## 4 Comparison and Discussion
### Power Control Approach  
PAPU [17] is considered to be the primary game-centric power control approach to mitigate interference among WBASNs. Because a distributed power control algorithm is used for WBASNs, each WBASN acts selfishly to maximize its own performance. However, each WBASN within the range of interference needs to exchange information about its current transmit power, channel gain, and interference power. Each transmission of WBASNs is decided by the payoff function, which consists of a power price and the current power transmission. If the power price is high, the network utilization and power efficiency in the WBASN are low. Hence, the tradeoff between network utilization and the power constraint for each user is needed. The update process must be fast for each WBASN to reach its stable state. The simulation result in [17] shows that the convergence time in PAPU is acceptable. The advantages of PAPU are the fast convergence time and the low power consumption in the WBASN achieved by using a low power price. However, the power price must be considered further because it affects the convergence and tradeoff. The disadvantage of PAPU is that it is not suitable for a real-world mobile scenario because of the fixed channel gain and interference gain. Moreover, the power price does not clearly show the QoS requirements of the transmitted signal from sensors. Although the update status in the PCG algorithm [18] is similar to the PAPU algorithm, PCG is more efficient than PAPU because PCG considers the dynamic environment problem by using an adaptive power price. In addition, the power budget of a WBASN is added to the tradeoff in the PCG. Therefore, the player in PCG is penalized if its power budget is low or its current channel status is bad. This scheme can mitigate interference in WBASNs and increase the performance of WBASNs in the varying channel scenario. Although PCG was developed to cope with a dynamic environment, it has some disadvantages. WBASNs must negotiate the best response for each network. This may lead to a long convergence time. Moreover, the QoS requirement of sensors is not considered in PCG. Unlike PAPU and nonlinear PCG, a PCG with a self-learning strategy [19] allows a WBASN to act as a single-action learner agent that learns from its experience to reduce interference from other networks. As each WBASN employs an RL algorithm to improve its performance, the WBASN updates its next state based on the previous state and the current SINR value. The improvement in RL is that WBASNs do not negotiate with others, although this algorithm has been developed for the PCG. Another benefit is that users operate well under a dynamic environment (e.g., channel, power levels) by setting a learning rate for WBASNs. Furthermore, RL results in a better tradeoff than PAPU or basic PCG despite its long convergence time. However, as in PAPU and nonlinear PCG, the QoS constraint is not considered. A novel genetic-fuzzy power controller [20] is a method that combines GA and FPC into a power control method. The benefit of FPC is similar to that of RL-based algorithms in that WBASNs do not need to negotiate with others. A new development in FPC is to employ GA to speed up the convergence time by using the current transmit power as a feedback value. Moreover, FPC uses this feedback value to improve the fitness function. The simulation result shows that the performance of FPC outperforms that of PCG [18]. Hence, it is reasonable to adapt FPC in a mobility scenario in which interference is unpredictable because FPC provides an independent and fast convergence method. It is clear that FPC is more effective than PCG and PAPU. Nevertheless, the QoS constraint of sensors is not mentioned here. In [21], the interaction information between WBASNs is explored in terms of the cumulative distribution function of the distance between WBASNs. Based on that study, WBASNs detect the distance between others within their transmission range before broadcasting to every WBASN within the interference range.