## 3 Performance through Simulation
### Simulation Results  
The simulations are performed with four different civilian densities (\(400\), \(600\), \(800\) and \(1000\) civilians) because the number of customers in a shopping centre may vary in a wide range (depending on the day, offers, holidays, _etc._). Note that \(400\) civilians on this layout is a sparse presence while \(1000\) people would be fairly crowded while \(600\) or \(800\) could be intermediate cases). Another important parameter is the starting position of the fire hazard: we have chosen two different locations shown in Figure 3 to start the fire, the first at node \(16-0\) is a critical location as it is very close to 3 exits, so the fire will spread very quickly towards the exits and then these exits become unreachable. The second starting position is less crucial, since the fire will be generated in the middle of a hallway, thus, the civilians can easily escape in different directions. Many studies of sensor networks for emergency management focus on the technical aspects of the sensor network itself, including the communication system efficiency, the packet travel delays, and the security [23, 24]. Here we have taken a different approach, and we essentially focus on the _success_ of the evacuation procedure by measuring the state of the evacuees themselves. Simulation results will be shown regarding two different aspects:  
* The first, shown in Figure 4, is the main focus of this paper, and it relates to the ultimate success of the evacuation. We measure how many evacuees reach the exit in how much time during the evacuation, and also evaluate their health level when they do exit the system. The percentage indicated is a measure of the effectiveness of the emergency evacuation system, and it captures the primary security metric of the proposed system;
* The second aspect focuses on the mobile self-organized network, and it measures the ratio of messages that are successfully delivered between CNs. This is a performance indicator of the communication protocol that is being used. The results from the first aspect highlight the differences between the path finding algorithms, while the second set of results focus on the success of the communication protocol. Figure 4 presents the results from the first aspect when the fire was generated at node \(16-0\). The first column in each group (indicated with ESS label) represents the same emergency system as in the previous work from our group, where the Dijkstra shortest path algorithm is used for selecting the evacuation path and the original version of Epidemic Routing is implemented. When observing the number of evacuated civilians (depicted in Figure 4b) it could be seen that this solution achieves around 95%-96%, which means that in the most dense case 950-960 civilians can be evacuated, but unfortunately the other 40-50 were unable to reach the exits in time. Figure 4a shows the health level (expressed as a percentage) of the successfully exited evacuees, while if a civilian could not exit the building, his/her health is not taken into  
account). The evacuated civilians have around 90%-93% of their health with the ESS. One important thing should be noticed, namely that the ESS (and other systems as well) gives better results when the density of the civilians is higher (so the performance of the ESS is dependent on population density). This is the positive impact of the self-organized mobile networks formed by the CNs: more nodes mean better communication opportunities therefore EMs can be delivered faster and more reliably to all CNs. The second, third and fourth columns show results for the enhanced system that uses our algorithm with the error model being used for the localization. ESS+ is an ideal scenario with no error in the positioning system, therefore, each SN can tell the exact position of a CN. Obviously this is not a real-life assumption.