## 3 Performance through Simulation
### Simulation Results
We also show a normal distribution with the maximum error set to 2 m (I) while in (II) it is 10 m. The performance of ESS+, can achieve about 3%-4% better results on evacuated civilians and 6%-7% on the health of the civilians than just simply ESS. The 3%-4% improvement in the number of the evacuees means 30-40 people in the overcrowded scenario. The proposed algorithm does not only rescue more people from the building, but the evacuated civilians have better health as well (Figure 4a). These results indicate that the directional based path finding algorithm can calculate the appropriate evacuation path with or without positioning error, since the variants of ESS+ achieve nearly the same results with differences well within statistical variations due to the simulations, and the proposed path finding algorithm appears to be tolerant to positioning errors. Other simulation results obtained when the fire starts at node \(100-2\) are shown in Figure 5. The same conclusions can be drawn as from Figure 4, while in this case the differences between the enhanced and the normal version of ESS are smaller with regard to the number of successful evacuees in Figure 5b that improves by 2%, and by 3% with regard to health in Figure 5a, though this improvement is not negligible since 2% of the evacuees is 8-20 rescued people. Figure 4: Results of the evacuation when the fire was generated at node \(16-0\). In each group the first column Emergency Support System (ESS) indicates the same emergency system as proposed in [19]. The second one (ESS+) is the enhanced system but without any localization error (an ideal case). The third and fourth are the enhanced version as well but with localization noise (which follows the normal distribution with maximum of \(2\) and \(10\) m). (**a**) Average evacuee health _vs_. number of the civilians; (**b**) Evacuated civilians _vs_. number of the civilians. The lower difference is because of the starting location of the fire hazard. This location (as it is depicted in Figure 3) is in the middle of a long hallway with no fire exits. In this case the shortest evacuation path to the nearest exit could be a good (but definitely not the best) solution as well, since the evacuation path from the hazardous area to the nearest exit is much less likely on the path of the fire, because the fire is being spread from a centre node towards the exits. Our final results relate to resource consumption in the mobile self-organized network formed by the CNs. Figure 6 shows the average number of the received EMs in a CN. The figure contains 5 columns instead of 4 in each group, since the fifth is the ESS+ system but implemented with the Epidemic Routing protocol instead of our communication protocol. This is essential to draw the right conclusions as nodes with ESS are forming different networks than with ESS+. To compare the two protocols, the same networks and the same conditions should be established (so in our aspect the first column here is meaningless, it is shown only for completeness). As it could be observed, the extended version of the Epidemic Routing protocol can achieve much better results, than the original version, when the number of duplications is taken into account. This is because Epidemic Routing protocol transfers all the new messages between the communication nodes, while the novel version transfers only those which fulfill two requirements. The first one is that the timestamp of the EM must be more recent than the receiver node's latest EM, and the second is that the contained direction must enclose a bigger angle with the resultant direction vector then \(\alpha_{t}\). In our simulations the \(\alpha_{t}\) has been chosen to \(\pi/4\). Obviously it can be seen as well, that increasing the density of the civilians, the number of the transferred EMs is increasing, since the average size of the network formed by the CNs is bigger as well.