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作者(中文):鄭瑞翔
作者(外文):Cheng, Ray-Hsinag
論文名稱(中文):無人機網路中基於動態拓樸的路徑規劃和節省功耗的軌跡調整
論文名稱(外文):Temporal Routing and Power-Efficient Trajectory Adjustment in Unmanned Aerial Vehicle Networks
指導教授(中文):許健平
指導教授(外文):Sheu, Jang-Ping
口試委員(中文):洪樂文
王志宇
口試委員(外文):Hong, Yao-Win
Wang, Chih-Yu
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系所
學號:105064526
出版年(民國):107
畢業學年度:107
語文別:英文
論文頁數:29
中文關鍵詞:無人機節能路由演算法飛行隨意網路
外文關鍵詞:Unmanned Aerial VehiclePower SavingRouting ProtocolFlying Ad-Hoc Networks
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無人機因為擁有高機動性,在空中飛行也使得他不會受到道路的限制、或是信號的干擾,因此無人機網路的發展是最近在網路的一個重大趨勢。在本篇論文中,無人機用於監視或收集區域中的感測器產生的數據。假設每台無人機遵循圓形飛行軌跡以維持其負責區域的覆蓋範圍。我們為這樣的無人機網路提出了節能軌跡的調整方案。首先建立網路中動態的無人機間連接拓樸關係,並採用兩種不同路由演算法,分別是最早到達路徑算法和最小成本路徑算法,來確定每台無人機與數據採集站之間的傳輸路徑。接下來,在受到這些路徑存在的約束情況下,進一步優化無人機的圓形飛行軌跡的半徑。在此框架下考慮我們兩個目標:總消耗功率最小化和最大化網路壽命。根據模擬結果顯示,在所提出的軌跡調整策略中,總功耗和網路壽命方面的性能都得到了顯著提高,並且最小成本路由演算法在軌跡調整期間提供了更大的靈活性,因此比最早的到達時間算法表現要好。
This thesis proposes power-efficient trajectory adjustment schemes for a network of unmanned aerial vehicles (UAV) deployed to monitor or gather data from underlying sensors in the field. Each UAV is assumed to follow a circular flight trajectory chosen to maintain coverage over its responsible region within the sensor field. Specifically, the relationship between routing in UAV networks and that in general temporal graphs is first established. Then, two temporal routing algorithms, namely, the earliest arrival path algorithm and the minimum cost path algorithm, are adopted to determine the transmission paths between each UAV and the data-gathering base-station. Next, the radius of the UAVs’ circular trajectories is further optimized subject to constraints on the existence of these temporal paths. Two problem formulations are considered under this framework: the total power minimization problem consumption and the maximum network lifetime problem. Numerical simulations show that the performance in terms of the total power consumption and the network lifetime are both improved significantly with the proposed trajectory adjustment strategies and that the minimum cost routing algorithm provides more flexibility during trajectory adjustment and, thus, achieves lower power consumption than the earliest arrival time algorithm.
Introduction 1
Related Work 4
System model 6
Temporal Graph Routing Algorithm 9
Power Efficient Trajectory Adjustment 13
Strategy I: Total Power Minimization 15
Strategy II: Lifetime Maximization 16
Numerical Results and Performance Comparisons 18
Varying Number of UAVs 18
Varying Delay Tolerance λ 22
Conclusion 27
References 28
[1] Y. Zeng, R. Zhang, and T. J. Lim, “Wireless communications with unmanned aerial vehicles: opportunities and challenges,” IEEE Communications Magazine, vol. 54, no. 5, pp. 36–42, May 2016.
[2] L. Gupta, R. Jain, and G. Vaszkun, “Survey of important issues in UAV communication networks,” IEEE Communications Surveys Tutorials, vol. 18, no. 2, pp. 1123–1152, Second Quarter 2016.
[3] M. Erdelj, E. Natalizio, K. R. Chowdhury, and I. F. Akyildiz, “Help from the sky: Leveraging UAVs for disaster management,” IEEE Pervasive Computing, vol. 16, no. 1, pp. 24–32, Jan.-Mar. 2017.
[4] J. Deruyck, M. Wyckmans, W. Joseph, and L. Martens, “Designing UAV-aided emergency networks for large-scale disaster scenarios,” EURASIP Journal on Wireless Communications and Networking, vol. 2018, no. 1, p. 79, Apr. 2018.
