# Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network
## 2 Related Works
### Mobile Charger Schedule  
In recent years, a lot of efforts have been made on exploring the mobility-assisted energy replenishment in WRSN, in which the charging scheduling schemes for the mobile charger is a prominent issue. In general, charging scheduling schemes for WRSN can fall into two categories: Periodical scheme and on-demand scheme. Periodical scheduling scheme converts the energy charging problem into a TSP based on nodes' distribution model and energy consumption model, where the mobile charger knows all nodes' statuses in advance and carries out the charging tour periodically. For example, [PERSON] et al. [15] focused on the scenario where the mobile charger has to accomplish the charging tasks by visiting all the nodes in the network with the objective of maximizing the ratio of the mobile charger's vacation time over the cycle time. The mobile charger's shortest traveling path is decided in advance before its departing from the service station due to the energy consumption rates of nodes are unchanged. They [16] extended their work to a multiple-node charging case, in which the nodes around the mobile charger's wireless power transmission range can be charged at the same time, thus greatly improving the charging efficiency. [PERSON] et al. [18] proposed an optimal movement strategy for the mobile RFID reader to charge all nodes in the network, such that the charging delay is minimized. They used the concepts of the smallest enclosing space and a space discretization method to find the optimal stop locations for the mobile reader. They also a proposed energy synchronized mobile charging (ESync) protocol [19] with the assumption that nodes' energy consumptions are already known by the mobile charger. They synchronized the charging request sequence of nodes with their sequence on charging tour to minimize the nodes' charging delay and constructed a set of nested TSP tours involving the nodes with low residual energy to reduce the mobile charger's moving distances. [PERSON] et al. [20] proposed a framework of joint wireless energy replenishment and anchor-point based mobile data gathering in WRSN. They formulated the problem into a network utility maximization problem and presented a distributed algorithm to solve it. [PERSON] et al. [21] formulated a mobile charger's velocity control problem under the constraints of patrolling cycle and acceleration limit to maximize the network lifetime, and developed a near-optimal heuristic solution with a provable upper bound. [PERSON] et al. [22] formulated a multiple mobile charger scheduling problem to charge life-critical sensors in the network with an objective to minimize the number of mobile chargers and then they proposed an approximation algorithm with a provable performance guarantee. The periodical scheduling schemes, which address the charging problem as an optimization path planning problem with the assumption that the node's energy consumption rate is constant and known in advance by the mobile charger, are not suitable for the dynamic network, in which the node's energy consumption rate is difficult to accurately estimate. In contrast, the on-demand scheduling scheme is carried out in an on-demand manner, where the mobile charger doesn't know all nodes' charging requests in advance. In practice, due to the dynamic variations of the node's energy consumption, the mobile charger's traveling path can't be globally planned. In the on-demand scheme, node's charging request is dynamically sent to the mobile charger only when the node's residual energy falls below a predefined threshold. Then, the mobile charger will select one request to service and put others into a waiting queue. For example, [PERSON] et al. [23] proposed an on-demand path planning method based on the discipline of Near Job Next with Preemption (NJNP), in which the mobile charger always greedily selects the nearest requesting node to charge. In Reference [24], [PERSON] et al.