[1]陳煜翔,行動軟體定義網路階層式仲介架構之霧運算聯盟開發工具,碩士論文,國立高雄科技大學 電子工程系 (第一校區),2022。[2]曾柏家,自主聯盟之行動霧運算架構,碩士論文,國立高雄科技大學 電子工程系 (第一校區),2020。[3]李昱興,基於深度支援向量機之可解釋車流與事件辨識系統,碩士論文,國立高雄科技大學 電子工程系 (第一校區),2021。[4]Mohindru, V., Vashishth, S., Bathija, D. (2022). Internet of Things (IoT) for Healthcare Systems: A Comprehensive Survey. In: Singh, P.K., Singh, Y., Kolekar, M.H., Kar, A.K., Gonçalves, P.J.S. (eds) Recent Innovations in Computing. Lecture Notes in Electrical Engineering, vol 832. Springer, Singapore, doi: https://doi.org/10.1007/978-981-16-8248-3_18.
[5]S. W. Turner, M. Karakus, E. Guler, and S. Uludag, “A Promising Integration of SDN and Blockchain for IoT Networks: A Survey,” in IEEE Access, vol. 11, pp. 29800–29822, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3260777.
[6]W. Rafique, L. Qi, I. Yaqoob, M. Imran, R. U. Rasool, and W. Dou, “Complementing IoT Services Through Software Defined Networking and Edge Computing: A Comprehensive Survey,” in IEEE Communications Surveys Tutorials, vol. 22, no. 3, pp. 1761–1804, 2020, doi: https://doi.org/10.1109/COMST.2020.2997475.
[7]S. Siddiqui et al., “Towards Software-Defined Networking-based IoT Frameworks: A Systematic Literature Review, Taxonomy, Open Challenges and Prospects,” in IEEE Access, pp. 1–1, 2022, doi: https://doi.org/10.1109/access.2022.3188311.
[8]P. Mishra, D. Puthal, M. Tiwary, and S. P. Mohanty, “Software Defined IoT Systems: Properties, State of the Art, and Future Research,” in IEEE Wireless Communications, vol. 26, no. 6, pp. 64–71, Dec. 2019, doi: https://doi.org/10.1109/mwc.001.1900083.
[9]H. Zemrane, Y. Baddi, and A. Hasbi, “SDN-Based Solutions to Improve IOT: Survey,” in 2018 IEEE 5th International Congress on Information Science and Technology (CiSt), Oct. 2018, doi: https://doi.org/10.1109/cist.2018.8596577.
[10]L. Zhao, W. Sun, Y. Shi, and J. Liu, “Optimal Placement of Cloudlets for Access Delay Minimization in SDN-Based Internet of Things Networks,” in IEEE Internet of Things Journal, vol. 5, no. 2, pp. 1334–1344, Apr. 2018, doi: https://doi.org/10.1109/jiot.2018.2811808.
[11]D. Espinel Sarmiento, A. Lebre, L. Nussbaum, and A. Chari, “Decentralized SDN Control Plane for a Distributed Cloud-Edge Infrastructure: A Survey,” in IEEE Communications Surveys & Tutorials, vol. 23, no. 1, pp. 256–281, 2021, doi: https://doi.org/10.1109/comst.2021.3050297.
[12]Hasneen, J., Sadique, K.M. (2022). A Survey on 5G Architecture and Security Scopes in SDN and NFV. In: Iyer, B., Ghosh, D., Balas, V.E. (eds) Applied Information Processing Systems . Advances in Intelligent Systems and Computing, vol 1354. Springer, Singapore, doi: https://doi.org/10.1007/978-981-16-2008-9_43.
[13]F. Al-Doghman, N. Moustafa, I. Khalil, Z. Tari, and A. Zomaya, “AI-enabled Secure Microservices in Edge Computing: Opportunities and Challenges,” in IEEE Transactions on Services Computing, pp. 1–1, 2022, doi: https://doi.org/10.1109/tsc.2022.3155447.
[14]R. A. Addad, D. L. C. Dutra, T. Taleb, and H. Flinck, “AI-based Network-aware Service Function Chain Migration in 5G and Beyond Networks,” in IEEE Transactions on Network and Service Management, pp. 1–1, 2021, doi: https://doi.org/10.1109/tnsm.2021.3074618.
[15]Harjula, E., Artemenko, A., Forsström, S. (2021). Edge Computing for Industrial IoT: Challenges and Solutions. In: Mahmood, N.H., Marchenko, N., Gidlund, M., Popovski, P. (eds) Wireless Networks and Industrial IoT. Springer, Cham, doi: https://doi.org/10.1007/978-3-030-51473-0_12.
