Abstract
Due to the rapid growth of maritime business data, data centers and backbone networks need to deal with a large number of complex network traffic. Resource-intensive tasks such as high-definition video playback, multimedia applications, and online games have been growing in recent years. These requirements not only increase the burden of bandwidth demand but also increase the energy consumption of the ship terminal network. At the same time, great changes have been raised in the characteristics and requirements of current maritime services, from the initial optimal requirements of point-to-point communication to support a variety of service quality. Exploring and deploying a maritime communication network with more omnidirectional coverage, higher reliability, higher speed, and lower cost will have a great influence on the development of the maritime industry and security in the future. Therefore, the introduction of brand new network technologies (such as software defined network, mobile edge computing, intelligence algorithms) into the field of maritime communications provides new ideas for solving challenges faced by maritime communications, but many basic research issues has not been thoroughly solved. We divide the existing works in the relevant literature into three categories of research problems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, T., Matinmikko, M., Chen, X., Zhou, X., Ahokangas, P.: Software defined mobile networks: concept, survey, and research directions. IEEE Commun. Mag. 53(11), 126–133, November 2015
Kreutz, D., Ramos, F.M.V., Verłssimo, P.E., Rothenberg, C.E., Azodolmolky, S., Uhlig, S.: Software-defined networking: a comprehensive survey. Proc. IEEE 103(1), 14–76 (2015). Jan
Liang, C., Yu, F.R.: Wireless network virtualization: a survey, some research issues and challenges. IEEE Commun. Surv Tutor. 17(1), 358–380, Firstquarter 2015
Nguyen, V., Brunstrom, A., Grinnemo, K., Taheri, J.: SDN/NFV-based mobile packet core network architectures: a survey. IEEE Commun. Surv. Tutor. 19(3), 1567–1602, thirdquarter 2017
Schulz-Zander, J., Mayer, C., Ciobotaru, B., Lisicki, R., Schmid, S., Feldmann, A.: Unified programmability of virtualized network functions and software-defined wireless networks. IEEE Trans. Netw. Serv. Manage. 14(4), 1046–1060 (2017). Dec
Chaudhary, R., Aujla, G.S., Kumar, N., Rodrigues, J.J.P.C.: Optimized big data management across multi-cloud data centers: software-defined-network-based analysis. IEEE Commun. Mag. 56(2), 118–126 (2018). Feb
Abbas, N., Zhang, Y., Taherkordi, A., Skeie, T.: Mobile edge computing: a survey. IEEE Internet Things J. 5(1), 450–465 (2018). Feb
Yang, P., Zhang, N., Bi, Y., Yu, L., Shen, X.S.: Catalyzing cloud-fog interoperation in 5G wireless networks: an SDN approach. IEEE Netw. 31(5), 14–20 (2017)
Zhang, Y., Yao, J., Guan, H.: Intelligent cloud resource management with deep reinforcement learning. IEEE Cloud Comput. 4(6), 60–69 (2017)
Cheng, M., Li, J., Nazarian, S.: DRL-cloud: deep reinforcement learning-based resource provisioning and task scheduling for cloud service providers. In: Proceedings of 23rd ASP-DAC Conference, Jeju, pp. 129–134 (2018)
Zhang, N., Zhang, S., Yang, P., Alhussein, O., Zhuang, W., Shen, X.S.: Software defined space-air-ground integrated vehicular networks: challenges and solutions. IEEE Commun. Mag. 55(7), 101–109 (2017). July
Huang, W., Ding, L., Meng, D., Hwang, J., Xu, Y., Zhang, W.: QoE-based resource allocation for heterogeneous multi-radio communication in software-defined vehicle networks. IEEE Access 6, 3387–3399 (2018)
Lai, C., Zhou, H., Cheng, N., Shen, X.S.: Secure group communications in vehicular networks: a software-defined network-enabled architecture and solution. IEEE Veh. Technol. Mag. 12(4), 40–49 (2017). Dec
Alasadi, E., Al-Raweshidy, H.S.: SSED: servers under software-defined network architectures to eliminate discovery messages. IEEE/ACM Trans. Netw. 26(1), 104–117 (2018). Feb
Tang, F., Mao, B., Fadlullah, Z.M., Kato, N.: On a novel deep-learning-based intelligent partially overlapping channel assignment in SDN-IoT. IEEE Commun. Mag. 56(9), 80–86 (2018). Sept
Fadlullah, Z.M., et al.: State-of-the-art deep learning: evolving machine intelligence toward tomorrows intelligent network traffic control systems. IEEE Commun. Surv. Tutor. 19(4), 2432–2455, Fourthquarter 2017