[5] Y. Zhou, N. Cheng, N. Lu, and X. S. Shen, “Multi-UAV-aided networks: Aerial-ground cooperative vehicular networking architecture,” IEEE Vehicular Technology Magazine, vol. 10, no. 4, pp. 36–44, Dec. 2015
[6] D.-T. Ho, E. I. Grøtli, P. B. Sujit, T. A. Johansen, and J. a. B. Sousa, “Optimization of wireless sensor network and UAV data acquisition,” Journal of Intelligent & Robotic Systems, vol. 78, no. 1, Apr. 2015.
[7] C. Zhan, Y. Zeng, and R. Zhang, “Energy-efficient data collection in UAV enabled wireless sensor network,” IEEE Wireless Communications Letters, vol. 7, no. 3, pp. 328–331, Jun. 2018.
[8] J. Tisdale, Z. Kim, and J. K. Hedrick, “Autonomous UAV path planning and estimation,” IEEE Robotics Automation Magazine, vol. 16, no. 2, pp. 35–42, Jun. 2009.
[9] Q. Yang and S. Yoo, “Optimal UAV path planning: Sensing data acquisition over IOT sensor networks using multi-objective bio-inspired algorithms,” IEEE Access, vol. 6, pp. 13 671–13 684, 2018.
[10] J. Liu, X. Wang, B. Bai, and H. Dai, “Age-optimal trajectory planning for UAV-assisted data collection,” in Proceedings of IEEE Conference on Computer Communications Workshops, (INFOCOM Workshops), pp. 553–558, Honolulu, HI, USA, Apr. 2018.
[11] Z. M. Fadlullah, D. Takaishi, H. Nishiyama, N. Kato, and R. Miura, “A dynamic trajectory control algorithm for improving the communication throughput and delay in UAV-aided networks,” IEEE Network, vol. 30, no. 1, pp. 100–105, Jan. 2016.
[12] S. Rosati, K. Kruelecki, L. Traynard, and B. Rimoldi, “Speed-aware routing for UAV ad-hoc networks,” in Proceedings of IEEE Globecom Workshops, pp. 1367–1373, Atlanta, GA, USA, Dec. 2013.
[13] Y. Zheng, Y. Wang, Z. Li, L. Dong, Y. Jiang, and H. Zhang, “A mobility and load aware OLSR routing protocol for UAV mobile ad- hoc networks,” in Proceedings of International Conference on Information and Communications Technologies (ICT), pp. 1–7, Nanjing, China, May 2014.
[14] B. Sliwa, D. Behnke, C. Ide, and C. Wietfeld, “B.a.t.mobile: Leveraging mobility control knowledge for efficient routing in mobile robotic networks,” in Proceedings of IEEE Globecom Workshops, pp. 1–6, Washington, DC, USA, Dec. 2016.
[15] R. Shirani, M. St-Hilaire, T. Kunz, Y. Zhou, J. Li, and L. Lamont, “On the delay of reactive-greedy-reactive routing in unmanned aeronautical ad-hoc networks,” Procedia Computer Science, vol. 10, pp. 535 – 542, 2012.
[16] C. E. Perkins and E. M. Royer, “Ad-hoc on-demand distance vector routing,” in Proceedings of IEEE Workshop on Mobile Computing Systems and Applications (WMCSA), pp. 90–100, New Orleans, LA, USA, Feb. 1999.
[17] R. Shirani, M. St-Hilaire, T. Kunz, Y. Zhou, J. Li, and L. Lamont, “The performance of greedy geographic forwarding in unmanned aeronautical ad-hoc networks,” in Proceedings of Annual Communication Networks and Services Research Conference, pp. 161–166, Ottawa, ON, Canada, May 2011.
[18] J. Jiang and G. Han, “Routing protocols for unmanned aerial vehicles,” IEEE Communications Magazine, vol. 56, no. 1, pp. 58–63, Jan. 2018.
[19] H. Wu, J. Cheng, S. Huang, Y. Ke, Y. Lu, and Y. Xu, “Path problems in temporal graphs,” Proceedings of VLDB Endow., vol. 7, no. 9, pp. 721–732, May 2014.
[20] J. Nocedal and S. J. Wright, Sequential Quadratic Programming. New York, NY: Springer New York, pp. 529–562, 2006.
[21] S. Boyd and L. Vandenberghe, Convex Optimization. New York, NY, USA: Cambridge University Press, 2004.
[22] Y. Zeng and R. Zhang, “Energy-efficient UAV communication with trajectory optimization,” IEEE Trans. Wireless Communication., vol. 16, no. 6, pp. 3747–3760, Jun. 2017.
[23] M. Grant and S. Boyd, “CVX: Matlab software for disciplined convex programming, version 2.1,” http://cvxr.com/cvx, Mar. 2014.
 
 
 
 
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