## 4 Simulation and Performance Analysis  
_Performance Metrics_  
The following performance metrics are considered in our QACM algorithm.  
Signal Reliability (SR): It is a measure of reachability signal transmission in crowd sensing between source node/s to the sink node. This metric is compared with previous works and was found to be significantly increased as shown in Figure 2. Additionally, the signal strength increased linearly from the initiation phase to completion phase that involved the available wireless infrastructure at that instance based on the requirements.  
Transmission Time (TT): It is defined as the time taken to send the sensed event signal information to the sink through different intermediate sensing nodes as shown in Figure 3.  
Figure 2: Signal reliability (SR).  
Crowd Sensing Efficiency (CSE): It is the level of crowd sensing efficiency based on different application requirements.  
Table 1 shows the TT comparison values of QACM with QICM and QOSA. There were remarkable changes in TT because of the two parameters of the QACM algorithm \(T_{ri}\) and \(T_{rt}\). There was a markedly reduced delay in transmission due to application-based transmission.  
Table 2 depicts the comparison of SR and TT of QACM with QIMC and QOSA. The comparison values are plotted in Figure 3. The simulation results indicate that QACM demonstrated greater signal reliability overall, but specifically between 6000 and 14,000 s, where its reliability was significantly higher than that of QIMC and QOSA. This is a clear indication of enhanced reliability due to the consideration of proper measures such as categorizing the mobile sensing nodes based on the parameter \(R_{bd}\).  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Values** \\ \hline Network size/range & 1000 m \(\ast\) 1000 m \\ Number of nodes & 300 \\ Node distribution & Random \\  & Involves different. \\ Node types & level of sensing \\  & elements \\ Simulation time & 14,000 s \\ \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Simulation** & \multicolumn{4}{c}{**SR**} & \multicolumn{4}{c}{**TT**} \\ \cline{2-7}
**Time** & **QIMC** & **QOSA** & **QACM** & **QIMC** & **QOSA** & **QACM** \\ \hline
2000 & 0.41 & 0.51 & 0.59 & 0.89 & 0.91 & 0.76 \\
4000 & 0.47 & 0.50 & 0.55 & 0.86 & 0.88 & 0.74 \\
6000 & 0.49 & 0.52 & 0.59 & 0.84 & 0.86 & 0.72 \\
8000 & 0.51 & 0.54 & 0.61 & 0.83 & 0.84 & 0.70 \\
10,000 & 0.53 & 0.57 & 0.65 & 0.81 & 0.83 & 0.69 \\
12,000 & 0.56 & 0.63 & 0.71 & 0.78 & 0.81 & 0.68 \\
14,000 & 0.58 & 0.65 & 0.74 & 0.76 & 0.79 & 0.67 \\ \hline \end{tabular}
\end{table}
Table 2: Comparative values of SR and TT of QACM with QIMC and QOSA.  
Figure 3: Transmission time.  
Table 2 shows the comparative values of TT of QACM with those of QICM and QOSA. Thus, there was an approximately 33% improvement in transmission delay.  
Figure 4 illustrates the crowd sensing efficiency (CSE). This graph clearly indicates very significant improvement in the crowd sensing process because of very efficient parameters such as \(R_{bd}\), \(T_{ri}\), \(T_{rt}\), and \(M_{qs}\) that collectively increased the efficiency 32% when comparing QIMC with QOSA and CACM.