Huang, X., Yuan, T., Qiao, G., Ren, Y.: Deep reinforcement learning for multimedia traffic control in software defined networking. IEEE Netw. 32(6), 35–41 (2018)
Jindal, A., Aujla, G.S., Kumar, N., Chaudhary, R., Obaidat, M.S., You, I.: SeDaTiVe: SDN-enabled deep learning architecture for network traffic control in vehicular cyber-physical systems. IEEE Netw. 32(6), 66–73 (2018)
Liu, W., Zhang, J., Liang, Z., Peng, L., Cai, J.: Content popularity prediction and caching for ICN: a deep learning approach with SDN. IEEE Access 6, 5075–5089 (2018)
He, Y., Zhao, N., Yin, H.: Integrated networking, caching, and computing for connected vehicles: a deep reinforcement learning approach. IEEE Trans. Veh. Technol. 67(1), 44–55 (2018). Jan
He, Y., Yu, F.R., Zhao, N., Leung, V.C.M., Yin, H.: Software-defined networks with mobile edge computing and caching for smart cities: a big data deep reinforcement learning approach. IEEE Commun. Mag. 55(12), 31–37 (2017). Dec
Chen, X., Jiao, L., Li, W., Fu, X.: Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans. Netw. 24(5), 2795–2808 (2016). October
Zhang, D., Chen, Z., Awad, M.K., Zhang, N., Zhou, H., Shen, X.S.: Utility-optimal resource management and allocation algorithm for energy harvesting cognitive radio sensor networks. IEEE J. Sel. Areas Commun. 34(12), 3552–3565 (2016). Dec
Zhang, J., Xia, W., Yan, F., Shen, L.: Joint computation offloading and resource allocation optimization in heterogeneous networks with mobile edge computing. IEEE Access 6, 19324–19337 (2018)
Mao, Y., Zhang, J., Letaief, K.B.: Dynamic computation offloading for mobile-edge computing with energy harvesting devices. IEEE J, Sel. Areas Commun. 34(12), 3590–3605 (2016). Dec
You, C., Huang, K., Chae, H., Kim, B.H.: Energy-efficient resource allocation for mobile-edge computation offloading. IEEE Trans. Wirel. Commun. 16(3), 1397–1411 (2017). March
Mao, Y., Zhang, J., Letaief, K.B.: Joint task offloading scheduling and transmit power allocation for mobile-edge computing systems. In: 2017 IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, pp. 1–6 (2017)
Zhang, K., et al.: Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks. IEEE Access 4, 5896–5907 (2016)
Le, H.Q., Al-Shatri, H., Klein, A.: Efficient resource allocation in mobile-edge computation offloading: completion time minimization. In: IEEE International Symposium on Information Theory (ISIT), Aachen, vol. 2017, pp. 2513–2517 (2017)
Wang, F., Xu, J., Wang, X., Cui, S.: Joint offloading and computing optimization in wireless powered mobile-edge computing systems. IEEE Trans. Wirel. Commun. 17(3), 1784–1797 (2018). March
Zhang, D., Shen, R., Ren, J., Zhang, Y.: Delay-optimal proactive service framework for block-stream as a service. IEEE Wirel. Commun. Lett. 7(4), 598–601 (2018). Aug
Wang, C., Liang, C., Yu, F.R., Chen, Q., Tang, L.: Computation offloading and resource allocation in wireless cellular networks with mobile edge computing. IEEE Trans. Wirel. Commun. 16(8), 4924–4938 (2017). Aug
Yang, L., Cao, J., Cheng, H., Ji, Y.: Multi-user computation partitioning for latency sensitive mobile cloud applications. IEEE Trans. Comput. 64(8), 2253–2266 (2015). Aug
Lee, S., Zhang, R.: Distributed wireless power transfer with energy feedback. IEEE Trans. Signal Process. 65(7), 1685–1699 (2017). Apr
Ren, J., Guo, H., Xu, C., Zhang, Y.: Serving at the edge: a scalable iot architecture based on transparent computing. IEEE Netw. 31(5), 96–105 (2017)
Zhang, D., Qiao, Y., She, L., Shen, R., Ren, J., Zhang, Y.: Two time-scale resource management for green internet of things networks. IEEE Internet of Things J. 6(1), 545–556 (2019). Feb
Byun, H.: A method of indirect configuration propagation with estimation of system state in networked multi-agent dynamic systems. IEEE Commun. Lett. 22(9), 1766–1769 (2018). Sept
Hoai, D.K., Van Phuong, N.: Anomaly color detection on UAV images for search and rescue works. In: 2017 9th International Conference on Knowledge and Systems Engineering (KSE), Hue, pp. 287–291 (2017)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Yang, T., Shen, X. (2020). Background and Literature Survey. In: Mission-Critical Application Driven Intelligent Maritime Networks. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-15-4412-5_2
Download citation
DOI: https://doi.org/10.1007/978-981-15-4412-5_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-4411-8
Online ISBN: 978-981-15-4412-5
eBook Packages: Computer ScienceComputer Science (R0)