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研究生: 董一志
Tung, Yi-Chih
論文名稱: 智慧家庭聯網之 QoS 資源調配的匯聚策略
Rendezvous Strategy of QoS Provisioning over Intelligent Home Networks
指導教授: 黃文吉
Hwang, Wen-Jyi
學位類別: 博士
Doctor
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2017
畢業學年度: 105
語文別: 英文
論文頁數: 65
中文關鍵詞: QoSGRNNHome NetworkQuality of ServiceChannel allocationPrediction algorithmsBandwidthHome automationAlgorithm design and analysisTrainingNetwork Access Device
英文關鍵詞: QoS, GRNN, Home Network, Quality of Service, Channel allocation, Prediction algorithms, Bandwidth, Home automation, Algorithm design and analysis, Training, Network Access Device
DOI URL: https://doi.org/10.6345/NTNU202202402
論文種類: 學術論文
相關次數: 點閱:78下載:0
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    This dissertation recommends a novel rendezvous strategy for QoS pro- visioning with minimum computing cost over intelligent home networks. Although the usual QoS algorithms such as ow control can be adapted for simplifying the bandwidth allocation, the methods are limited only for local services. The proposed algorithm, termed GRNN QoS (Generalized Regression Neural Network for Quality of Service), is able to provide global home network services while requiring minimum computing complexity. The GRNN QoS is a hybrid combination of ow control and path selection. The ow control is adopted for the adjustment and/or allocation of local bandwidth; whereas, the path selection is used for the collection and/or delivery of local network information to the home networks. With GRNN computation, services provided by the home networks are then global optimality. This algorithm is well-suited for intelligent home networks where the fast QoS provisioning and low computing efforts of the small scale home networks are desired.

    GRNN QoS establishing profiles of user’s various responses to the communication links to different services is the first step for GRNN QoS algorithm. Based on the profiles GRNN QoS can estimate the future feedback to the services from a user. Upon the bandwidth allocations for receiving positive feedback, the algorithm finds a way to minimize the bandwidth usage of the networks. The main advantages of this algorithm are that it quickly adapts to user response and it does not require offline training. This dissertation provides both analytical and numerical analyses to demonstrate the efficacy of said algorithm.

    Contents Acknowledgments i Abstract ii Contents iii List of Figures v List of Tables viii 1 Introduction 1 2 Strategy 5 2.1 Wire and Wireless Relay Network 5 2.2 SON:Self Organizing Network 7 2.3 Path Selection 9 2.4 Flow Control 12 3 GRNN Algorithm 15 General Regression NeuralNetwork 15 4 Proposed Algorithm 18 TheProposed Algorithm: GRNN QoS 18 5 Analytical Analysis 25 GRNN QoS Analytical Analysis 25 6 Experimental Results 31 6.1 Numerical results pertaining to the Proposed Algorithm 31 6.2 More Numerical Examples 37 6.3 Numerical Comparison With the ANN Algorithm 51 6.4 Single-ServiceExperiments 52 7 Conclusion 56 A Home Network Standards 58 A.1 IEEE1905.1 58 A.2 ITU-TG.hn 59 Bibliography 62

