# Routing Protocols for Delay Tolerant Networks:
## 3 Performance Evaluation
### Impact of the Mobility Model on Performance  
Mobility models clearly affect nodes' opportunity to meet other nodes, namely the probability to reach the destination and the inter-contact probability. Consequently, also messages' likelihood to be delivered before being dropped depends on mobility models. In this section, we evaluate the impact of the mobility models on some of the protocols we discussed in the previous sections. Moreover, the relevance of the information exchanged between nodes to update their knowledge on the environment in many cases strongly depends on mobility models. As an example, social information in RWP is useless, while the same information in the CM is used to predict the expected movement pattern of nodes. Figures 11 and 12 show the effects of different mobility models on the delivery ratio and delay of some of the protocols discussed earlier. When the movement pattern is controlled by some intentional behavior (as in LW and CM), protocols using the knowledge (e.g., MaxProp) perform better, and in any case, the gap between such protocols and the ones that do not use the knowledge is remarkably high. When random movement patterns (as in RW, RWP) are considered, this gap becomes less evident, due to the fact that the knowledge acquired by nodes can be hardly used to predict future behaviors. Surprisingly, the delivery ratio of knowledge-based protocols is higher in the experiments based on random mobility models. As an example, the delivery ratio of MaxProp when 10 nodes are considered is more than 98% in Figure 4, while it is about 92% in Figure 11. This behavior can be explained by the fact that in the random mobility models, the probability of meeting other nodes is significantly higher and more uniform than in LW and CM. The evidence of this is that the Direct-Delivery protocol, which only delivers messages to the destination, has a delivery ratio that is very high (more than 75% for all densities) in the random mobility case (see Figure 2) and relatively smaller for LW an CM (see Figure 11). This fact eases the delivery of the messages to random selected destinations. In any case, we stress again that the gap between knowledge-based protocols and other protocols is pretty evident when LW and CM are used. We finally compare the results of the experiments performed on real-world traces to those of the experiments using synthetic traces (the simulated area is one square km) generated according to the Levy Walk (LW) mobility model, in which the Inter-Contact Time (ICT) distribution, _i.e._, the time period between two consecutive contact times of the same two nodes, approximates the one observed in real traces. The ICT reflects the mobility pattern of the nodes. In [16], it is shown that the ICT distribution of human walking exhibits a power-law tendency up to some time after which it shows exponential decay. The authors show that adjusting the LW parameters, it is possible to generate an ICT distribution similar to the one observed in Infocom traces. Figure 13 shows the similarity between the ICT distribution extracted from the Haggle traces and the one obtained from LW, setting the parameters as suggested in [16]. From this figure, it is clear that  
Figure 11: Delivery ratio with different mobility models, 40 nodes. Figure 12: Delay with different mobility models, 40 nodes. though the two distributions are similar, in real traces, we expect more nodes having longer ICT than in synthetic ones. For the case of the outdoor mobility trace, it is evident that the much larger network area affects the overall performance of the protocols, as it takes much longer for nodes to meet. In addition, the fact that some nodes do not meet each other and there exist periods of time where the network is partitioned, the overall delivery ratio is much lower than that observed for the indoor case. However, since the mobility of the nodes cannot be affected by the protocols, it is impossible to overcome this problem and design a protocol that achieves higher delivery rates or reduces the delivery delay.