請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/55911
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 謝宏昀(Hung-Yun Hsieh) | |
dc.contributor.author | Yun-Da Tsai | en |
dc.contributor.author | 蔡昀達 | zh_TW |
dc.date.accessioned | 2021-06-16T05:10:38Z | - |
dc.date.available | 2016-08-25 | |
dc.date.copyright | 2014-08-25 | |
dc.date.issued | 2014 | |
dc.date.submitted | 2014-08-19 | |
dc.identifier.citation | [1] System improvements for machine-type communications (MTC) (TR23.888),
3GPP Std. TR23.888, Rev. 11.0.0, 09 2012. Online Available at: https://tonic.ee.ntu.edu.tw/depot/sroan/23888-b00.doc [2] Study on provision of low-cost MTC UEs based on LTE (TR36.888), 3GPP Std., Rev. 2.1.1, 06 2013. Online Available at: https://tonic.ee.ntu.edu.tw/ depot/sroan/36888-v211.doc [3] Study on RAN improvements for machine-type communications (MTC) (TR37.868), 3GPP Std., Rev. 11.0.0, 09 2011, 4. Online Available at: https://tonic.ee.ntu.edu.tw/depot/sroan/37868-b00.doc [4] Study on enhancements to machine-type communications (MTC) and other mobile data applications (TR37.869), 3GPP Std., Rev. 0.2.0, 04 2013. Online Available at: https://tonic.ee.ntu.edu.tw/depot/sroan/ 37869-020 FS MTCe RAN.doc [5] IEEE 802.16p draft standards, IEEE Std. P802.16p/D3 Std., Jan. 2012. [6] Z. Fadlullah, M. Fouda, N. Kato, A. Takeuchi, N. Iwasaki, and Y. Nozaki, Toward intelligent machine-to-machine communications in smart grid,' Communications Magazine, IEEE, vol. 49, no. 4, pp. 60{65, 2011. [7] J. Almeida, S. Shintre, M. Boban, and J. Barros, Probabilistic key distribu- tion in vehicular networks with infrastructure support,' in Global Communi- cations Conference (GLOBECOM), 2012 IEEE, 2012, pp. 973{978. [8] M.-Y. Weng, C.-L. Wu, C.-H. Lu, H.-W. Yeh, and L.-C. Fu, Context-aware home energy saving based on energy-prone context,' in Intelligent Robots and Systems (IROS), 2012 IEEE/RSJ International Conference on, 2012, pp. 5233{5238. [9] Z. Fan and S. Tan, M2M communications for e-health: Standards, enabling technologies, and research challenges,' in Medical Information and Commu- nication Technology (ISMICT), 2012 6th International Symposium on, 2012, pp. 1{4. [10] G. Wu, S. Talwar, K. Johnsson, N. Himayat, and K. Johnson, M2M: From mobile to embedded internet,' Communications Magazine, IEEE, vol. 49, no. 4, pp. 36{43, 2011. [11] J. K. Hart and K. Martinez, Environmental sensor networks: A revolution in the earth system science?' Earth-Science Reviews, vol. 78, no. 34, pp. 177 { 191, 2006. Online Available at: http: //www.sciencedirect.com/science/article/pii/S0012825206000511 [12] M. A. Osborne, S. J. Roberts, A. Rogers, and N. R. Jennings, Real-time information processing of environmental sensor network data using bayesian gaussian processes,' ACM Trans. Sen. Netw., vol. 9, no. 1, pp. 1:1{1:32, Nov. 2012. Online Available at: http://doi.acm.org/10.1145/2379799.2379800 [13] Study on provision of low-cost MTC UEs based on LTE (TR36.888), 3GPP Std., Rev. 11.5.0, 06 2012. Online Available at: https: //tonic.ee.ntu.edu.tw/depot/sroan/36888-v211.doc [14] O. D. Incel, A. Ghosh, B. Krishnamachari, and K. Chintalapudi, Fast data collection in tree-based wireless sensor networks,' Mobile Computing, IEEE Transactions on, vol. 11, no. 1, pp. 86{99, 2012. [15] T. M. Chiwewe and G. P. Hancke, A distributed topology control technique for low interference and energy e ciency in wireless sensor networks,' Indus- trial Informatics, IEEE Transactions on, vol. 8, no. 1, pp. 11{19, 2012. [16] S. Guo and Y. Yang, A distributed optimal framework for mobile data gath- ering with concurrent data uploading in wireless sensor networks,' in INFO- COM, 2012 Proceedings IEEE, 2012, pp. 1305{1313. [17] M. Zhao and Y. Yang, An optimization based distributed algorithm for mobile data gathering in wireless sensor networks,' in INFOCOM, 2010 Pro- ceedings IEEE, 2010, pp. 