# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 5 Performance Evaluation  
We compare the performance of Fuzzy Q-charging with the most relevant four existing algorithms. The first one is INMA [16], in which the MC determines the next sensor to charge based on factors, including the residual energy of sensors and the distance from sensors to the MC. The next charging sensor is chosen to minimize the number of other requesting nodes that may suffer from energy depletion. The second one is GSA [17]. At each charging round in GSA, the MC uses the gravitational search algorithm to determine a near-optimal charging order to fulfill all charging requests. In both INMA and GSA, the MC always charges to the maximum battery capacity of the sensor. The third comparison benchmark is RMP-RL [15]. RMP-RL uses the Deep Q-learning technique to determine the charging path of the MC. The objective is to minimize the number of dead sensor nodes and the moving distance of MC. The last comparison benchmark is our previous work, namely Q-charging [30]. Q-charging leverages Q-learning to determine the next charging location. However, different from Fuzzy Q-charging, Q-learning tries to maximize the number of sensors being charged to a predefined energy level. Besides, we also measure the network lifetime when there is no charging scheme is applied. Hereafter, we call this option a \"no-charging\" scheme.  
We conduct two experiments, among which the first complements the other. The first experiment investigates the impact of parameters \(\gamma\) and \(\alpha\) on the performance of our proposal. Based on the first experiment results, we determine the optimal values of \(\gamma\) and \(\alpha\). They are used in the second experiment, which compares Fuzzy Q-charging performance to the existing works. The metrics of interest include the network lifetime and the number of non-monitored targets over time in the evaluation. In all experiments, the network area is fixed at the size of \(100\) m \(\times\)\(100\) m. The sensors and targets were randomly scattered in the simulated region. The charging locations are positioned in the same place as the sensors. Each value plotted on the curves is the average obtained from \(20\) runs.  
Regarding the charging model, we adopted the parameters proposed in [31; 43]. The parameters have been verified by the experiments in [31; 43]. More specifically, we set \(\lambda=4.32\times 10^{-4}\), \(\beta=0.2316\), \(e_{MC}=10\) J/s, \(e_{move}=0.01\) J/s. Moreover, the initial energy of sensors and MC are \(10\)/ and \(100\)/, respectively. Each sensor has a battery capacity of \(10J\). In this simulation, we assume that sensors follow the Zigbee communication standard. We set the transmission range of sensors to \(15\) m. The reason is that based on the real experiment results reported in [44], the transmission becomes unreliable (i.e., the drop ratio becomes greater than \(0\)) beyond \(15\) m. The velocity of the MC is \(5\) m/s. The average energy consumption rate of the sensors is estimated by the base station, as mentioned in Section 3. The parameters are summarized in Table 7.