## 4 IoT-Enabled Traffic-Aware Telemedicine Architecture (ITTA)
### Wearable-Device-based Monitoring Algorithm for Edge Device (WDMA-Edge)
The geographical area considered for this simulation is mapped and edited using JOSM and SUMO for generating the node mobility. The VEINS simulator was used for building the simulations. The simulation parameters used have been provided in Table 5. The number of stationary nodes has been varied, compensating for low vehicular-density. Figure 4: Representation of Scenario 2. #### 5.2.1 Performance Analysis of Healthcare VANET

Based on the simulations that were performed according to the experimental design discussed above, the results were recorded and graphical representations of the same are shown in the following sub-sections given below. **Scenario 1**: _Variability of Packet Loss Ratio with respect to different routing protocols and message types._

The detailed simulation results have shown that the packet loss rate (PLR) is highest for the classic routing protocol--flooding. It does not demonstrate an impact of variability in PLR with respect to time slots or density of vehicles, nor with respect to the geographic divisions, whereas the other two routing protocols, that is, ad hoc on-demand distance vector (AODV) and greedy perimeter stateless routing (GPSR), show variability in PLR with respect to time slots or density of vehicles, and with respect to the geographic divisions. In Scenario 1, it can be observed in Table 7 and Figure 5 that as density increases, the PLR decreases in the case of AODV. When flooding is used, it is seen that the PLR is always higher than 90%. In the case of GPSR, we observe that the performance is similar to AODV. It is clear that the PLR is always high and greater than 80% on average for all the routing protocols. The reason for this is that even though the nodes are high in density during certain time slots, the ambulance moves at a rate up to 4 times faster compared to the other nodes (which is also true in reality). Owing to this, the ambulance is unable to stay connected to a single node for a sufficiently long period of time in order to result in a proper route. Hence, for every routing mechanism, an efficient transmission of data is below the expected quality. When viewed from the angle of vehicular density, we observe that during mid-day and evening, when the density is highest, at 664 and 726, the performance of AODV and GPSR improves a lot, with AODV being comparatively lower, as shown in Table 7. A similar behavior is also seen in the case of flooding. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Timings** &
\begin{tabular}{c} **Vehicular** \\ **Density** \\ \end{tabular} & **PR** & **Audio** & **ECG** & **Total** \\ \hline \hline  & & **Low** & **High** & **Low** & **High** & **Low** & **High** & **Low** & **High** \\ \hline \hline   Early Morning & 104 & AODV (86\%) & Flooding (97\%) & AODV (86\%) & Flooding (97\% ) & GPSR (85\%) & Flooding (97\%) & AODV (86\%) & Flooding (97\%) \\ \hline   Morning & 517 & AODV (69\%) & Flooding (91\%) & AODV (69\%) & Flooding (91\%) & AODV (69\%) & Flooding (91\%) & AODV (69\%) & Flooding (91\%) \\ \hline   Late Morning & 368 & GPSR (91\%) & Flooding (96\%) & GPSR (91\%) & Flooding (96\%) & GPSR (91\%) & Flooding (96\%) & AODV (91\%) & Flooding (96\%) \\ \hline   Mid-day & 664 & AODV (47\%) & Flooding (91\%) & AODV (48\%) & Flooding (91\%) & AODV (49\%) & Flooding (91\%) & AODV (47\%) & Flooding (91\%) \\ \hline   Evening & 726 & AODV (34\%) & Flooding (92\%) & AODV (34\%) & Flooding (92\%) & AODV (35\%) & Flooding (92\%) & AODV (34\%) & Flooding (92\%) \\ \hline   Late Evening & 294 & AODV (90\%) & Flooding (92\%) & AODV (90\%) & Flooding (92\%) & AODV (89\%) & Flooding (92\%) & AODV (90\%) & Flooding (90\%) \\ \hline   Night & 144 & AODV (87\%) & Flooding (95\%) & AODV (87\%) & Flooding (95\%) & AODV (86\%) & Flooding (95\%) & AODV (41\%) & Flooding (89\%) \\ \hline  & & & & & & & & \\ \end{tabular}
\end{table} 
Table 7: Low and high PLR based on data type and routing protocol in Scenario 1.