Open Access
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
Article Number 04002
Number of page(s) 6
Section Circuit Simulation, Electric Modules and Displacement Sensor
Published online 19 November 2018
  1. J. Gubbi, R. Buyya, S. Marusic, M. Palaniswami. Internet of things (iot): a vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645-1660(2012) [Google Scholar]
  2. Z. Wang, J. Crowcroft. Quality-of-service routing for supporting multimedia applications. IEEE Journal on Selected Areas in Communications, 14(7), 1228-1234(2002) [CrossRef] [Google Scholar]
  3. S. Sutton, G. Barto. Introduction to reinforcement learning. Machine Learning, 16(1), 285-286(2005) [Google Scholar]
  4. R. Widyono. The design and evaluation of routing algorithms for real-time channels (1994) [Google Scholar]
  5. C. Pornavalai, G. Chakraborty, N. Shiratori. QoS based routing algorithm in integrated services packet networks. International Conference on Network Protocols, 1997. Proceedings (Vol.7, pp.167-174) (1997) [CrossRef] [Google Scholar]
  6. S. Chen, M. Song, S. Sahni. Two techniques for fast computation of constrained shortest paths. IEEE/ACM Transactions on Networking, 16(1), 105-115(2008) [CrossRef] [Google Scholar]
  7. Y. Cui, K. Xu, J. Wu. Precomputation for multiconstrained QoS routing in high-speed networks. Joint Conference of the IEEE Computer and Communications. IEEE Societies (Vol.2, pp.1414-1424 vol.2)(2003) [Google Scholar]
  8. P. Bose, P. Morin. Competitive online routing in geometric graphs. Theoretical Computer Science, 324(2), 273-288(2001) [CrossRef] [Google Scholar]
  9. D. B. Magnani, I. A. Carvalho, T. F. Noronha. Robust optimization for ospf routing. IFAC-PapersOnLine, 49(12), 461-466(2016) [Google Scholar]
  10. Y. Sun, L. Li, J. Qi. Cognitive networks qos routing optimization based on multi-objective genetic algorithm. Journal of Convergence Information Technology, 7(12), 215-225(2012) [Google Scholar]
  11. D. T. Hai. Multi-objective genetic algorithm for solving routing and spectrum assignment problem. Seventh International Conference on Information Science and Technology (pp.177-180)(2017) [Google Scholar]
  12. C. H. Qiu, Y. Gong, K. X.Zhou. The Research on QoS Routing Algorithm Based on Improved Optimization Sorting Ant Colony Algorithm. National Conference on Electrical, Electronics and Computer Engineering(2016) [Google Scholar]
  13. R. M. Entz, H. A. Porto, R. F. D. Oliveira, R. A. D. Lima. Efficient Aircraft Routing Algorithm Based on Ant Colony Optimization. Aiaa/issmo Multidisciplinary Analysis and Optimization Conference(2015) [Google Scholar]
  14. L. Zhang, L. B. Cai, M. Li, F. H. Wang. A method for least-cost qos multicast routing based on genetic simulated annealing algorithm. Computer Communications, 32(1), 105-110(2009) [CrossRef] [Google Scholar]
  15. M. Ramezani, M. Jahanshahi. Load-aware multicast routing in multi-radio wireless mesh networks using fca-cmac neural network. Computing(4), 1-29(2017) [Google Scholar]
  16. Z. M. Fadlullah, F. Tang, B. Mao, N. Kato, O. Akashi, T. Inoue, et al. State-of-the-art deep learning: evolving machine intelligence toward tomorrow’s intelligent network traffic control systems. IEEE Communications Surveys & Tutorials, 19(4), 2432-2455(2017) [CrossRef] [Google Scholar]
  17. N. Kato, Z. M. Fadlullah, B. Mao, F. Tang, O. Akashi, T. Inoue, et al. The deep learning vision for heterogeneous network traffic control: proposal, challenges, and future perspective. IEEE Wireless Communications, PP(99), 2-9(2016) [Google Scholar]
  18. T. Hu, Y. Fei. Qelar: a machine-learning-based adaptive routing protocol for energy-efficient and lifetime-extended underwater sensor networks. IEEE Transactions on Mobile Computing, 9(6), 796-809(2010) [CrossRef] [Google Scholar]
  19. S. C. Lin, I. F. Akyildiz, P. Wang, M. Luo. QoS-Aware Adaptive Routing in Multi-layer Hierarchical Software Defined Networks: A Reinforcement Learning Approach. IEEE International Conference on Services Computing (pp.25-33)(2016) [Google Scholar]

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