近年來，無線感測器網路（WSN）在現實生活中得到了廣泛的應用。無線感測器網路由電池有限的微型感測器組成。因此，對無線感測器進行充電是一個很有前景的課題，近年來受到了廣泛的關注。
借助無線電力傳輸（WPT）技術，行動充電器(MC)將電量傳遞給感測器。該技術為延長無線可充電無線感測器網路(WRSNs) 的使用壽命提供了一種新的解決方案。現有文獻大多認為感測器同等重要，構建的充電路徑演算法主要是用來增加充電的感測器數量或減少充電的路徑長度。然而，不同感測器的覆蓋貢獻是不同的，對覆蓋貢獻較大的感測器進行充電，可獲得更好的監測品質。本文提出的充電演算法分為初始化、充電排程和路徑構建三個階段。在第二階段，本文提出了兩種充電排程演算法，即成本效益（CE）演算法和考慮覆蓋和公平性的成本效益（C2F）演算法。行動充電器利用以上兩種演算法，計算覆蓋貢獻及路徑成本，覆蓋貢獻大、路徑成本低的的感測器具有更高的充電權重。行動充電器將按照感測器的充電權重，由高到低來構建充電路徑。實驗結果表明，與現有研究相比，CE和C2F演算法在充電公平性、充電穩定性和覆蓋率方面具有更好的性能。
上述CE和C2F沒有考慮需要充電的感測器的空間和時間品質。於是，本文提出了一種最大化時空資料精準度的充值排程演算法，稱爲RS-STQ，旨在最大化給定網路的資料精準度。該演算法首先將需要充電的感測器的空間品質納入考量；此後提出一種能量管理策略，使每個需充電的感測器能夠局部調整感測的時間序列，以提高其時間品質貢獻。實際應用中，感測器的能量消耗率可能不同，因此本文為感測器制定了一個自我調整的充電請求閾值，以便適用實際情形。實驗結果表明，與現有研究相比，該演算法在資料精準度和充電感測器的空間品質方面具有更好的性能。
針對RS-STQ不適用於密集感測器網路的特點，本文提出了一種基於工作負載感知的充電排程演算法(WLARS)。該演算法將需要充電的感測器在空間和時間上的監控品質納入考量，並以最大化網路的監控品質為目標，對行動充電器進行充電排程。WLARS首先根據感測器的傳輸負載將網路劃分為多個子區域，旨在均勻分配各行動充電器的充電負載。此後，每個感測器根據排隊理論確定自己的充電請求閾值。另外，本文將各行動充電器之間的自我調整緩衝和相鄰行動充電器之間的合作納入考量。實驗結果表明，WLARS在空間和時間監控品質、感測器的平均等待時間和未及時充電引起的空間品質損失方面均優於現有研究。

Wireless sensor networks (WSNs) are used in many real-life applications in recent days. WSNs consists of tiny sensors with limited battery. Therefore, energy recharging to the sensors has been a promising issue and received much attention recently. 
With the help of wireless power transfer (WPT) technology, the mobile charger (MC) can transfer energy to the sensor nodes. This technology provides a new solution to prolong the lifetime of wireless rechargeable sensor networks (WRSNs). In literature, many recharging path construction algorithms have been proposed. Most of them considered that all sensors are equally important and designed algorithms to increase the number of recharged sensors or decrease the path length of the MC. However, different sensors have different coverage contributions. Recharging the sensors with a larger coverage contribution can achieve better surveillance quality. The proposed recharging scheduling algorithm is divided into three phases, including the Initialization, Recharging Scheduling and Path Construction Phases. In the second phase, this study proposed two recharging scheduling algorithms, namely the Cost-Effective (CE) algorithm and Cost-Effective with Considerations of Coverage and Fairness (C^2 F) algorithm. The proposed two algorithms construct paths for MC and select the recharging sensors based on the higher weight in terms of larger coverage contribution and smaller path cost. Performance results show that the CE and C^2 F algorithms yield better performance in terms of the fairness of recharging, recharging stability and coverage ratio, as compared with the existing studies.
The above CE and C^2 F does not consider the spatial and temporal qualities of the recharging requested sensors. This study proposes a Recharge Scheduling Algorithm for Maximizing Spatial and Temporal Data Accuracy called RS-STQ, aiming to maximize the data accuracy of the given network. The proposed recharging schedule considers the spatial quality contribution of each recharging requested sensor. In addition, an energy management strategy is proposed for each requested sensor to locally adjust the sensing time sequence, aiming to improve the temporal quality. Each sensor might have a different energy consumption rate, therefore this study also formulates an adaptive recharging request threshold for the sensor nodes, which is suitable for real applications. The experimental study shows that the proposed algorithm outperforms the literature in terms of data accuracy as well as recharged sensor’s spatial quality contributions.
Since the RS-STQ is not suitable for the dense sensor networks, this study proposes a work-load aware recharge scheduling algorithm (WLARS) which aims to maximize the surveillance quality of the network by considering the spatial and temporal surveillance qualities of the recharging requested sensors and schedules the MC for recharging. The proposed WLARS firstly partitions the network into many sub-regions by considering the routing load of the sensors, aiming to evenly distribute the charging load of the MCs. Then each sensor determines its threshold value based on the queuing theory. In addition, the adaptive buffer of the MC and cooperation between the neighbouring MCs is also considered in this study. Performance results show that the WLARS outperforms the existing mechanism in terms of spatial and temporal surveillance qualities, average waiting time as well as uncharged sensors spatial quality loss. 