1{5. [18] W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, An application- speci c protocol architecture for wireless microsensor networks,' Wireless Communications, IEEE Transactions on, vol. 1, no. 4, pp. 660{670, 2002. [19] O. Younis and S. Fahmy, Heed: a hybrid, energy-e cient, distributed clus- tering approach for ad hoc sensor networks,' Mobile Computing, IEEE Trans- actions on, vol. 3, no. 4, pp. 366{379, 2004. [20] M. Lot nezhad, B. Liang, and E. Sousa, Adaptive cluster-based data col- lection in sensor networks with direct sink access,' Mobile Computing, IEEE Transactions on, vol. 7, no. 7, pp. 884{897, 2008. [21] A. D. Amis, R. Prakash, T. H. Vuong, and D. T. Huynh, Max-min d-cluster formation in wireless ad hoc networks,' in INFOCOM 2000. Nineteenth An- nual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, vol. 1. IEEE, 2000, pp. 32{41. [22] J. N. Al-Karaki, R. Ul-Mustafa, and A. E. Kamal, Data aggregation and routing in wireless sensor networks: Optimal and heuristic algorithms,' Com- puter networks, vol. 53, no. 7, pp. 945{960, 2009. [23] S. J. Baek, G. De Veciana, and X. Su, Minimizing energy consumption in large-scale sensor networks through distributed data compression and hier- archical aggregation,' Selected Areas in Communications, IEEE Journal on, vol. 22, no. 6, pp. 1130{1140, 2004. [24] P. Wang, R. Dai, and I. F. Akyildiz, Collaborative data compression using clustered source coding for wireless multimedia sensor networks,' in INFO- COM, 2010 Proceedings IEEE. IEEE, 2010, pp. 1{9. [25] | |
dc.identifier.citation | , Visual correlation-based image gathering for wireless multimedia sen-
sor networks,' in INFOCOM, 2011 Proceedings IEEE. IEEE, 2011, pp. 2489{2497. [26] C. Caione, D. Brunelli, and L. Benini, Distributed compressive sampling for lifetime optimization in dense wireless sensor networks,' Industrial Informat- ics, IEEE Transactions on, vol. 8, no. 1, pp. 30{40, 2012. [27] S. C. Ergen and P. Varaiya, Tdma scheduling algorithms for wireless sensor networks,' Wireless Networks, vol. 16, no. 4, pp. 985{997, 2010. [28] T. ElBatt and A. Ephremides, Joint scheduling and power control for wire- less ad hoc networks,' Wireless communications, IEEE Transactions on, vol. 3, no. 1, pp. 74{85, 2004. [29] A. Behzad and I. Rubin, Optimum integrated link scheduling and power control for multihop wireless networks,' Vehicular Technology, IEEE Trans- actions on, vol. 56, no. 1, pp. 194{205, Jan 2007. [30] L. Shi and A. Fapojuwo, Tdma scheduling with optimized energy e ciency and minimum delay in clustered wireless sensor networks,' Mobile Comput- ing, IEEE Transactions on, vol. 9, no. 7, pp. 927{940, 2010. [31] S.-Y. Lien and K.-C. Chen, Massive access management for qos 3gpp machine-to-machine communications,' IEEE Communications letters, vol. 15, no. 3, pp. 311{313, 2011. [32] A. Lioumpas and A. Alexiou, Uplink scheduling for machine-to-machine communications in lte-based cellular systems,' in Proceedings of IEEE Global Communications Conference (GLOBECOM), 2012. [33] C.-H. Chang and H.-Y. Hsieh, Not every bit counts: A resource alloca- tion problem for data gathering in machine-to-machine communications,' in Global Communications Conference (GLOBECOM), 2012 IEEE, 2012, pp. 5537{5543. [34] T. ElBatt and A. Ephremides, Joint scheduling and power control for wire- less ad hoc networks,' Wireless communications, IEEE Transactions on, vol. 3, no. 1, pp. 74{85, 2004. [35] S. Ehsan and B. Hamdaoui, A survey on energy-e cient routing techniques with qos assurances for wireless multimedia sensor networks,' Communica- tions Surveys & Tutorials, IEEE, vol. 14, no. 2, pp. 265{278, 2012. [36] R. Dai and I. F. Akyildiz, A spatial correlation model for visual information in wireless multimedia sensor networks,' Multimedia, IEEE Transactions on, vol. 11, no. 6, pp. 1148{1159, 2009. [37] JMVC Reference software, http://ftp3.itu.int/av-arch/jvt-site/2008 