感測器的電池電量充足與否一直是相當關鍵且值得被討論的議題，在最近幾年許多能夠延長感測器生命周期的方法依再被提出，如:能量感知路由選擇，太陽能充電，增加電池電容量。

而在能源再利用領域上，太陽能充電一直是無線感測網能否有突破性發展的主要發展方向.最快的解決方案是將所有的感測器旁都裝一個太陽能板，但目前太陽能板的成本相對於感測器還是來的較高，因此這種作法在實作上其實是不夠經濟的，故在本篇論文裡我們探討了如何在大量的感應器下以有限的太陽能板以及無線充電的技術來為無線感測網做排程的充電，使得在無線感測網裡使用頻率較頻繁的感測器與較不頻繁的感測器都可以得到適量的能量，達到綠能的效果，固本篇論文提供了能源感知充電演算法來解決分配太陽能給不同使用度的感測器的方法，並且在最後證實了我們的方法能夠使得電池生命週期比一般的最早能源耗竭優先充電以及隨機充電等方法還要好上二到六倍。

Battery life is a critical issue for wireless sensor devices. Some solutions for extending the battery lifetime of sensor devices are proposed, such as energy-aware routing, limited hardware capacity, and solar charger. The solar charger is a new technology in renewable energy field for wireless sensor network (WSN). A quick solution to extend the battery life is to integrate solar panel with sensor device, but the solar panel is very expensive for sensor deployment. Hence, equipping every sensor node with a solar panel to collect renewable energy is uneconomic. In general wireless charging solution, people usually deploy a power source provider in a WSN and place some relay chargers to cover sensor nodes in the WSN. Therefore, the problem of scheduling recharge task becomes a critical problem in relay node and power center. We propose a power-aware effective charging (PEC) solution to solve the recharge arrangement problem in WSN. This paper shows that PEC can extend the battery lifetime of sensor device 2~6 times the performance of PEC is better than EDF and random scheduling.
(Abstract)
Index Terms—Energy-aware, wireless charger, wireless sensor network, solar panel, renewable energy. (key words)

Outline
1.Introduction
1.1.Background
1.2.Paper Organization
2 System Background And Model
2.1 Related work
2.2 System Model and Problem Formulation
2.2.1 Parameter Definition
2.2.2 Power Function
3 Energy-aware Recharging Scheduling Algorithm
3.1 System Component
3.2 Network Topology
3.3 PEC Algorithm
3.3.1 Charging Task Queue Construction
3.3.2 Charger Level Construction
3.3.3 Dynamic Priority Assignment
4.Simulation Result
4.1 Simulation Parameters
4.2 Assessment Criteria
4.2.1 Lifetime of Sensor Network
4.2.2 The Number of Survival Nodes
4.2.3 Task Success Ratio
4.3 Simulation Result
4.3.1 Lifetime of Sensor Network
4.3.2 The Number of Survival Nodes
4.2.3 Task Success Ratio
5. Conclusion and Future Work
6. Reference

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