Outline	V
List of Figures	VIII
List of Tables	X
Chapter 1. Introduction	1
1. 1 Background	1
1. 2 Research Goals	4
1. 3 Organization of the Thesis	5
Chapter 2. Related Work	6
2.1 Energy Recharging Technologies	6
2.1.1 Environmental Energy Harvesting	6
2.1.2 Energy Replenishment from Mobile Chargers	6
2.2 Recharging Approaches	8
2.2.1 Offline Recharging Approaches	8
2.2.2 Online Recharging Approaches	9
Chapter 3. The Coverage Aware Recharging Algorithm	12
3.1 Assumptions and Problem Statement	13
3.1.1 Network Environment	13
3.1.2 Problem Statement	13
3.2 The Design of Coverage-Aware Recharging Algorithm	16
3.2.1 Initialization Phase	17
3.2.2 Recharging Scheduling Phase	18
3.2.3 Path Construction Phase	26
3.3 Simulation	26
3.3.1 Simulation Settings	27
3.3.2 Simulation Results	27
3.4 Summary	34
Chapter 4. Recharge Scheduling Algorithm (RS-STQ)	36
4.1 Assumptions and Problem Statement	37
4.1.1 Network Environment	37
4.1.2 Sensor Recharging Model	37
4.1.3 Data Accuracy Model and Problem Statement	39
4.2 The Design of RS-STQ	44
4.2.1 Threshold Determination (TD) Phase	45
4.2.2 Recharging Path Construction (RPC) Phase	47
4.2.3 Sensing Rate Calculation (SRC) Phase	52
4.3 Simulation	55
4.3.1 Simulation Environment	55
4.3.2 Simulation Results	55
4.4 Summary	63
Chapter 5. The Work Load Aware Recharging Mechanism (WLARS)	64
5.1 Assumptions and Problem Statement	64
5.1.1 System Model and Assumptions	65
5.1.2 Recharging Model	65
5.1.3 Problem Statement	67
5.2 The Design of WLARS	70
5.2.1 Network Planning Phase	71
5.2.2 Recharging Request Determination Phase	72
5.2.3 Adaptive Buffer and Cooperation Phase	78
5.3 Simulation of WLARS	82
5.3.1 Simulation Environment	82
5.3.2 Experimental Results	83
5.4. Summary	86
Chapter 6. Conclusion and Future Work	87
References	89

List of Figures
Fig. 1. 1. The spatial and temporal distances will impact the data accuracy...	3
Fig. 3. 1. The scenario of the considered network environment...	17 
Fig. 3. 2. The sensing and charging ranges of the sensor〖 s〗_i...	17
Fig. 3. 3. An example to illustrate the recharging and sensing range of grid..	18 
Fig. 3. 4. CE algorithm, M finds the next visited location....	22
Fig. 3. 5. C^2𝐅 algorithm, 𝑴 chooses the best grid....	24 
Fig. 3. 6. The flowchart of the proposed CE and C^2𝐅 algorithms.....	26
Fig. 3. 7. The recharging path construction of CE and C^2𝐅 algorithms...	28 
Fig. 3. 8. Performance comparison of surveillance quality for C^2𝐅, CE, HSA-DFWA and SDT algorithms....	29
Fig. 3. 9. Performance comparison for C^2 F, CE, HSA-DFWA and SDT by varying the grid size..	30 
Fig. 3. 10. Performance comparison for different deployment policies....	31
Fig. 3. 11. Performance comparison of fairness index of recharging....	32 
Fig. 3. 12. Performance comparison of ADQ of recharged sensors....	32
Fig. 3. 13. Comparison of recharging stability of C^2 F, CE, HSA-DFWA and SDT algorithms..	34 
Fig. 3. 14. Performance comparison of coverage ratio for C^2 F, CE, HSA-DFWA and SDT algorithms...	34
Fig. 4. 1. The considered network environment...	39 
Fig. 4. 2. An example to represent the spatial and temporal qualities....	43
Fig. 4. 3. An example path constructed by MC adopting the SPU strategy..	51 
Fig. 4. 4. An example to present the cooperative sensing between neighbors....	53
Fig. 4. 5. The sensing time sequence adjustment of neighbouring sensors..	54 
Fig. 4. 6. Performance comparison of total data accuracy....	56
Fig. 4. 7. Performance comparison by varying the threshold value e^min and value of 𝛼.. .	57 
Fig. 4. 8. Performance comparison of EUE and AWT algorithms....	58
Fig. 4. 9. Performance comparison of RSSQ and total data accuracy...	59
Fig. 4. 10. The total data accuracy of the network considering the SPU and MPU strategies.	61
Fig. 4. 11. Performance comparison of Uncharged Sensors Data Accuracy Loss (DAL)...	62 
Fig. 4. 12. Performance comparison of control packet overhead....	62
Fig. 5. 1. The network environment of the proposed work..	66 
Fig. 5. 2. Surveillance quality representation from space and time point of view....	68
Fig. 5. 3. The network partitioning approach with m=5 showing (a) Random partitioning (b) Axis rotated by 〖300〗^(°" " )....	71
Fig. 5. 4. An example of path construction and scheduling of M^k.	74
Fig. 5. 5. The busy levels of M^k and duration check of s_i^k....	76 
Fig. 5. 6. The sensing rate frequency adjustment of the sensor s_i^k....	78
Fig. 5. 7. The states diagram of a sensor s_i^k...	78
Fig. 5. 8. The adaptive buffer of M^k...	80
Fig. 5. 9. The collaborative schedule between the neighbouring MCs.....	82 
Fig. 5. 10. Performance comparison of traveling distance of MC......	84
Fig. 5. 11. Comparison of average waiting time....	84 



List of Tables
Table 2. 1. Comparison of the coverage aware mechanism with the related studies.	8
Table 2. 2. Comparison of RS-STQ and WLARS with the existing related works....	11 
Table 3. 1. Simulation Parameters....	27

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