10 Busan/JVT-AC207.zip [38] R. Dai, P. Wang, and I. F. Akyildiz, Correlation-aware qos routing with dif- ferential coding for wireless video sensor networks,' Multimedia, IEEE Trans- actions on, vol. 14, no. 5, pp. 1469{1479, 2012. [39] M. C. Vuran, O. B. Akan, and I. F. Akyildiz, Spatio-temporal correlation: theory and applications for wireless sensor networks,' Comput. Netw., vol. 45, no. 3, pp. 245{259, June 2004. [40] S. E. C. Gregory Gaspari, Construction of correlation functions in two and three dimensions,' Goddard Space Flight Center, 1998, 1. [41] J. Kusuma, L. Doherty, and K. Ramchandran, Distributed compression for sensor networks,' in Image Processing, 2001. Proceedings. 2001 International Conference on, vol. 1, 2001, pp. 82{85 vol.1. [42] R. Cristescu, B. Beferull-Lozano, and M. Vetterli, On network correlated data gathering,' in INFOCOM 2004. Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies, vol. 4, 2004, pp. 2571{ 2582 vol.4. [43] J. O. Berger, V. D. Oliveira, and B. Sanso, Objective bayesian analysis of spatially correlated data,' JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, vol. 96, pp. 1361{1374, 2000. [44] K. Ni, N. Ramanathan, M. N. H. Chehade, L. Balzano, S. Nair, S. Zahedi, E. Kohler, G. Pottie, M. Hansen, and M. Srivastava, Sensor network data fault types,' ACM Trans. Sen. Netw., vol. 5, no. 3, pp. 25:1{25:29, June 2009. Online Available at: http://doi.acm.org/10.1145/1525856.1525863 [45] A. Deshpande, C. Guestrin, S. R. Madden, J. M. Hellerstein, and W. Hong, Model-driven data acquisition in sensor networks,' in Proceedings of the Thirtieth International Conference on Very Large Data Bases - Volume 30, ser. VLDB '04. VLDB Endowment, 2004, pp. 588{599. Online Available at: http://dl.acm.org/citation.cfm?id=1316689.1316741 [46] R. Cheng, J. Chen, M. Mokbel, and C.-Y. Chow, Probabilistic veri ers: Evaluating constrained nearest-neighbor queries over uncertain data,' in Data Engineering, 2008. ICDE 2008. IEEE 24th International Conference on, April 2008, pp. 973{982. [47] A. Krause, C. Guestrin, A. Gupta, and J. Kleinberg, Near-optimal sensor placements: Maximizing information while minimizing communication cost,' in Proceedings of the 5th international conference on Information processing in sensor networks. ACM, 2006, pp. 2{10. [48] S.-E. Wei, H.-Y. Hsieh, and H.-J. Su, Joint optimization of cluster forma- tion and powe control for interference-limited machine-to-machine commu- nications,' in Proceedings of IEEE Global Telecommunications Conference (Globecom), Dec. 2012. [49] K. Lee, J. Shin, Y. Cho, K. S. Ko, D. K. Sung, and H. Shin, A group- based communication scheme based on the location information of mtc devices in cellular networks,' in Communications (ICC), 2012 IEEE International Conference on, 2012, pp. 4899{4903. [50] K. Yuen, B. Liang, and B. Li, A distributed framework for correlated data gathering in sensor networks,' Vehicular Technology, IEEE Transactions on, vol. 57, no. 1, pp. 578{593, 2008. [51] S. Joshi and S. Boyd, Sensor selection via convex optimization,' Signal Pro- cessing, IEEE Transactions on, vol. 57, no. 2, pp. 451{462, 2009. [52] T. M. Cover and J. A. Thomas, Elements of Information Theory. New York, NY, USA: Wiley-Interscience, 1991. [53] A. Goldsmith and S.-G. Chua, Variable-rate variable-power mqam for fading channels,' IEEE Transactions on Communications, vol. 45, no. 10, pp. 1218{ 1230, Oct 1997. [54] T. Sandholm, K. Larson, M. Andersson, O. Shehory, and F. Tohme, Coali- tion structure generation with worst case guarantees,' Arti cial Intelligence, 1999. [55] B. W. Wah and T. Wang, Simulated annealing with asymptotic convergence for nonlinear constrained global optimization,' in Principles and Practice of Constraint Programming. Springer, 1999, pp. 461{475. [56] G. L. Nemhauser and L. A. Wolsey, Integer and combinatorial optimization. Wiley