近年來，無線可充電感測器網路（WRSNs）因其在許多監測應用中的重要作用而成為一個重要議題。無線可充電感測器網路通常由許多可充電的電池進行供電的小型感測器組成。由於感測器體積小，其電池容量有限。因此，無線可充電感測器網路的壽命受到限制，阻礙了其大規模部署。為延長無線可充電感測器網路的生命期，近年來人們在能量充電方面做出了廣泛的努力。
無線能量傳輸（WET）技術作為一種革命性的能源供應技術，為延長無線可充電感測器網路的生命期提供了一種替代解決方案。近年來，使用行動充電器（MC）對感測器進行無線充電已得到廣泛討論。在文獻中，已經提出了許多充電演算法來構建充電路徑並確定行動充電器對感測器充電的停駐點。大多數研究將所有感測器視為同等重要，旨在最大限度地增加充電感測器的數量。然而，不同的感測器有不同的貢獻，尤其是在網路連通和覆蓋方面。靠近基站的感測器對網路連通的貢獻更大，因為它的故障會阻止更多的數據傳輸。另一方面，與其他感測器具有較小感測覆蓋重疊的感測器對網路覆蓋的貢獻較大，因為如果其能量耗盡，很少或沒有相鄰感測器可以替換其感測覆蓋。本研究提出了一種稱為ERSQ的充電機制，它將監測區域劃分為一些大小相等的網格，並考量包含覆蓋貢獻、網路連通貢獻和剩餘能量在內的重要因素，旨在最大限度地提高給定的無線可充電感測器網路的監測質量。實驗結果表明，所提出的ERSQ在監測覆蓋、工作感測器的數量以及工作感測器的有效性指標方面優於現有的充電機制。
上述ERSQ沒有考量充電請求感測器的數據質量。稀疏區域中的感測器對數據質量的貢獻更大，因為如果其能量耗盡，很少或沒有相鄰感測器可以代替感測器執行感測操作。本研究提出了一種稱為JDCC的充電機制，它將監測區域劃分為多個網格，並考量每個網格在網路連通性、數據質量以及路徑成本方面的貢獻，旨在最大限度地提高監測質量。實驗結果表明，所提出的JDCC在數據質量、工作感測器的數量以及工作感測器的有效性指標方面優於現有的充電機制。

Wireless rechargeable sensor networks (WRSNs) have become an important issue in recent years due to their important roles in many monitoring applications. The WRSNs usually consist of many small sensors powered by rechargeable batteries. Since the sensor is small, the capacity of its battery is limited. As a result, the lifetime of WRSNs is limited which obstructs the large-scale deployment of WRSNs. To prolong the lifetime of WRSNs, extensive efforts for energy recharging have been taken in recent years.
Wireless energy transfer (WET) technology as a revolutionized energy supply technology provides an alternative solution to prolong the lifetime of the WRSNs. Wireless charging of sensors using a mobile charger (MC) has been widely discussed in recent years. In literature, a lot of recharging algorithms have been proposed to construct recharging paths and determine the stopping points for mobile chargers to recharge the sensors. Most studies treated all sensors as equally important and aimed to maximize the number of recharged sensors. However, different sensors have different contributions, especially for network connectivity and coverage. The sensor closer to the base station has a larger contribution to network connectivity since its failure can block more data transmissions. On the other hand, a sensor that has a small overlapped sensing coverage with others, has a larger contribution to network coverage, since few or no neighboring sensors can replace its sensing coverage if the energy is exhausted. This study proposes an energy recharging mechanism, called ERSQ, which partitions the monitoring region into several equal-sized grids and considers the important factors, including coverage contribution, network connectivity contribution, and the remaining energy, aiming to maximize surveillance quality for a given WRSNs. Performance studies reveal that the proposed ERSQ outperforms existing recharging mechanisms in terms of the coverage, the number of working sensors as well as the effectiveness index of working sensors.
The above ERSQ does not consider the data quality of the recharging requested sensors. A sensor in a sparse region has a larger contribution to data quality because few or no neighboring sensors can execute the sensing operation instead of the sensor if it is energy exhaustion. This study proposes an energy recharging mechanism, called JDCC, which partitions the monitoring region into grids and considers the contribution of each grid in terms of network connectivity, data quality as well as path cost, aiming to maximize the surveillance quality. Performance studies reveal that the proposed JDCC outperforms existing recharging mechanisms in terms of the data quality, the number of working sensors as well as the effectiveness index of working sensors.

