近年來能源採集技術逐漸成熟，越來越多無線感測器都可以藉由轉換不同的能源對電池進行充電，讓整體無線感測網路能夠透過感測器的充電來延長網路生命。然而採集能源收穫量會因為無線感測器分布之地點、時間差異而有所不同，因此規劃良好的資料收集方法變得更加困難。無線感測網路為了能夠永續運作，會透過不同的方式來延長感測器之壽命，來避免感測器耗盡電池電量而造成部分連結中斷以及資料收集不完整。此外，為了提升網路效能，許多研究都將整體網路不中斷作為必要條件，將感測器的電量有效地利用及分配，使感測器可以輸出最大的資料收集率，藉此提升整體網路資料收集量。

本研究將再生能源當作電量來源，目的是在再生能源收穫量已知的前提下，快速地建立一棵資料收集樹以及各感測器相對應的資料收集率。該資料收集樹中每一個感測器在所有的工作時段內皆不會耗盡自身電池電量，且輸出最大資料收集率，即為整體網路的資料收集率辭典編纂排序最大化。而本論文提出的建樹方法為分散式的啟發式演算法，首先使用一任意樹作為起始樹狀結構，接著過透過不斷提升感測器節點的資料收集率來調整樹狀結構，加快建樹速度並提升資料收集率之辭典編纂排序。

最後實驗顯示本研究提出的演算法能夠加速求解時間，並且能夠求得一組高辭典編纂排序的資料收集率，而隨著無線感測網路越大，能夠加速的時間也越多。

With the new energy-harvesting technology, wireless sensors can recharge batteries by converting energy from different kinds of renewable sources. Then the energy-harvesting wireless networks (EH-WSN) can prolong lifetime by recharging batteries of wireless sensors. However, the renewable energy will be different based on the locations and the time periods of wireless sensors. Therefore, if there is no efficient method to manage and utilize renewable energy, some sensors will run out of battery and interrupt the operations of data collection.

In this thesis, a data collection algorithm is proposed to compute a routing structure and a high lexicographic rate assignment rapidly under the premise that the renewable energy data are given. The routing structure is constructed by adjusting the data collection rate and routing structure.

Besides network lifetime, data collection rate is also an important criterion. Most studies focus on raising the data collection rate under the premise that wireless sensor network won't be terminated due to depletion of some batteries. And these methods can roughly be classified into two categories: maximizing the total data collection rate of all wireless sensors and maximizing lexicographic data collection rate assignment.

In the routing structure, each wireless sensor will not deplete the battery at any time between one day. The algorithm we proposed to construct the routing structure.

Bao, X., & Ding, G. (2016). An routing algorithm for maximizing network performace in energy harvesting wireless sensor network. In Information science and control engineering (icisce), 2016 3rd international conference on (pp. 1267–1270).

Chen, S., Fang, Y., & Xia, Y. (2007). Lexicographic maxmin fairness for data collection in wireless sensor networks. IEEE Transactions on Mobile Computing, 6(7), 762–776.

Chen, Y.-C. (2017). Robust data collection for energy-harvesting wirelesssensor networks (Unpublished master’s thesis). National Cheng Kung University.

Dong, Y., Wang, J., Shim, B., & Kim, D. I. (2016). Dearer: A distance-and-energyaware routing with energy reservation for energy harvesting wireless sensor networks. IEEE Journal on Selected Areas in Communications, 34(12), 3798–3813.

He, J., Ji, S., Pan, Y., & Li, Y. (2014). Constructing load-balanced data aggregation trees in probabilistic wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 25(7), 1681–1690.

Jaffe, J. (1981). Bottleneck flow control. IEEE Transactions on Communications, 29(7), 954–962.

Kansal, A., Hsu, J., Zahedi, S., & Srivastava, M. B. (2007). Power management in energy harvesting sensor networks. ACM Transactions on Embedded Computing Systems (TECS), 6(4), 32.

Li, J., & Liu, D. (2016). An energy aware distributed clustering routing protocol for energy harvesting wireless sensor networks. In Communications in china (iccc), 2016
ieee/cic international conference on (pp. 1–6).

Liu, R.-S., Fan, K.-W., Zheng, Z., & Sinha, P. (2011). Perpetual and fair data collection for environmental energy harvesting sensor networks. IEEE/ACM Transactions on Networking, 19(4), 947–960.