New York, 1988, vol. 18. [57] D. R. Smith, Random trees and the analysis of branch and bound proce- dures,' J. ACM, vol. 31, no. 1, pp. 163{188, Jan. 1984. [58] Blender Online Community, Blender - a 3D modelling and rendering package, Blender Foundation, Blender Institute, Amsterdam, Available at: http://www.blender.org [59] Arnaud Couturier, Suicidator City Generator - Create 3D cities, Online Available at: http://cgchan.com/suicidator [60] R. Yates, A framework for uplink power control in cellular radio systems,' Selected Areas in Communications, IEEE Journal on, vol. 13, no. 7, pp. 1341{ 1347, 1995. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/55911 | - |
dc.description.abstract | 在多數的無線物聯通訊研究中,常僅以支援密集部署的網路情境作為優化
的目標而忽略了物聯裝置所搜集之資料存在著相關性。由於物聯資料相關特性, 無線物聯裝置所要傳送的資料可以透過網路壓縮的技術有效減少,進而增進系統 效能。考量到物聯裝置『節省功耗』以及物聯應用『有效資訊』這兩項特性,在 本論文中我們提出以有效資訊為基準的群組形成與排程演算法協同設計。我們首 先提出兩階層的無線網路傳輸架構並以最小化功率消耗作為目標。接著,我們將 此機制拆解成兩個子問題:『最小化功耗之節點群組問題』以及『最小化功耗之 節點排程問題』。為解決這兩個子問題,我們提出考慮限制條件的模擬退火演算 法(Constrained Simulated Annealing, CSA) 以解決最小化功耗之群組問題。 在形成兩階層的網路群組結構後,最小化功耗之節點排程問題則可以被化簡成為 混和整數非整數之線性規劃問題。因此我們進一步提出一個有效的演算法以求得 此問題之最佳解。我們經由高斯資料模型以及影像資料兩種不同資料來源進行數 值效能分析。透過數據驗證,除了可以說明無線群組技術將可以比直接傳輸獲得 更好的功耗表現之外,本論文所提出的兩階層網路傳輸機制在相關性資料傳輸情 境中也獲得比傳統群組演算法還要好的功耗表現。 | zh_TW |
dc.description.abstract | Clustering and transmission power control have been proposed as an e ective
way to support massive access in wireless machine-to-machine (M2M) networks. For M2M service, the quality of gathered information is a more realistic factor to evaluate the system performance than the link quality of each machine. However, most of the recent work focuses on enhancing the service quality of individual machines but ignore the nature of data correlation among machines (sensors). In this thesis, we rst formulate a problem for 2-tier minimum power data gather- ing in M2M networks. Then, we decompose the problem into two sub-problems: minimal power consumption data-centric clustering and minimal power consump- tion scheduling. We apply Constrained Simulated Annealing (CSA) to solve the cluster formation problem. After the cluster structure is determined, the minimal power consumption scheduling problem can be transformed into a mixed-integer linear programming (MILP) problem. We then proposed a 0-1 branch-and-bound algorithm to obtain the optimal solution. To evaluate our proposed transmission scheme, we consider both Gaussian data source and real image data. Evaluation results show that the proposed scheme achieves better performance than conven- tional clustering algorithms. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T05:10:38Z (GMT). No. of bitstreams: 1 ntu-103-R01942053-1.pdf: 3096414 bytes, checksum: 3993fbc0bf3ead9822386d936eaf3b01 (MD5) Previous issue date: 2014 | en |
dc.description.tableofcontents | TABLE OF CONTENTS
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii CHAPTER 1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . 1 CHAPTER 2 BACKGROUND AND RELATED WORK . . . . . 6 2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.1 Temporal Correlation Function . . . . . . . . . . . . . . . . 6 2.1.2 Spatial Correlation Function . . . . . . . . . . . . . . . . . 7 2.1.3 Model-based Data Gathering . . . . . . . . . . . . . . . . . 7 2.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.1 Cluster Formation in Wireless Sensor Network . . . . . . . 8 2.2.2 Scheduling in Wireless Sensor Network . . . . . . . . . . . 10 CHAPTER 3 SCENARIO AND PROBLEM FORMULATION . 13 3.1 Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.1 Tier-2 Transmission . . . . . . . . . . . . . . . . . . . . . . 15 3.1.2 Tier-1 Transmission . . . . . . . . . . . . . . . . . . . . . . 15 3.1.3 Network Structure Summary . . . . . . . . . . . . . . . . . 16 3.2 Data Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2.1 Data Compression Model . . . . . . . . . . . . . . . . . . . 