and 50 sources, 50 MNs in (1000m)2
Timeout = γ R(h), 20 sources DSR, 20 sources Timeout = γ R(h), 50 sources DSR, 50 sources (a) 10−1 100 101 0 1 2 3 4 5 6 7 8
Timeout scaling factor γ
End−to−end delay (seconds)
Random Waypoint, mean v = 5 m/s, 20 and 50 sources, 50 MNs in (1000m)2
Timeout = γ R(h), 20 sources DSR, 20 sources Timeout = γ R(h), 50 sources DSR, 50 sources (b) 10−1 100 101 0 20 40 60 80 100 120 140
Timeout scaling factor γ
Routing overhead (thousand packets)
Random Waypoint, mean v = 5 m/s, 20 and 50 sources, 50 MNs in (1000m)2 Timeout = γ R(h), 20 sources DSR, 20 sources Timeout = γ R(h), 50 sources DSR, 50 sources (c) 10−1 100 101 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
Timeout scaling factor γ
Average path length (hops)
Random Waypoint, mean v = 5 m/s, 20 and 50 sources, 50 MNs in (1000m)2
Timeout = γ R(h), 20 sources DSR, 20 sources
Timeout = γ R(h), 50 sources DSR, 50 sources
(d)
Figure 4.13: Comparison of network performance between modified DSR using
γR(h) and the original DSR with no route cache expiration for the Random Way- point Mobility model. The node density: 50 MNs in 1000m×1000m. High traffic load: 50 sources; low traffic load: 20 sources. Mean node speed: 5m/s.
4.8 Simulations in NS-2 87 10−1 100 101 0 10 20 30 40 50 60 70 80 90 100
Timeout scaling factor γ
Packet delivery ratio (%)
Random Walk, mean v = 10 m/s, 20 sources, 50 MNs in (750m)2 and (1000m)2
Timeout = γ E{R(h)}, high node density DSR, high node density
Timeout = γ E{R(h)}, low node density DSR, low node density
(a) 10−1 100 101 0 1 2 3 4 5 6 7 8
Timeout scaling factor γ
End−to−end delay (seconds)
Random Walk, mean v = 10 m/s, 20 sources, 50 MNs in (750m)2 and (1000m)2 Timeout = γ E{R(h)}, high node density DSR, high node density
Timeout = γ E{R(h)}, low node density DSR, low node density
(b) 10−1 100 101 0 20 40 60 80 100 120 140
Timeout scaling factor γ
Routing overhead (thousand packets)
Random Walk, mean v = 10 m/s, 20 sources, 50 MNs in (750m)2 and (1000m)2 Timeout = γ E{R(h)}, high node density DSR, high node density
Timeout = γ E{R(h)}, low node density DSR, low node density
(c) 10−1 100 101 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
Timeout scaling factor γ
Average path length (hops)
Random Walk, mean v = 10 m/s, 20 sources, 50 MNs in (750m)2 and (1000m)2
Timeout = γ E{R(h)}, high node density DSR, high node density
Timeout = γ E{R(h)}, low node density DSR, low node density
(d)
Figure 4.14: Comparison of network performance between modified DSR using
γE{R(h)} and the original DSR with no route cache expiration for the Random
Walk Mobility model. High node density: 50 MNs in 750m×750m; low node
density: 50 MNs in 1000m×1000m. Traffic load: 20 sources. Mean node speed:
88 Choice of Timeout for Route Caching
end-to-end delay can be achieved.
It is important to note that, whenγ = 1, the minimum end-to-end delay can be achieved for various network scenarios, as shown in Fig. 4.12(b), Fig. 4.13(b) and Fig. 4.14(b). And, clearly, the minimum latency is always less than that for DSR with no route cache expiration mechanism. As expected, we also observe that the end-to-end delay is increased if we decrease the node density, increase the mean node speed, or increase the load traffic.
We observe that the delay curves in Fig. 4.13(b) are much flatter and have lower values than the corresponding curves in Fig. 4.12(b), where the mean node speed is higher. This shows that using the (almost) optimal cache timeout is more effective when the network topology changes frequently.
Comparing Fig. 4.12(b) and Fig. 4.14(b), we observe that the minimum end-to- end delay using the Random Waypoint model is less than the delay for the Random Walk model. The difference is due to the deterministic link residual times for the Random Waypoint model and its non-random travelling pattern when long epochs occur.
Effects on Routing Overhead We now examine the effects of choosing a near-
optimal cache timeout value on the amount of routing overhead. The routing overhead is calculated as the number of routing control packets transmitted by the protocol. That is, the routing overhead is calculated with respect to routing control messages, whereas the end-to-end delay is calculated with respect to transmitted and received data packets. As previously mentioned, the routing overhead consists of three types of routing packets: RREQ, RREP and RERR.
