# Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN
## 4 System Model
### Network Model  
The proposed IoT-WBAN network consists of ten (10) heterogeneous SNs having limited hardware resources. These SN's are deployed in/on different parts of the front and back side of human body. Beside these SNs two CNCs are also deployed on the left and right hips. The position of the CNCs is important in order to minimize line-of-site (LoS) problems which can affect network connectivity. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**A Summary of Energy Harvesting Source, Mechanism and Efficiency**} \\ \hline
**Source** & **Sensing** & **Source Type** & **Harvested Power [30; 37]** & **Advantages** \\ \hline Solar & Photovoltaic Cells & Natural & 12 mW/cm\({}^{2}\) & Inexhaustible and clean \\ Ambient Indoor light & Photovoltaic Cells & Artificial/Natural & 100 mW/cm\({}^{2}\) & Inexhaustible and clean \\ Thermoelectric & Thermocouple & Artificial & 60 \(\mu\)W/cm\({}^{2}\) at & Small size, no vibration \\  & & & \(\Delta T=5\,^{\circ}\)C & and noise and reliable \\ Ambient Air flow & MEMS turbine & Natural & 1 mW/cm\({}^{3}\) at 301/min & - \\ Wind & Anemometer & Artificial/Natural & Upto 1200 mW/day & Widely available \\ Footfalls & Piezoelectric & Artificial & 5 W & Easily available \\ Finger motion & Piezoelectric & Artificial & 2.1 mW & High conversion \\  & & & & efficiency \\ Exhalation & Breath Mask & Artificial & 0.4 W & High conversion \\ Breathing & Ratchet-Flywheel & Artificial & 0.42 W & Pollution free energy \\ Blood Pressure & Micro-generator & Natural & 0.37 W & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Energy harvesting sources and their mechanisms [30]. Figure 2: Energy harvesting system with components architecture. The CNC simply receives data from SNs and forwards it to a personal digital assistant (PDA) for further processing. Table 2 provides a detailed description of the SNs and CNCs. The deployment of different health-related SNs on different parts of the human body has been portrayed in Figure 3.  
The communication scenario is in single-hop and multi-hop. Single-hop takes place when the sensed data is either serious or SN is near to CNCs. If the scenario is different as mentioned above, then the SNs communicate its data using multi-hop communication using the Path Cost Estimation (_P.C.E._) function. The required factors for _P.C.E._ computation are calculated by SNs and then send to CNC. This is performed after assigning predefined time slots to each SN at initialization stage of each sensing/transmission round by using TDMA technique. The following assumptions have been made in proposed research work:  
1. [label=(0)]
2. Due to mobility of human body, the positions of the SNs may frequently change. 3. Power consumption during computation/processing is considered to be negligible. As its low compare to data packet transmission or reception. 4. To cater limited energy availability problem, an energy harvesting scheme is used in the proposed technique. With the passage of time SNs could harvest energy (as shown in Table 2) to prolong network lifetime. The prediction of the harvested energy of each SN is articulated in Equation (2): \[E_{Hav}\ (t,\ Q_{Setup})\ =\ \int_{t}^{t+Q_{Setup}}\lambda_{i}d\tau\] (2)  
where \(E_{Hav}\) represents probable harvested energy by a SN \(i\) in a pre-defined period \(\tau\). The term \(\lambda_{i}(\tau)\) represents the charging rates of SN \(i\) in time \(\tau\). 5. Ten sensors are positioned in/on human body of which 07 are wearable and 03 are implanted. Two CNCs have been deployed to minimize Line-of-Sight (LoS) problem.