    [1] Peerapol Tinnakornsrisuphap, Punyaslok Purkayastha, and Bibhu Mohanty. Coverage and capacity analysis of hybrid home networks. In Computing, Networking and Communications (ICNC), 2014 International Conference on, pages 117–123. IEEE, 2014.
    [2] Yi-Chih Tung, Chien-Min Ou, Wen-Jyi Hwang, and Wei-De Wu. Service discovery of ip cameras using sip and zeroconf protocols. In International Conference on Autonomic and Trusted Computing, pages 388–402. Springer, 2008.
    [3] Stefania Sesia, Matthew Baker, and Issam Tou k. LTE-the UMTS long term evolution: from theory to practice. John Wiley & Sons, 2011.
    [4] Anindya Majumder et al. Power line communications. IEEE potentials, 23(4):4–8, 2004.
    [5] Ieee 1901 standard for broadband over powerline networks: Medium access control and phy layer speci cations. In https://standards.ieee.org/ ndstds/standard/ 1901-2010.html. IEEE Standards Association, 2010.
    [6] Konstantinos Samdanis, Filipe Leitão, Simon Oechsner, Jaume Rius I Riu, Roberto David Carnero Ros, and Guiu Fabregas. From interworking to hybrid access systems and the road toward the next-generation of xed-mobile convergence. IEEE Communications Standards, 1(1):36–43, 2017.
    62[7] Jeongho Jeon, Huaning Niu, Qian Clara Li, Apostolos Papathanassiou, and Geng Wu. Lte in the unlicensed spectrum: Evaluating coexistence mechanisms. In Globecom Workshops (GC Wkshps), 2014, pages 740–745. IEEE, 2014.
    [8] The Broadband Forum. TR-348 Hybrid Access Broadband Network Architecture. BBF, 2016.
    [9] Alan Ford, Costin Raiciu, Mark Handley, and Olivier Bonaventure. Tcp extensions for multipath operation with multiple addresses, draft-ietf-mptcp-multiaddressed-09. Internetdraft, IETF (March 2012), 2012.
    [10] Ieee 1905.1 standard for a convergent digital home network for heterogeneous technologies. In https://standards.ieee.org/ ndstds/standard/1905.1-2013.html. IEEE Standards Association, 2013.
    [11] 1905.1a-2014 - ieee standard for a convergent digital home network for heterogeneous technologies amendment 1: Support of new mac/phys and enhancements. In https://standards.ieee.org/ ndstds/standard/1905.1a-2014.html. IEEE Standards Association, 2014.
    [12] Vladimir Oksman and Stefano Galli. G. hn: The new itu-t home networking standard. IEEE Communications Magazine, 47(10), 2009.
    [13] Marie Chan, Daniel Estève, Christophe Escriba, and Eric Campo. A review of smart homes—present state and future challenges. Computer methods and programs in biomedicine, 91(1):55–81, 2008.
    [14] Stefano Galli, Anna Scaglione, and Zhifang Wang. For the grid and through the grid: The role of power line communications in the smart grid. Proceedings of the IEEE, 99(6):998–1027, 2011.
    [15] Abdesselem Kortebi, Patrick Le Dain, and Fabien Dure. Home network assistant: Towards better diagnostics and increased customer satisfaction. In Global Information Infrastructure Symposium, 2013, pages 1–6. IEEE, 2013.
    [16] Mario Marchese. QoS over heterogeneous networks. John Wiley & Sons, 2007.
    [17] Jiann-Liang Chen, Ming-Chiao Chen, and Yi-Ru Chian. Qos management in heterogeneous home networks. Computer Networks, 51(12):3368–3379, 2007.
    [18] Donald F Specht. A general regression neural network. IEEE transactions on
    neural networks, 2(6):568–576, 1991.
    [19] Simon S Haykin, Simon S Haykin, Simon S Haykin, and Simon S Haykin. Neural networks and learning machines, volume 3. Pearson Upper Saddle River, NJ, USA:, 2009.
    [20] Jianfeng Deng, Ling Zhang, Jinlong Hu, and Dongli He. Adaptation of ann based video stream qoe prediction model. In Paci c Rim Conference on Multimedia, pages 313–322. Springer, 2014.
    [21] Elisangela Aguiar, André Riker, Antônio Abelém, Eduardo Cerqueira, and Mu Mu. Video quality estimator for wireless mesh networks. In Proceedings of the 2012 IEEE 20th International Workshop on Quality of Service, page 1. IEEE Press, 2012.
    [22] Pablo Muñoz, Raquel Barco, José María Ruiz-Avilés, Isabel de la Bandera, and Alejandro Aguilar. Fuzzy rule-based reinforcement learning for load balancing techniques in enterprise lte femtocells. IEEE Transactions on Vehicular Technology, 62(5):1962–1973, 2013.
    [23] Yi-Chih Tung, Wen-Jyi Hwang, and Chih-Hsiang Ho. A novel qos mapping algorithm for heterogeneous home networks using general regression neural networks. In Computational Science and Engineering (CSE), 2014 IEEE 17th International Conference on, pages 519–526. IEEE, 2014, DOI: 10.1109/CSE.2014.121.
    [24] Wen-Jyi Hwang, Yi-Chih Tung, Ying-Lun Chen, Po-Yu Lai, and Chih-Hsiang Ho. A novel user-oriented quality of service algorithm for home networks. IEEE Systems Journal, Accepted for Publication (Available online 14 October 2015, DOI: 10.1109/JSYST.2015.2480869).
    [25] Wen-Jyi Hwang, Tsung-Ming Tai, Yun-Jie Jhang, Yi-Chih Tung, Chih-Hsiang Ho, and Sy-Yen Kuo. Quality of service management for home networks using online service response prediction. IEEE Internet of Things Journal, Accepted for Publication (Available online 23 May 2017, DOI: 10.1109/JIOT.2017.2707094).
    [26] Hsin-Pu Chung, Yi-Chih Tung, Yen-Ru Peng, and Li-Hung Liao. Method for assigning wireless network transmission resource and application thereof, October 9 2009. US Patent App. 12/576,248.
    [27] Chih-Hsiang Ho, Yi-Chih Tung, Pang-Fu Liu, Hao-Gen Wong, and Li-Sheng Chen. Coverage hole detection apparatus and method, January 17 2017. US Patent 9,549,358.
    [28] Yi-Chih Tung, Pang-Fu Liu, Wen-Jyi Hwang, and Chih-Hsiang Ho. Heterogeneous network system, network apparatus, and rendezvous path selection method thereof, May 28 2015. US Patent App. 14/723,659.
    [29] Yi-Chih Tung, Hao-Gen Wong, Wen-Jyi Hwang, and Chih-Hsiang Ho. Rendezvous ow control apparatus, method, and non-transitory tangible computer readable medium, June 8 2015. US Patent App. 14/732,927.
    [30] Bert Hubert, Thomas Graf, Greg Maxwell, Remco van Mook, Martijn van Oosterhout, P Schroeder, Jasper Spaans, and Pedro Larroy. Linux advanced routing & tra c control. In Ottawa Linux Symposium, page 213, 2002.
    [31] Ajay Tirumala, Feng Qin, Jon Dugan, Jim Ferguson, and Kevin Gibbs. Iperf: The tcp/udp bandwidth measurement tool. http://dast.nlanr.net/Projects, 2005.
    [32] John Egan, Agustin Badenes Corella, Marcos Martinez Vazquez, Chano Gomez Martinez, David Thorne, Erez Ben-Tovim, and Ravi Mantri. Plc neighboring networks, 2012.

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