# Adaptive Energy Balanced Routing Strategy for Wireless Reschargeable Sensor Networks
## 1 Introduction
At this time, premature death for some nodes may be inevitable when the WCV is unable to replenish corresponding energy for nodes. In [18; 19], the charging operation is triggered periodically taking into account every node in the network. These algorithms mainly focus on the full coverage of the network, the charging points locations and the charging time allocation. In [20], a charging path named TRIANGLE is proposed. In [21], four different charging paths are compared and the influence of the number of charging points is also discussed. The authors in [22] investigate a throughput maximization problem by allocating WCV charging time. However, the above studies only consider the routing strategy or charging strategy unilaterally, ignoring the interaction between the charging strategy and routing algorithm. Articles which simultaneously optimize the network lifetime from both aspects show great research value. In [23], the concept of energy-aware routing cost is proposed for nodes to choose the next node, and the charging path is planned according to residual node energy after routing process. The authors in [24] investigate the problem of maximizing the minimum sampling rate of the nodes, by jointly considering routing and energy beamforming for wireless energy transmission. In [25], the simultaneous wireless information and power transfer (SWIPT) technology is applied into the cluster sensor networks. Moreover, a non-convex constrained optimization problem is proposed and solved by the distributed iteration algorithm, which can reduce energy consumption and improve energy efficiency. Nevertheless, the above optimizations adopt the point-to-point charging mode, where WCV charges only one sensor node at a time. In a multi-node charging scheme, WCV is able to charge multiple neighboring nodes within its charging range simultaneously, which greatly improves the charging efficiency [26]. The authors in [27] adopt multi-node charging scheme and genetic algorithm to optimize the routing and charging process with the purpose of obtaining the best data transmission path and mobile path. In this way, the waste of charging time is reduced and sensor nodes can get more charging energy. The authors in [28] develop a mathematical model for the WCV as a mobile platform for both WET and data collection, with the goal of minimizing energy consumption associated with the WCV. [PERSON] et al. [29] propose a grid-based charging routing joint optimization algorithm, in which the WCV stays at fixed charging points in each charging cycle and replenishes energy according to node energy consumption rate to balance node energy; then,nodes select the next hop node according to their location and charging efficiency. The location of charging points is the same each charging cycle, which leads that nodes' charging efficiency not change. The next hop node of each node is fixed during the data transmission process, and some nodes that remain in a high energy consumption state will die faster. Based on the above analysis, we propose an energy balanced routing strategy that is adaptive to the charging method in multi-node charging mode. Firstly, the charging efficiency of the node is adjusted by changing the position of the charging points in different cycles, so as to improve the overall replenishment effect. Moreover, the WCV charges for nearby nodes according to their energy consumption rate to ensure the energy balance of network as far as possible. Meanwhile, the node makes different routing choices according to its own charging characteristics to make compromise between single node energy consumption and the whole energy consumption, with the goal to not only reduce the overall network energy consumption but also optimize the average hops. In Section 2, the system model is formulated in advance. The charging strategy is proposed in Section 3. The routing algorithm adaptive to charging process is presented in Section 4. Implementation guidelines and complexity analysis are discussed in Section 5.