## 3 Performance through Simulation  
Although mathematical models [21] can be used to evaluate the performance of subsystems such as for instance the oppcomms, our objective is to evaluate the effect of the proposed directional protocol in the context of a realistic evacuation environment. Thus we have chosen to conduct a simulation study using the DBES tool [20] in a realistic evacuation scenario. In order to compare with earlier results, we use the same parameter values as in [19], with some exceptions.  
In our settings, each CN can store 100 EMs for communication purposes, its data transfer rate is 100 Kbits/s and the maximum effective CN communication range is 6 m, since it can be varied between 2-10 m and we have selected a middle range. An SN is located at each vertex of the building graph and each SN uses its own location identifier to locate the CNs. Location messages (LM) are transferred between CNs and SNs, and each LM contains the SN's location identifier. Thus, the navigation system operating on the CNs can determine its current position from the received LMs.  
The simulations are based on a single-story building representation of Figure 3 that was created from the blueprint of a real shopping centre in London. The layout contains 8 fire exits and 321 other nodes,where each node (_i.e._, graph vertex) has a maximum capacity (in our case it is set to 10). Thus, when a graph vertex has more evacuees than its maximum capacity, that node becomes overcrowded and the simulations take physical congestion into account.  
Two different mobility models are used in the simulations. In emergency cases the evacuees move to the selected exit on the shortest path. However, during normal operation the civilians use the following pattern. After waiting for some time which represents shopping or looking around in the store, the civilian chooses a random destination within the building, which is not an emergency exit. After moving to that location (the shortest path between two locations in the building is used) and then waiting for a random time at this destination, it chooses the next destination and so on in a repetitive Markovian random scheme. The average of the random waiting time in our simulations is 10 min. The movement velocity for each civilian is 1 m/s (since a majority of the people in a shopping centre wander from store to store), and we assume that the current health of the civilian does not affect its speed.  
Fire hazard with the same intensity is used in all the simulations to achieve a consistent evaluation. The fire spreads along graph edges, following a Bernoulli trial model. The fire model in the simulation and its effects on evacuee health have been inspired by [22]. In the simulations, the fire starts 600 s after the simulation begins and the results are an aggregate of 20 simulation runs with different distributions of evacuees. In all our simulations we did not assume that there was a central alarm in the building to represent the full potential of the self-organized network (formed by the CNs).  
Four different simulation scenarios have been tested:  
* The emergency system identical to that of [19] for comparison purposes;  
Figure 3: A single-story building used in simulation. The layout parts are connected to each other through the hallways. The string positions of the fire could be seen at (**a**) and (**c**) subfigure. (**a**) The first part of the layout; (**b**) The first second of the layout; (**c**) The third part of the layout; (**d**) The fourth part of the layout.  
* The ESS enhanced with our novel algorithm, where the SNs can determine positions without error (an ideal case);
* The previous system, but operating with positions with errors having a Gaussian distribution with a maximum error of \(2\) m; and
* The same as the previous one, but with Gaussian errors and a maximum error of \(10\) m.