[16]Poltronieri, F., Stefanelli, C., Suri, N. et al. “Value is King: The MECForge Deep Reinforcement Learning Solution for Resource Management in 5G and Beyond”. in J Netw Syst Manage, Springer vol. 30, no. 4, Jul. 2022, doi: https://doi.org/10.1007/s10922-022-09672-6.
[17]D. C. Nguyen, M. Ding, P. N. Pathirana, A. Seneviratne, J. Li, and H. V. Poor, “Federated Learning for Internet of Things: A Comprehensive Survey,” in IEEE Communications Surveys & Tutorials, pp. 1–1, 2021, doi: https://doi.org/10.1109/comst.2021.3075439.
[18]W. Y. B. Lim et al., “Federated Learning in Mobile Edge Networks: A Comprehensive Survey,” in IEEE Communications Surveys & Tutorials, pp. 1–1, 2020, doi: https://doi.org/10.1109/comst.2020.2986024.
[19]Sita Rani, Aman Kataria, Sachin Kumar, Prayag Tiwari, “Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review,” in Knowledge-Based Systems, vol. 271, p. 110658, May 2023, doi: https://doi.org/10.1016/j.knosys.2023.110658.
[20]T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated Learning: Challenges, Methods, and Future Directions,” in IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 50–60, May 2020, doi: https://doi.org/10.1109/msp.2020.2975749.
[21]Singh, P., Singh, M.K., Singh, R., Singh, N. (2022). Federated Learning: Challenges, Methods, and Future Directions. In: Yadav, S.P., Bhati, B.S., Mahato, D.P., Kumar, S. (eds) Federated Learning for IoT Applications. EAI/Springer Innovations in Communication and Computing. Springer, Cham, doi: https://doi.org/10.1007/978-3-030-85559-8_13.
[22]Selvi, K.T., Thamilselvan, R. An intelligent traffic prediction framework for 5G network using SDN and fusion learning. Peer-to-Peer Netw. Appl. vol. 15, pp. 751–767, Jan. 2022, doi: https://doi.org/10.1007/s12083-021-01280-6.
[23]Huang, CM., Dao, DT. & Chiang, MS. SDN-FHOR-DMM: a software defined network (SDN)-based fast handover with the optimal routing control method for distributed mobility management (DMM). Telecommun Syst, vol. 72, pp. 157–177, Mar. 2019, doi: https://doi.org/10.1007/s11235-019-00567-7.
[24]W. Hao et al., “Towards a Trust-Enhanced Blockchain P2P Topology for Enabling Fast and Reliable Broadcast,” IEEE Transactions on Network and Service Management, vol. 17, no. 2, pp. 904–917, Jun. 2020, doi: https://doi.org/10.1109/tnsm.2020.2980303.
[25]Li, P., Xie, W., Yuan, Y. et al. Deep Reinforcement Learning for Load Balancing of Edge Servers in IoV. Mobile Netw Appl, vol. 27, pp. 1461–1474 , May 2022, doi: https://doi.org/10.1007/s11036-022-01972-0.
[26]M. W. Hussain, K. H. K. Reddy, J. J. P. C. Rodrigues, and D. S. Roy, "An Indirect Controller-Legacy Switch Forwarding Scheme for Link Discovery in Hybrid SDN," in IEEE Systems Journal, vol. 15, no. 2, pp. 3142-3149, Jun. 2021, doi: https://doi.org/10.1109/jsyst.2020.3011902.
[27]P. R. Desai, S. Mini, and D. K. Tosh, "Edge-based Optimal Routing in SDN-enabled Industrial Internet of Things," in IEEE Internet of Things Journal, pp. 1-1, 2022, doi: https://doi.org/10.1109/jiot.2022.3163228.
[28]Agyemang, J.O., Yu, D., Kponyo, J.J. (2022). Autonomic IoT: Towards Smart System Components with Cognitive IoT. In: Ngatched, T.M.N., Woungang, I. (eds) Pan-African Artificial Intelligence and Smart Systems. PAAISS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 405. Springer, Cham, doi: https://doi.org/10.1007/978-3-030-93314-2_16.
[29]P. T. Duy, H. D. Hoang, D. T. T. Hien, A. G.-T. Nguyen, and V.-H. Pham, "B-DAC: A decentralized access control framework on Northbound interface for securing SDN using blockchain," Journal of Information Security and Applications, vol. 64, p. 103080, Feb. 2022, doi: https://doi.org/10.1016/j.jisa.2021.103080.
[30]J. Gao et al., “A Blockchain-SDN enabled Internet of Vehicles Environment for Fog Computing and 5G Networks,” in IEEE Internet of Things Journal, pp. 1–1, 2019, doi: https://doi.org/10.1109/jiot.2019.2956241.
