## 4 Comparison and Discussion
### Power Control Approach
The first advantage of this comes from the interaction steps; the WBASN that receives this information needs to optimize its power to avoid interference. Another advantage is that a WBASN can update the changing network topology to adapt its transmit power. Despite that, the time of social information detection and convergence must be considered. Because WBASNs must exchange information about its own channel, SINR, network utilization, and channel gain before the update, these data must be detected fast and accurately. GA [22], a new power control-based algorithm, considers the QoS requirement to be the major factor for improving WBASN service and reducing power consumption. QoS requirements are considered in two services. The first service is the throughput-sensitive service, which must guarantee the minimum throughput requirement of the sensor. The second service is the BER-sensitive service, which must know the maximum BER threshold of each sensor. The simulation in [22] shows that GA outperforms PAPU in terms of throughput and BER. The disadvantage of GA is that real-world mobility environments are not considered, which may lead to a slow update in the WBASN and a long convergence time. The Bayesian game [23] also considers the tradeoff between power consumption and throughput, which is introduced in PAPU [17] and PCG [18]. While the other power control games do not take the sensor nodes into account, the Bayesian game schedules each sensor node's transmission in its channel based on the type of sensors. Therefore, WBASN can adapt its power at the node level based on the information about the dynamic environment or the type of nodes. The main advantage of the Bayesiangame is that it works without exchanging data between WBASNs. However, the severity of the interference at each sensor node is not considered, nor is the convergence time. In [24], cross-layer interference management attempts to reduce the power while ensuring the performance of the transmission. Compared to the other power control mechanisms, it is simple but effective to apply to WBASNs in a hospital environment. Because of its reduced transmission range, it cannot interfere with other WBASNs. However, the QoS constraint of sensor nodes is not considered. The above power control algorithms use TDMA; therefore, new MAC protocols that are suitable for IEEE 802.15.6 requirements must be established. A detailed comparison of these power control schemes is presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Interference** & \multirow{2}{*}{**Throughput Scheme**} & **Energy** & **Mobility** & \multirow{2}{*}{**Negotiation**} & **QoS** & **Self-Learning** & **Channel** & **Convergence** & **Tradeoff** \\  & & **Consumption** & **Support** & & & **Guarantees** & **Ability** & **Parameter** & **Time** & **Time** & \\ \hline \hline \multirow{4}{*}{PAPU [17]} & \multirow{2}{*}{Low} & \multirow{2}{*}{High} & \multirow{2}{*}{No} & \multirow{2}{*}{Yes} & \multirow{2}{*}{No} & \multirow{2}{*}{No} & \multirow{2}{*}{ \begin{tabular}{c} Channel gain, \\ power, \\ interference \\ gain \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} Slow \\ Low \\ \end{tabular}  } & \multirow{2}{*}{Low} & \multirow{2}{*}{High} & \multirow{2}{*}{No} & \multirow{2}{*}{Yes} & \multirow{2}{*}{No} & \multirow{2}{*}{No} & \multirow{2}{*}{\ \begin{tabular}{c} Power \\ budget, SINR \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} Slow \\ Low \\ \end{tabular}  } & \multirow{2}{*}{Low} \\  & & & & & & & & & \\ \cline{1-1}  & & & & & & &