# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 2 Related Work  
Initially, we introduce the existing works related to periodic charging in WRSNs. In [6], the authors leverage PSO and GA to propose a charging path determination algorithm that minimizes the docking time during which the MC recharges itself at the depot. Ref. [7] jointly considers charging path planning and depot positioning to minimize the number of MCs while ensuring no sensor runs out of energy before being recharged. The work in [8] determines a charging path to maximize the MC's accumulative charging utility gain or minimize the MC's energy consumption during traveling. The authors then propose approximation algorithms with constant ratios for the maximization and minimization problems. Arguing that an MC can not fulfill all sensors' demand in dense networks, [PERSON] et al. in [9] introduce a multi-chargers approximation model to increase the charging speed. In [10], [PERSON] et al. derive a new energy transfer model with distance and angle factors. They also consider the problem of minimizing the total charging delay for all nodes. They use linear programming and obtain the optimal solution. As the charging schedule is always fixed, the periodic scheme fails to adapt to the dynamic of sensors' energy consumption. Regarding the on-demand charging, the authors in [16] address the node failure problem. They first propose to choose the next charging node based on the charging probability. Second, they introduce a charging node selected method to minimize the number of other requesting nodes suffering from energy depletion. In [12; 13], aiming to maximize the charging throughput, they propose a double warning threshold charging scheme. Two dynamic warning thresholds are triggered depending on the residual energy of sensors. The authors in [17] studied how to optimize the serving order of the charging requests waiting in the queue using the gravitational search algorithm. In [15], [PERSON] et al. introduce a new metric (i.e., charging reward), which quantifies the charging scheme's quality. The authors then address the problem of maximizing the total reward in each charging tour under the constraint of the MC's energy and sensors' charging time windows. They use a deep reinforcement learning-based on-demand charging algorithm to solve the addressed problem. The existing charging algorithms have two serious problems. First, the charging time problem has not been thoroughly considered. Most of the charging schemes leverage either the fully charging approach [6; 7; 8; 11; 12; 13; 16] or the partial charging one [18]. We want to emphasize that the charging time is an essential factor that decides how much the charging algorithm can prolong the network lifetime. Moreover, there is no existing work considering the target coverage and connectivity constraints concurrently. Most previous works treat all sensors in WRSNs evenly; hence, the MC may charge unnecessary sensors while necessary ones may run out of energy. Unlike them, this work addresses the target coverage and connectivity constraints in charging schedule optimization. We uniquely consider the optimization of charging time and charging location simultaneously. We use Fuzzy logic and Q-learning in our proposal. Fuzzy logic has been applied in many fields, such as signal processing [19; 20], robotics [21], and embedded controllers [22]. In WSNs, Fuzzy logic is a promising technique in dealing with various problems, including localization, routing [23; 24], clustering [25], and data aggregation [26; 27]. [PERSON] et al. in [23] propose a Fuzzy logic-based routing for lifetime enhancement in WSNs, which maps the network status into corresponding cost values to calculate the shortest path. In [28], the authors also leverage Fuzzy logic and Q-learning but in a cooperative multi-agent system for controlling the energy of a microgrid. In [29], Fuzzy and Q-learning are combined to address the problem of thermal unit commitment.