Figure 4.12(c), 4.13(c) and 4.14(c) plot the routing overhead for both the Ran- dom Waypoint and Random Walk models. The major difference is that minimum routing overhead is achieved when γ = 1 for the Random Waypoint model, but it is achieved when γ ≈ 2 for the Random Walk mobility model. Again, this difference is because the link residual times of the cached route for the Random Waypoint Model tend to be deterministic, but they are exponentially distributed for the Random Walk model. The simulation results confirm our theoretical results from Section 4.5 that showed that the minimum flooding probability, which causes minimum control overhead, can be achieved by setting the timeout to 2E{R(h)}, if the link residual times of the cached route are not deterministic.
We can also observe that the routing overhead curves generated in Fig. 4.12(c), Fig.4.13(c) and Fig. 4.14(c) are concave, again concurring with our previous theo- retical results. This is because small values of timeout cause an increased number of route requests (i.e., flooding routing packets), but a decreased number of route errors, while large values of timeout cause a decreased number of route requests but an increased number of route errors.
4.9 Conclusions 89
Effects on Other Performance Measures The packet delivery ratio is the
fraction of data packets that are successfully delivered over all data packets sent from the source. It is one of the most important measures used to evaluate the performance of an ad hoc routing protocol. Observing the packet delivery ratios in Fig. 4.12(a), Fig. 4.13(a) and Fig. 4.14(a), we find that the network generally has the highest delivery ratio when γ = 1. Also, due to the deterministic link residual times, much higher packet delivery ratios can be achieved with the Random Waypoint model than with the Random Walk model, as shown in Fig. 4.12(a) and Fig. 4.14(a).
The average path length is shorter for smaller cache timeouts (i.e.,γ is smaller). This is because, with a smaller timeout value, the source node has to execute route discovery more frequently, and the newly found route is basically the shortest path. Therefore, we observe that the average path length increases as the scaling factor
γ increases, as illustrated in Fig.4.12(d), Fig.4.13(d) and Fig. 4.14(d).
4.9
Conclusions
A key to achieving efficient MANET performance with on-demand routing pro- tocols is to use a route cache. Liang and Haas [54] proposed setting the cache timeout to a calculated optimal value, Topt, with respect to minimizing routing delay. Unfortunately Topt is difficult to implement in practice. We have proposed
setting the cache timeout to a mobility metric, the expected path residual time. Numerical and simulation results show that the increase in expected routing delay is always less than 2.5% and most often less than 0.5% using the mobility metric as timeout, rather than Topt.
Further, we have shown that choosing the timeout to minimize routing delay does not necessarily minimize routing overhead, which contravenes the intent of on-demand routing protocols. For exponentially distributed links, practically min- imum routing overhead is achieved for timeout at least twice the expected residual time. We have found the following:
• In order to achieve minimum routing delay for on-demand routing, the mobil- ity metric, expected path residual time, is a near-optimal solution compared with the optimal timeout, Topt. The results reflect those in [54] that show
that choosing E{R(h)} to minimize routing delay is independent of traffic distribution.
• Since the expected path residual time can be simply derived from the mean values of the link residual times along the path, setting the route cache time- out by using the expected path residual time is more practical than using the analyzedTopt, which involves much more complex computation, based on
90 Choice of Timeout for Route Caching
finding the roots of an h-order equation. Moreover, the calculation of Topt
needs the explicit PDF expressions of the links in the given route.
• Empirically, the minimum probability of flooding can be achieved if the time- out is set to at least twice the expected path residual time.
• We derive an expression for the expected average routing overhead, which is a function of number of nodes in the network, cache timeout, route length from source node to destination node, mean link residual time and the arrival rate of the route requests.
• In general, when E{R(h)} < timeout < 2E{R(h)}, a balance of minimum routing delay and overhead is achieved.
The principles obtained in this chapter give insight into the optimal setting of route cache timeout in terms of routing delay and routing overhead. This leads to the conclusion that a suitable tradeoff of timeout exists to compromise between minimum routing latency and number of control overhead packets.
In this chapter we have only considered the random walk and random waypoint mobility models. It is well-recognised that these models, while being mathemati- cally tractable, do not translate well to practical systems. In future work, we will consider more realistic mobility models in order to verify the general applicability and advantages of the caching approach presented in this chapter.
Chapter 5
Choice of Timeout for Link
Caching
Following on from the route cache timeout analysis in Chapter 4, we investigate link cache timeout in this chapter. Compared to route caching, link caching has the potential to utilize route information more efficiently. To remove stale routing in- formation, link caching schemes delete links at some fixed time after they enter the cache. This chapter proposes using either the expected path duration or the link residual time as the link cache timeout. These mobility metrics are theoretically calculated for an appropriate random mobility model. Simulation results in NS-2 show that both of the proposed link caching schemes can improve network perfor- mance in DSR by reducing dropped data packets, latency and routing overhead, with the link residual time scheme out-performing the path duration scheme.