無線感測網路(Wireless Sensor Networks, WSNs)是由眾多低成本且體積小的感測器(Sensor)所組成，應用範圍很廣，包括農業動植物監控、醫療與健康照護、智慧生活、軍事用途、綠色節能等。大多數的感測器由電池供電，且更換電池不易，致使感測器電量一旦耗盡，則視同死亡。因此，如何延續無線感測器網路的生命期，一直是學者們所共同努力的目標。近年來，隨著無線充電技術的逐漸成熟，行動充電車可近距離替感測器進行無線充電。本研究擬針對規則佈建的無線感測器網路，探討充電車對感測器的充電策略。本研究將考慮感測器在感測任務及傳輸任務間的電量分配比例、行動充電車的行進方向與移動速率，以期達到較佳的事件捕捉率。最後，透過效能評估，本研究將歸納較好的充電參數設定值，諸如：感測器在感測任務及傳輸任務間的電量分配比例、行動充電車的行進方向與移動速率等，已達到較好的網路監控效能。

Recently, the emerging wireless charging technology has received much attention in the wireless sensor network (WSN) because it achieves the perpetual operation of WSNs and forms the wireless rechargeable sensor network (WRSN). In this thesis, we consider the WRSNs where plenty of static sensors are regularly deployed to monitor the suspicious events. Mobile chargers aim to replenish the energy of sensors and collect their sensing data in an efficient way such that high monitoring quality and small data reporting delay can be achieved. This thesis proposes four charging strategies for mobile chargers and then conducts analyses for the event detection quality of each strategy. Based on the performance evaluations, this thesis generalizes better parameter settings, including energy distribution between tasks of sensing and transmitting, and movement speed of mobile chargers, to improve the event detection quality.

Table of Contents
List of Figures	IV
List of Tables	V
Chapter 1. Introduction	- 1 -
Chapter 2. Related Works	- 5 -
Chapter 3. Problem Statement	- 8 -
3.1 Network Model	- 8 -
3.2 Wireless Communication Model	- 10 -
3.3 Wireless Recharging Model	- 11 -
3.4 Problem Statement	- 12 -
Chapter 4. QoC Analysis	- 15 -
4.1 Mobile Charging Strategy by Constant Velocity	- 16 -
4.2 Enhance Mobile Charging Strategy by Constant Velocity	- 23 -
4.3 Mobile Charging Strategy by Constant Angular Velocity	- 25 -
4.4 Hybrid Mobile Charging Strategy	- 27 -
4.5 Summary	- 29 -
Chapter 5. Performance Evaluation	- 30 -
Chapter 6. Conclusions	- 37 -
References	- 38 -
Appendix-English paper	- 41 -

List of Figures
Fig. 1. Network environment.	- 8 -
Fig. 2. MC-H scenario.	- 27 -
Fig. 3. Impact of different speed with the throughput of transmission.	- 31 -
Fig. 4. Impact of delay time between MC-CV and EMC-CV.	- 31 -
Fig. 5. The scale of deployment of MC-CV and EMC-CV.	- 32 -
Fig. 6. Impact of different parameters for MC-CV and EMC-CV.	- 33 -
Fig. 7. Network Utility at φ=0.5 in MC-CAV.	- 34 -
Fig. 8. Network Utility at φ=0.5 in MC-H.	- 35 -
Fig. 9. Event detection rate in different charging scheduling.	- 36 -
Fig. 10. Data delivery latency in different charging scheduling.	- 36 -

List of Tables
TABLE 1	- 30 -

[1]Y. Gu and T. He, “Dynamic Switching-Based Data Forwarding for Low-Duty-Cycle Wireless Sensor Networks,” IEEE Transactions on Mobile Computing, vol. 10, no. 12, pp. 1741–1754, Dec. 2010. 
[2]Z. Li, Y. Peng, W. Zhang, and D. Qiao, “J-RoC: A Joint Routing and Charging Scheme to Prolong Sensor Network Lifetime,” IEEE ICNP, BC Canada, Oct. 2011. 
[3]H. P. Gupta, S. V. Rao, and T. Venkatesh, “Sleep scheduling for partial coverage in heterogeneous wireless sensor networks,” IEEE Comsnets, Bangalore, India, Jan. 2013 
[4]J. Lin and M. A. Ingram, “SCT-MAC: A Scheduling Duty Cycle MAC Protocol for Cooperative Wireless Sensor Network,” IEEE ICC, Ottawa Canada, June 2012. 
[5]S. Tiang, X. Y. Li, X. Shen, J. Zhang, G. Dai, and S. K. Das, “Cool: On Coverage with Solar-Powered Sensors,” IEEE ICDCS, Minneapolis USA, June 2011. 
[6]Y. Zhang, S. He, J. Chen, Y. Sun, and X. Shen, “Distributed Sampling Rate Control for Rechargeable Sensor Nodes with Limited Battery Capacity,” IEEE Transactions on Wireless Communications, vol. 12, no 6, pp. 3096-3106 June 2013. 
[7]B. Gaudette, V. Hanumaiah, S. Vrudhula, and M. Krunz, “Optimal range assignment in solar powered active wireless sensor networks,” IEEE INFOCOM, Orlando USA, Mar. 2012. 
[8]Y. Peng, Z. Li, W. Zhang, and D. Qiao, “Prolonging Sensor Network Lifetime Through Wireless Charging,” IEEE RTSS, San Diego, CA, Nov. 2010. 
[9]Z. Zhou and G. Qu, “An Energy Efficient Adaptive Event Detection Scheme for Wireless Sensor Network,” IEEE ASAP, Santa Monica, CA, USA, Sept. 2011. 
[10]J. Meng, H. Li, and Z. Hen, “Sparse event detection in wireless sensor networks using compressive sensing,” Proceeding of Conference on Information Sciences and Systems, pp. 181-185, 2009.
[11]S. He, J. Chen, D. K. Yau, H. Shao, and Y. Sun, “Energy-efficient capture of stochastic events by global- and local-periodic network coverage,”MobiHoc, p. 155, 2009. 
[12]D. Yau, N. Yip, C. Ma, N. Rao, and M. Shankar. “Quality of Monitoring of Stochastic Events by Periodic and Proportional-Share Scheduling of Sensor Coverage,” ACM Trans. Sens. Netw., vol. 7, no 2, pp. 1-49, 2010. 
[13]S. He, J. Chen, F. Jiang, D. K.Y. Yau, G. Xing, and Y. Sun, “Energy Provisioning in Wireless Rechargeable Sensor Networks,” IEEE INFOCOM, Shanghai, China, April 2011. 
[14]S. Zhang, J. Wu, and S. Lu, “Collaborative Mobile Charging for Sensor Networks,” IEEE MASS, Las Vegas, USA, Oct. 2012. 
[15]K. Li, H. Luan, and C.C. Shen, “Qi-ferry: Energy-constrained wireless charging in wireless sensor networks,” IEEE WCNC, Shanghai, China, April 2012. 
[16]J. Li, M. Zhao, and Y. Yang, “OWER-MDG: A Novel Energy Replenishment and Data Gathering Mechanism in Wireless Rechargeable Sensor Networks,” IEEE GLOBECOM, California, USA, Dec. 2012.
[17]S. Guo, C. Wang, and Y. Yang, “Mobile Data Gathering with Wireless Energy Replenishment in Rechargeable Sensor Networks,” IEEE INFOCOM, Turin Italy, April 2013. 
[18]R. P. Sawnat, Q. Liang, D. O. Popa, and F. L. Lewis. “Experimental Path Loss Models for Wireless Sensor Networks,” IEEE MILCOM, Orlando FL, USA, Oct 2007.