## 5 Performance Evaluation  
To simulate our proposed system, we considered a room with dimensions of 5 m \(\times\) 4 m \(\times\) 3 m. Particularly, we employed static surveillance cameras rather than any mobile robot. Table 3 lists the unchanged parameters used in performing the simulations. Note here that any change to luminous parameters will affect the simulation results. All the simulations were executed in MATLAB. The network selection mechanism for a new NAR particularly depends on the OCC performance. When an LED array is detected by a camera using CNN, \(\lambda\) of the camera is immediately investigated to evaluate the possibility of selecting a camera. Four performance parameters, namely instantaneous received power, number of strips projected in the image sensor, SINR, and LOS communication range, \begin{table}
\begin{tabular}{c c} \hline \hline
**OCC parameters** & \\ \hline Effective LED area, \(\mathrm{A_{I}}\) & 7 cm\({}^{2}\) \\ Half-intensity radiation angle, \(\Psi_{1/2}\) & 60\({}^{\circ}\) \\ Transmit power, \(\mathrm{P_{t}}\) & 15 dBm \\ Gain of optical filter, \(\mathrm{g_{op}}\) & 1.0 \\ Image sensor aspect ratio & 32 aspect ratio \\ Pixel edge length, \(\rho\) & 1.5 um \\ Frame rate, \(\mathrm{f_{r}}\) & 30 fps \\ Focal length, \(\mathrm{f_{o}}\) & 36 mm (effective) \\ Responsivity, \(\
u\) & 0.51 \\ \hline
**BLE parameters** & \\ \hline Frequency band & 2.4 GHz \\ Modulation index & 0.5 \\ Channel bandwidth & 2 MHz \\ Transmit power, \(\mathrm{P_{tr}}\) & 20 dBm \\ \hline
**Indoor scenario** & \\ \hline Room dimension & 5 m \(\times\) 4 m \(\times\) 3 m \\ Camera height from ground & 1.5 m \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Unchanged system parameters for the simulation. were analyzed to calculate the value of \(\lambda\). We implemented more than 100 rules to obtain the precise score. The COG method was utilized to calculate \(\lambda\). Figure 6 depicts the variation of \(\lambda\) in each input parameter with increasing distance using the COG method. To evaluate \(\lambda\), we considered triangular MFs for all the inputs. The MFs were selected on the basis of a maximum communication range of 4 m.  
Noticeably, when increasing the number of cameras, \(\partial_{\text{AOV}}\) will be increased to a great extent. Therefore, the probability for the LED to appear inside the camera coverage will also increase. Concurrently, the selection probability for BLE will be reduced. In addition, when coverage of a camera overlaps with another camera, \(\text{S}_{\text{P-OCC}}\) depends on the value of \(\text{d}_{\text{overlap}}\). Evidently, the cameras can cover a large area when the value of \(\text{d}_{\text{overlap}}\) is small, hence increasing the selection probability. Figure 7 depicts the variation in \(\text{S}_{\text{P-OCC}}\) for a new NAR considering different values of \(\partial_{\text{AOV}}\) and \(\xi\). In this simulation, \(\text{d}_{\text{overlap}}\) and \(\text{d}_{\text{x},\beta}\) were fixed at 50 cm and 3 m, respectively. As shown in Figure 7, \(\text{S}_{\text{P-OCC}}\) is significantly improved with higher values of \(\partial_{\text{AOV}}\). On the other hand, the value of \(\text{S}_{\text{P-BLE}}\) is decreased concomitantly. The selection probability also depends on the outage probability of each system. The outage probability for the hybrid system is illustrated in Figure 8. The outage probability of OCC is comparatively high. Several reasons can be addressed for the high outage probability of OCC. First, as most of the current commercial cameras have limited AOV, the overall coverage area is small. Second, data communication requires a LOS connection between the camera and the  
Figure 6: Variation of the selection score (SS) with distance and (**a**) SNIR, (**b**) number of strips, and (c) instantaneous received power. LED. Third, the optical signal in OCC is almost unaffected by the NLOS component. In Figure 8, we considered both the LOS and NLOS scenarios.