Outline	V
List of Figures	VII
List of Tables	IX
Chapter 1. Introduction	1
1. 1 Background	1
1. 2 Research Goals	3
1. 3 Organization of the Thesis	3
Chapter 2. Related Work	5
2.1 The Non-Partition Model	5
2.2 The Partition Model	6
Chapter 3. Coverage and Connectivity Aware Energy Charging Mechanism	8
3.1 Assumptions and Problem Statement	9
3.1.1 Network Environment	9
3.1.2 Energy Model	10
3.1.3 Objective	11
3.1.4 Constraints	12
3.2 The Proposed ERSQ Algorithm	13
3.2.1 Initial Phase	14
3.2.2 Coverage-Based Benefit Evaluation (CBBE) Phase	15
3.3 Simulation	22
3.3.1 Simulation Settings	22
3.3.2 Simulation Results	22
3.4 Summary	32
Chapter 4. Data Quality and Connectivity Aware Energy Charging Mechanism	33
4.1 Assumptions and Problem Statement	34
4.1.1 Network Environment	34
4.1.2 Energy Model	35
4.1.3 Objective	36
4.1.4 Constraints	37
4.2 The Proposed JDCC Algorithm	39
4.2.1 Initial Phase	40
4.2.2 Scheduling Phase	41
4.2.3 Moving & Recharging Phase	45
4.3 Simulation	55
4.3.1 Simulation Settings	55
4.3.2 Simulation Results	56
4.4 Summary	62
Chapter 5. Conclusion and Future Work	63
References	65
List of Figures
Fig 1. 1. Example of the different importance of sensors in terms of coverage contribution.	2
Fig. 3. 1. Cycle T consists of several rounds, which consist of three tasks.	14
Fig. 3. 2. Partition of the monitoring region R.	15
Fig. 3. 3. Pseudocode of the proposed ERSQ algorithm.	20
Fig. 3. 4. Simulation environment.	23
Fig. 3. 5. Evaluation of parameters in the ERSQ algorithm	24
Fig. 3. 6. Comparison of three algorithms in terms of the number of working sensors.	25
Fig. 3. 7. Comparison of three algorithms in terms of coverage and sensor efficiencies.	25
Fig. 3. 8. Comparison of three algorithms in terms of the coverage of the monitoring region.	26
Fig. 3. 9. Comparison of three algorithms in terms of the number of working sensors.	27
Fig. 3. 10. Comparison of three algorithms in terms of coverage efficiency.	27
Fig. 3. 11. Comparison of three algorithms in terms of the recharging efficiency.	28
Fig. 3. 12. Comparison of four algorithms in terms of the effectiveness index of working sensors.	29
Fig. 3. 13. Comparison of four algorithms in terms of the effectiveness index of working sensors.	29
Fig. 3. 14. Comparison of four algorithms in terms of the coverage connectivity index.	30
Fig. 3. 15. Comparison of the ERSQ algorithm in terms of the coverage of the monitoring region.	31
Fig. 3. 16 Comparison of three algorithms in terms of the coverage of the monitoring region.	32
Fig. 4. 1. Cycle T consists of several rounds which consist of two tasks.	40
Fig. 4. 2. Partition of the monitoring region R.	41
Fig. 4. 3. Representative of the sensor.	43
Fig. 4. 4. Representative of the sensor.	46
Fig. 4. 5. Recharging path in the Hm,n.	47
Fig. 4. 6. Virtual sensor replacement	48
Fig. 4. 7. Representative of the sensor.	51
Fig. 4. 8. Workflow of the proposed JDCC.	52
Fig. 4. 9. Simulation environment.	56
Fig. 4. 10. Comparison of three algorithms in terms of the data quality of the monitoring region.	57
Fig. 4. 11. Comparison of three algorithms in terms of the number of working sensors.	58
Fig. 4. 12. Comparison of three algorithms in terms of the data quality of the monitoring region.	58
Fig. 4. 13. Comparison of three algorithms in terms of the number of working sensors.	59
Fig. 4. 14. Comparison of three algorithms in terms of the recharging efficiency.	60
Fig. 4. 15. Comparison of three algorithms in terms of the effectiveness index of working sensors.	60
Fig. 4. 16. Comparison of three algorithms in terms of the effectiveness index of working sensors.	61
Fig. 4. 17. Comparison of three algorithms in terms of the data quality index.	62

List of Tables
Table 2. 1. Comparison Between Algorithms	7
Table 3. 1. Simulation Parameters	22
Table 4. 1. Pseudocode of the proposed JDCC algorithm.	53
Table 4. 2. Simulation Parameters	55

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