## 5 Experimentation
### Results
#### 5.5.1 Replication Limit Tendency for the Control Configuration  
Figure 6 depicts the tendency of the calculated Control replication limit along the simulation. We observe that the calculated \(l\) tendency is inverse to the filling up of the buffer for each scenario (Figure 7), i.e., the calculated \(l\) values are initially high, corresponding to the period where buffers are still not overwhelmed but tend to decrease along the simulation depending on the congestion readings. Specifically, the \(l\) values for Taxis tends toward 8, Info5 tends toward 2, Campus tends toward 2, and Emergency tends toward 4.  
The controller's goal is keeping a high \(l\), aiming for a higher delivery ratio and lower latency while the buffers are not stressed, and lowering \(l\) to prevent this stress from happening. Following this behaviour, for the less congested scenario (Taxis), where the controller can keep a higher \(l\) value, we observe that the controller decreases \(l\) slowly. Conversely, for higher congested scenarios (Info5 and Campus), where high \(l\) values would rapidly overwhelm the buffers, we see that the controller decreases \(l\) much faster. Specifically, we observe that the controller reduces the \(l\) in the Info5 scenario faster than in the Campus scenario. Indeed, for the Info5 scenario for all the No-Control configurations, the buffer is similarly overwhelmed, whereas, for the Campus scenario, it depends on the No-Control replication limit configuration. More precisely, we can observe the controller's capacity to adjust the \(l\) in the Emergency scenario, where in the first half of the simulation (the more congested phase) the controller decreases the \(l\) and increases it in the second half of the simulation (the less congested phase).  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Scenario Setting** & **Taxis** & **Info5** & **Campus** & **Emergency** \\ \hline Simulation time & 69 h & 70 h & 34 h & 23 h \\ Simulation area & San Francisco Bay & hotel & 4.5 \(\times\) 3.4 km & 1 km\({}^{2}\) \\  & Contact & Contact & Map-Based & \\ Mobility model & Traces & Traces & + POI & RWP \\ \# Nodes & 304 & 41 & 80 & 100 \\ \# Contacts & 69,412 & 22,459 & 168,442 & 38,013 \\ TTL (s) & 10,000 & 10,000 & 10,000 & 4000 \\ Buffer size & 10 M & 10 M & 10 M & 10 M \\ Message generation & ISTH & ISTH & ISTH & CBR \\ distribution & FR: [10–60] s; & FR: [10–60] s; & FR: [10–60] s; & FR: 10 s; & 0–8 h: 10 s; \\ frequency (s) & TR: 1800 s & TR: 900 s & TR: 1800 s & 8 h–end: 80 s \\ Message size & [10–500] k & [10–500] k & [10–500] k & [10–500] k & [10–500] k & [10–500] m/s \\ Walk speed & N/A & N/A & 0.5 m/s & [0.5–1] m/s \\ \hline
**Control Settings** & **Taxis** & **Info5** & **Campus** & **Emergency** \\ \hline Nrof controllers & 10 & 2 & 4 & 20 \\ \([o_{min}-o_{max}]\) & [0.6–0.7]\% & [0.3–0.5]\% & [0.6–0.7]\% & [0.7–0.9]\% \\ Additive increase (\(k_{2}\)) & 1 & 1 & 1 & 1 \\ Multiplicative decrease (\(k_{1}\)) & 0.25 & 0.25 & 0.25 & 0.25 \\ LR nor inputs (2) & 6 & 10 & 6 & 6 \\ Aggregation interval (\(\hat{t}\)) & 60 s & 30 s & 300 s & 120 s \\ Prediction time factor (\(\phi\)) & 2 & 5 & 2 & 2 \\ Directive generation frequency & 900 s & 900 s & 900 s & 900 s \\ Reduction factor for a Decay (\(r\)) & 0.103 (\(d\) of 5\% at 1800 s) & 0.3 (\(d\) of 1\% at 300 s) & 0.058 (\(d\) of 5\% at 300 s) & 0.3 (\(d\) of 1\% at 300 s) \\ Decay Threshold & 0.1 & 0.1 & 0.1 & 0.1 \\ nrof Aggregations weight (\(\alpha\)) & 0.2 & 0.2 & 0.2 & 0.2 \\ \hline \end{tabular}
\end{table}
Table 3: Summary of the specific simulation settings per scenario.