Mansourkiaie, F., Ismail, L. S., Elfouly, T. M., & Ahmed, M. H. (2017). Maximizing lifetime in wireless sensor network for structural health monitoring with and without energy harvesting. IEEE Access, 5, 2383–2395.

Mao, S., Cheung, M. H., & Wong, V. W. (2012). An optimal energy allocation algorithm for energy harvesting wireless sensor networks. In Communications (icc), 2012 ieee international conference on (pp. 265–270).

Martinez, G., Li, S., & Zhou, C. (2014). Wastage-aware routing in energy-harvesting wireless sensor networks. IEEE Sensors Journal, 14(9), 2967–2974.

Phayung, M., Herwigm, U., & Sirapat, B. (2013). The 9th international conference on computing and informationtechnology. Springer Science Business Media.

Shafieirad, H., Adve, R. S., & ShahbazPanahi, S. (2016). Opportunistic routing in large-scale energy harvesting sensor networks. In Globecom workshops (gc wkshps), 2016 ieee (pp. 1–6).

Sharma, V., Mukherji, U., Joseph, V., & Gupta, S. (2010). Optimal energy management policies for energy harvesting sensor nodes. IEEE Transactions on Wireless Communications, 9(4).

Stoffel, T., & Andreas, A. (1981). Nrel solar radiation research laboratory (srrl): Baseline measurement system (bms); golden, colorado (data) (Tech. Rep. No. DA- 5500-56488). National Renewable Energy Lab.(NREL), Golden, CO (United States). Retrieved from http://www.osti.gov/scitech/servlets/purl/1052221

Sudevalayam, S., & Kulkarni, P. (2011). Energy harvesting sensor nodes: Survey and implications. IEEE Communications Surveys & Tutorials, 13(3), 443–461.

Sun, J. (2010). Car battery efficiencies. Stanford University Course Work, Physics, 240, 100.

Taneja, J., Jeong, J., & Culler, D. (2008). Design, modeling, and capacity planning for micro-solar power sensor networks. In Proceedings of the 7th international conference on information processing in sensor networks (pp. 407–418).

Vigorito, C. M., Ganesan, D., & Barto, A. G. (2007). Adaptive control of duty cycling in energy-harvesting wireless sensor networks. In Sensor, mesh and ad hoc communications and networks, 2007. secon’07. 4th annual ieee communications society conference on (pp. 21–30).

Wu, Y., Fahmy, S., & Shroff, N. B. (2008). On the construction of a maximumlifetime data gathering tree in sensor networks: Np-completeness and approximation algorithm. In Infocom 2008. the 27th conference on computer communications. ieee (pp. 356–360).

Ye, W., Heidemann, J., & Estrin, D. (2002). An energy-efficient mac protocol for wireless sensor networks. In Infocom 2002. twenty-first annual joint conference of the ieee computer and communications societies. proceedings. ieee (Vol. 3, pp. 1567–1576).

Ye, W., Heidemann, J., & Estrin, D. (2004). Medium access control with coordinated adaptive sleeping for wireless sensor networks. IEEE/ACM Transactions on Networking (ToN), 12(3), 493–506.

Yetgin, H., Cheung, K. T. K., El-Hajjar, M., & Hanzo, L. H. (2017). A survey of network lifetime maximization techniques in wireless sensor networks. IEEE Communications Surveys & Tutorials, 19(2), 828–854.

Zeng, B., & Zhao, L. (2013). Solving two-stage robust optimization problems using a column-and-constraint generation method. Operations Research Letters, 41(5), 457–
461.

Zhang, B., Simon, R., & Aydin, H. (2011). Maximum utility rate allocation for energy harvesting wireless sensor networks. In Proceedings of the 14th acm international
conference on modeling, analysis and simulation of wireless and mobile systems (pp.7–16).

Zhang, Y., He, S., & Chen, J. (2016). Data gathering optimization by dynamic sensing and routing in rechargeable sensor networks. IEEE/ACM Transactions on Networking, 24(3), 1632–1646.

Zhang, Y., He, S., Chen, J., Sun, Y., & Shen, X. S. (2013). Distributed sampling rate control for rechargeable sensor nodes with limited battery capacity. IEEE Transactions
on Wireless Communications, 12(6), 3096–3106.

Zhu, J., Roy, S., Guo, X., & Conner, W. S. (2005). Maximizing aggregate throughput in 802.11 mesh networks with physical carrier sensing and two-radio multi-channel clustering. In Advances in pervasive computing and networking (pp.137–166).
Springer.