[31]Arzo, S.T., Scotece, D., Bassoli, R. et al. MSN: A Playground Framework for Design and Evaluation of MicroServices-Based sdN Controller. J Netw Syst Manage, vol. 30, no. 1, Jan. 2022, doi: https://doi.org/10.1007/s10922-021-09631-7.
[32]Satheesh, K.K.S.V.A., Janani, M., Venkateswarlu, S.C., Kumar, R.G., Gupta, A., Kotaiah, B. (2022). AI and Machine Learning Enabled Software Defined Networks. In: Bhateja, V., Khin Wee, L., Lin, J.CW., Satapathy, S.C., Rajesh, T.M. (eds) Data Engineering and Intelligent Computing, pp. 131–144. Lecture Notes in Networks and Systems, vol 446. Springer, Singapore, doi: https://doi.org/10.1007/978-981-19-1559-8_14.
[33]M. Tanha, D. Sajjadi, R. Ruby, and J. Pan, “Capacity-Aware and Delay-Guaranteed Resilient Controller Placement for Software-Defined WANs,” in IEEE Transactions on Network and Service Management, vol. 15, no. 3, pp. 991–1005, Sep. 2018, doi: https://doi.org/10.1109/tnsm.2018.2829661.
[34]H. Mojez, A. M. Bidgoli, and H. H. S. Javadi, "Star capacity-aware latency-based next controller placement problem with considering single controller failure in software-defined wide-area networks," The Journal of Supercomputing, Mar. 2022, doi: https://doi.org/10.1007/s11227-022-04360-3.
[35]L. Jai Vinita and V. Vetriselvi, “Federated Learning-based Misbehaviour detection on an emergency message dissemination scenario for the 6G-enabled Internet of Vehicles,” Ad Hoc Networks, vol. 144, p. 103153, May 2023, doi: https://doi.org/10.1016/j.adhoc.2023.103153.
[36]P. Tam, S. Math, and S. Kim, “Optimized Multi-Service Tasks Offloading for Federated Learning in Edge Virtualization,” in IEEE Transactions on Network Science and Engineering, pp. 1–17, 2022, doi: https://doi.org/10.1109/TNSE.2022.3200057.
[37]J. Mills, J. Hu, and G. Min, “Communication-Efficient Federated Learning for Wireless Edge Intelligence in IoT,” in IEEE Internet of Things Journal, pp. 1–1, 2019, doi: https://doi.org/10.1109/jiot.2019.2956615.
[38]B. Sellami, A. Hakiri, and S. Ben Yahia, “Deep Reinforcement Learning for energy-aware task offloading in join SDN-Blockchain 5G massive IoT edge network,” Future Generation Computer Systems, vol. 137, pp. 363–379, Dec. 2022, doi: https://doi.org/10.1016/j.future.2022.07.024.
[39]H. Kang, Z. Li, and Q. Zhang, “Communicational and Computational Efficient Federated Domain Adaptation,” in IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 12, pp. 3678–3689, Dec. 2022, doi: https://doi.org/10.1109/TPDS.2022.3167457.
[40]X. Zhang, Y. Wang, S. Chen, C. Wang, D. Yu, and X. Cheng, “Robust communication-efficient decentralized learning with heterogeneity,” Journal of Systems Architecture, p. 102900, May 2023, doi: https://doi.org/10.1016/j.sysarc.2023.102900.
[41]X. Ma, J. Zhu, Z. Lin, S. Chen, and Y. Qin, “A state-of-the-art survey on solving non-IID data in Federated Learning,” Future Generation Computer Systems, vol. 135, pp. 244–258, Oct. 2022, doi: https://doi.org/10.1016/j.future.2022.05.003.
[42]Y. Ye, S. Li, F. Liu, Y. Tang, and W. Hu, “EdgeFed: Optimized Federated Learning Based on Edge Computing,” in IEEE Access, pp. 1–1, 2020, doi: https://doi.org/10.1109/access.2020.3038287.
[43]A. Sadeghi-Niaraki, “Internet of Thing (IoT) review of review: Bibliometric overview since its foundation,” Future Generation Computer Systems, Feb. 2023, doi: https://doi.org/10.1016/j.future.2023.01.016.
[44]M. Wang, Y. Cui, X. Wang, S. Xiao and J. Jiang, "Machine Learning for Networking: Workflow, Advances and Opportunities," in IEEE Network, vol. 32, no. 2, pp. 92-99, March-April 2018, doi: https://doi.org/10.1109/MNET.2017.1700200.
[45]H. Zhu, J. Xu, S. Liu, and Y. Jin, “Federated learning on non-IID data: A survey,” Neurocomputing, vol. 465, pp. 371–390, Nov. 2021, doi: https://doi.org/10.1016/j.neucom.2021.07.098.