## 4 IoT-Enabled Traffic-Aware Telemedicine Architecture (ITTA)
### Wearable-Device-based Monitoring Algorithm for Edge Device (WDMA-Edge)
A comprehensive comparison of various existing telemedicine architectures is performed and the results are tabulated in Table 4. Parameters such as the following have been considered to compare existing telemedicine architectures ([29; 30; 31; 48; 49; 50; 51; 52]) in the literature:

* Location-based services (LBS): GPS-based tracking and navigation assistance for ambulances can be of immense assistance to both ambulance operators as well as healthcare practitioners. * Real-time health monitoring: Monitoring the dynamically changing health parameters of the patients being transported is necessary since the risk level of the patient will vary with the change in the level of vital parameters. * Multi-level health risk assessments: Just as monitoring the vital parameters is important, it is equally important to ensure that the criticality or risk level of the patient is monitored. * Data transmission from mobile ambulance: After measuring the level of vital parameters, it is necessary to transmit the same to the doctor at the remote hospital. * QoS: While transmitting the data to the remote hospital, it is important to ensure that requisite QoS parameters are considered. Healthcare data, being very critical in nature, have stringent QoS parameters. * Availability of V2V/V2I communication: During the data communication, as VANETs are considered in the current work, we verify what type of network topology is possible/available in the respective road link. * Adaptive routing: Based on the type of network topology found on the road link, the routing protocol is selected dynamically. * Cloud storage or processing: All the data need to be transmitted to the doctor in the remote hospital. However, since the doctor is not present on the same network, cloud storage is required as an intermediary. The processing of the criticality analysis is also performed on the cloud. * Medical scoring for patients during transit: Categorizing the risk level of the patients cannot happen randomly and requires a methodical analysis. The same is also required for triaging of the patient. ## 5 Simulation Study for Edge-to-Fog Communication Service Analysis

### Simulation Scenarios

Two major scenarios in which V2V is utilized for data dissemination from mobile medical edges through mobile vehicles or stationary relay nodes are considered for this feasibility study/analysis. The simulation parameters used for designing the experimental setup and the scenarios are mentioned in Table 5.

Scenario 1--Edge Node Peer-to-Peer Communication (V2V):Here, the density of the vehicles is varied on a spatio-temporal basis based on the chosen time slots and the expected region of the traversal, as presented in Table 1.0 In this scenario, IM\({}^{2}\)E transmits data via vehicular ad hoc network based on the density of vehicles. Scenario 1 is represented in Table 1.1 \begin{table}
\begin{tabular}{l l l l l} \hline
**Time of Day** & **Rural** & **Suburban** & **Urban** & **Total (in 6 km)** \\ \hline Early Morning (3–5) & 8 & 28 & 68 & 104 \\ Morning (6–9) & 36 & 205 & 276 & 517 \\ Late Morning (10–11) & 28 & 164 & 176 & 368 \\ Mid-day (12–3) & 50 & 302 & 312 & 664 \\ Evening (4–6) & 60 & 324 & 342 & 726 \\ Late Evening (7–9) & 30 & 94 & 170 & 294 \\ Night (10–2) & 14 & 46 & 84 & 144 \\ \hline \end{tabular}
\end{table} 
Table 6: Number of vehicles used in simulation with 2 km stretch in each segment.