17 3.3 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.3.1 Cluster Formation . . . . . . . . . . . . . . . . . . . . . . . 17 3.3.2 Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.3.3 Power Control . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.3.4 Tier-2 Transmission . . . . . . . . . . . . . . . . . . . . . . 19 3.3.5 Tier-1 Transmission . . . . . . . . . . . . . . . . . . . . . . 20 3.3.6 Correlation Aware Machine Selection . . . . . . . . . . . . 21 3.3.7 Minimal Power Consumption Data Gathering Problem . . . 21 3.4 Decomposition of Minimal Power Consumption Data Gathering Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.4.1 Minimal Power Consumption Clustering Sub-Problem . . . 24 3.4.2 Minimal Power Consumption Scheduling Sub-Problem . . . 25 CHAPTER 4 MINIMAL POWER CONSUMPTION CLUSTER- ING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 4.1 Tier-1 Power Minimization . . . . . . . . . . . . . . . . . . . . . . 26 4.2 Constrained Simulate Annealing . . . . . . . . . . . . . . . . . . . 28 4.2.1 Penalizing The Objective Function . . . . . . . . . . . . . . 29 4.2.2 Initial Solution . . . . . . . . . . . . . . . . . . . . . . . . . 30 4.2.3 Neighborhood Denition . . . . . . . . . . . . . . . . . . . 31 4.2.4 Movement Conrmation Mechanism . . . . . . . . . . . . . 33 4.2.5 Stop Criterion . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.3 Analysis of Constraint Global Minimum . . . . . . . . . . . . . . . 35 CHAPTER 5 MINIMAL POWER CONSUMPTION SCHEDUL- ING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 5.1 Linearization of Minimal Power Consumption Sub-problem . . . . 37 5.1.1 Interference Constraints . . . . . . . . . . . . . . . . . . . . 37 5.1.2 Selection Constraints . . . . . . . . . . . . . . . . . . . . . 38 5.1.3 Objective . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 5.1.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 5.2 Problem Solving . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 5.2.1 Branch and Bound Algorithm . . . . . . . . . . . . . . . . 40 5.3 Complexity Analysis of 0-1 Branch-and-bound Algorithm . . . . . 42 CHAPTER 6 PERFORMANCE EVALUATION . . . . . . . . . . 44 6.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 6.2 Impact on Solution Quality . . . . . . . . . . . . . . . . . . . . . . 44 6.2.1 Convergence Analysis of Constrained Simulated Annealing 44 6.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 6.3.1 Demonstration of Solution . . . . . . . . . . . . . . . . . . 47 6.3.2 Impact of Fidelity Constraint . . . . . . . . . . . . . . . . . 49 6.3.3 Impact of Tier-1 Time Resource Constraint . . . . . . . . . 50 6.3.4 Impact of Tier-2 Slot Number Constraint . . . . . . . . . . 50 6.4 Performance Evaluation in Image Transmission . . . . . . . . . . . 51 6.4.1 Experiment Setup . . . . . . . . . . . . . . . . . . . . . . . 52 6.4.2 K-means Clustering Algorithm . . . . . . . . . . . . . . . . 54 6.4.3 Performance Evaluation . . . . . . . . . . . . . . . . . . . . 54 CHAPTER 7 CONCLUSION AND FUTURE WORK . . . . . . 58 REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 | |
dc.language.iso | en | |
dc.title | 物聯通訊下考慮資料相關性之群組形成與排程演算法協同設計 | zh_TW |
dc.title | Joint Clustering and Scheduling for Correlated Data Gathering in Machine-to-Machine Wireless Networks | en |
dc.type | Thesis | |
dc.date.schoolyear | 102-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 魏宏宇(Hung-Yu Wei),周俊廷(Chun-Ting Chou) | |
dc.subject.keyword | 無線網路,物聯網,感知無線網路,最佳化, | zh_TW |
dc.subject.keyword | Wireless Network,Machine-to-Machine Network,Wireless Sensor Network,Optimization, | en |
dc.relation.page | 63 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2014-08-19 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
顯示於系所單位: | 電信工程學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-103-1.pdf 目前未授權公開取用 | 3.02 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。