# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 1 Introduction  
Wireless Sensor Networks (WSNs) have found various applications, such as air quality monitoring, environmental management, etc., [1, 2]. A WSN typically includes many battery-powered sensor nodes, monitoring several targets, and sending sensed data to a base station for further processing. In the WSNs, it is necessary to provide sufficient monitoring quality surrounding the targets (i.e., guaranteeing target coverage). Moreover, the WSNs need to have an adequate capacity for the communication between the sensors and base station (i.e., ensuring connectivity) [3, 4, 5]. The target coverage and connectivity are severely affected by the depletion of the battery on sensor nodes. When a node runs out of battery, it becomes a dead node without sensing and communication capability, damaging the whole network in consequence. Wireless Rechargeable Sensor Networks (WSRNs) leverages the advantages of wireless power transferring technology to solve thatcritical issue in WSNs. A WRSN uses a mobile charger (MC) to wirelessly compensate for a rechargeable battery's energy consumption on a sensor node, aiming to guarantee both the target coverage and connectivity. In a normal operation, the MC moves around the networks and performs charging strategies, which can be classified into periodic [6; 7; 8; 9; 10] or on-demand charging [11; 12; 13; 14; 15; 16; 17]. In the former, the MC, with a predefined trajectory, stops at charging locations to charge the nearby sensors' batteries. In the latter, the MC will move and charge upon receiving requests from the sensors, which have the remaining energy below a threshold. The periodic strategy is limited since it cannot adapt to the sensors' energy consumption rate dynamic. On the contrary, the on-demand charging approach potentially deals with the uncertainty of the energy consumption rate. Since a sensor with a draining battery triggers the on-demand operation, the MC's charging strategy faces a new time constraint challenge. The MC needs to handle two crucial issues: deciding the next charging location and the staying period at the location. Although there are many, the existing on-demand charging schemes in the literature face two serious problems. The first one is the consideration of the same role for the sensor nodes in WRSNs. That is somewhat unrealistic since, intuitively, several sensors, depending on their locations, significantly impact the target coverage and the connectivity than others. Hence, the existing charging schemes may enrich unnecessary sensors' power while letting necessary ones run out of energy, leading to charging algorithms' inefficiency. It is of great importance to take into account the target coverage and connectivity simultaneously. The second problem is about the MC's charging amount, which is either a full capacity (of sensor battery) or a fixed amount of energy. The former case may cause: (1) a long waiting time of other sensors staying near the charging location; (2) quick exhaustion of the MC's energy. In contrast, charging a too small amount to a node may lead to its lack of power to operate until the next charging round. Therefore, the charging strategy should adjust the transferred energy level dynamically following the network condition. Motivated by the above, we propose a novel on-demand charging scheme for WRSN that assures the target coverage and connectivity and adjusts the energy level charged to the sensors dynamically. Our proposal, named Fuzzy Q-charging, aims to maximize the network lifetime, which is the period from when the network starts till the first target is not monitored by any sensor. A target is considered to be monitored by a sensor if it is covered by a sensor (i.e., remaining within the sensor's sensing range) and the sensor is connected to the base station (i.e., a routing path exists between the sensor and the base station). To achieve this ultimate goal, Fuzzy Q-charging attempts to extend the lifetime of the sensors, in which, sensors that contribute more to target monitoring will be prioritized more.