# Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network
## 6 Conclusions and Future Work  
In this paper, we have proposed a novel mobile charger scheduling algorithm for WRSN. The proposed algorithm finds a charging tour for the mobile charger by maximizing the charging nodes' total weighted criticality indexes which can measure the node's connectivity contribution and energy criticality, subject to the traveling distance constraint of the mobile charger. Due to the NP-hardness of the problem, we then design a fast heuristic algorithm to solve it. Finally, we evaluate the performance of proposed algorithm against the NJNP and TSP scheduling algorithm through simulations. The proposed algorithm outperforms the others, particularly by reducing the network's data loss. Besides this, we also compare the proposed algorithm with different rewards, which are the  
Figure 8: Impact of \(p_{r}\) on the charging scheduling algorithms: (**a**) Total nodes’ disjointed time; (**b**) Total nodes’ inactive time; (**c**) Network’s data loss rate.  
Figure 7: Impact of \(v\) on the charging scheduling algorithms: (**a**) Total nodes’ disjointed time; (**b**) Total nodes’ inactive time; (**c**) Network’s data loss rate.  
criticality index without energy criticality and betweenness centrality. From the result, we can verify the necessity and feasibility of the proposed weighted criticality index.  
Some issues still exist that are worthy of further study. First, in the realistic environment, the battery life of a sensor node can be correlated to many issues, such as the node's transmission range (near field or far field), application type (temperature reading, imaging, video surveillance, tracking, etc.), deployment environment (city, field, mountain, forest, etc.), routing structure (single-hop, multi-hop, etc.), sink type (mobile sink [32] or fixed sink) and so on. In this paper, we simplify the energy consumption model of the sensor nodes. It's not very similar to the practical issues. Second, the charging efficiency of the WPT technology is related to the charging distance and antenna type. In this paper, we only consider the received charging rate of nodes, and neglect the power loss of the mobile charger. In order to improve the energy efficiency of the mobile charger, the charging distance between nodes and the mobile charger and the other antenna type of the WPT devices can also be considered. Finally, many obstacles in the practical environment exist which can cause trouble during the mobile charger's movement. Designing a charging tour to avoid such obstacles is a new challenge in WRSN. In future work, we will focus on the charging model refinement and parameter consideration of the issues mentioned above to facilitate practical implementations.  
[PERSON] presented the main ideas, designed the algorithm, performed the simulations and wrote the paper; [PERSON] provided some advice and revised the paper many times; [PERSON], [PERSON], and [PERSON] revised the paper.  
This work is supported by the Natural Science Foundation of Hunan Province, China under Grant No. 2017J3316 and No. 2018J2156; the Research Foundation of Hunan Provincial Educational Department, China under Grant No. 16C1547; the Natural Science Foundation of China under Grant No. 61672447, No. 61602398 and No. 61772195.  
The authors declare no conflict of interest.