## 5 Simulation Results
### Experimental Results of Phase II  
The output obtained in Phase I is fed as input in Phase II, i.e., post-deployment phase where a WBAN configuration is simulated in Castalia 3.2 [35] which is a Wireless Sensor Network simulator based on OMNET++ platform useful for early phase algorithm/protocol testing. A total of 13 nodes including sink are deployed all over human body (around \(10~{}\mathrm{m}\times 10~{}\mathrm{m}\) simulation area) out of which node 4 and node 9 are considered as implanted ones as shown in Figure 7 in such a way that they form a connected graph at the beginning where the sink node acts as BAN coordinator residing at roughly the center (i.e., waist). The network size follows the typical medical network based on WBAN which consists of 6 to 20 nodes (maximum) [2]. The default parameters used in the experiments are listed in Table 3 and any alterations to these values are stated explicitly. Mobility of each node due to posture change is modeled with LineMobility model [35]. The effective transmission power for each data communication is obtained from the set of transmission power levels \(\{-20~{}\mathrm{dbm},-12~{}\mathrm{dbm},-15~{}\mathrm{dbm},-10~{}\mathrm{dbm}\}\) defined by BANRadio (i.e., the radio module of Castalia 3.2 [35] for WBAN communication) to be operated with IEEE 802.15.4 ZigBeeMAC protocol. Accordingly, the transmission range corresponding to each power level is governed by BANRadio [35] as well. Although the experimentations are carried out based on IEEE 802.15.4 ZigBeeMAC but the proposed approach can work with other existing technologies (for instance Bluetooth Low Energy (BLE) [2]). The SAR is quantified for each node following Equations (4) and (5). The optimal set of actions depending on \(X_{t}\) of a node as obtained in Phase I is incorporated in each node during deployment following Algorithm 1 to minimize computational complexity and maximize lifetime without degrading the performance. \begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation Parameter** & **Default Value** \\ \hline Simulation area & \(10~{}\mathrm{m}\times 10~{}\mathrm{m}\) \\ Simulation time & \(10\),\(000~{}\mathrm{s}\) \\ Network size & \(13\) \\ Mobility model & LineMobility model [35] \\ MAC protocol & IEEE 802.15.4 \\ Data generation rate & \(14~{}\mathrm{packets/s}\) \\ \(SAR_{th}\) & \(0.3357~{}\mathrm{Watt/Kg}\)[3] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters and their default values. Figure 6: Variation of resultant discounted utility with system state for different combinations of probability values related to remaining battery power \(L_{t}\), event generation P (\(p_{on},p_{off}\)), packet transmission rate (\(p_{on},p_{off}\)) and link quality (\(l_{qon},l_{qoff}\)). Herewith, a series of experiments have been carried out to validate the proposed strategy as well as to estimate performance with respect to the state-of-the-art work. Data packets received by sink has been chosen as a metric to quantify the performance of the proposed approach. At the beginning, experiments have been done to determine ideal combinations of the probability values, i.e., \(p_{on},p_{off},pr_{on},pr_{off},lq_{on},lq_{off}\) in relation to event generation, packet sending rate and LQ respectively as illustrated beforehand. As depicted in Figure 7(a) utility values corresponding to each combination are presented in ascending order. The corresponding performance of each combination when mapped into routing strategy measured in terms of data packets received by sink has been plotted in Figure 7(b). Other metric data packets forwarded by relay nodes are also included in Figure 7(b) to study the behavior of the multi-hop routing. Results show that performance of the proposed strategy when simulated (in Figure 7(b)) enhanced following similar pattern as predicted before deployment of the network presented in terms of utility values in Figure 7(a). This justifies the correctness of mapping of the mathematical formulations into routing approach.