L2-LBMT: A Layered Load Balance Routing Protocol for underwater multimedia data transmission
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Providing highly efficient underwater transmission of mass multimedia data is challenging due to the particularities of the underwater environment. Although there are many schemes proposed to optimize the underwater acoustic network communication protocols, from physical layer, data link layer, network layer to transport layer, the existing routing protocols for underwater wireless sensor network (UWSN) still cannot well deal with the problems in transmitting multimedia data because of the difficulties involved in high energy consumption, low transmission reliability or high transmission delay. It prevents us from applying underwater multimedia data to real-time monitoring of marine environment in practical application, especially in emergency search, rescue operation and military field. Therefore, the inefficient transmission of marine multimedia data has become a serious problem that needs to be solved urgently. In this paper, A Layered Load Balance Routing Protocol (L2-LBMT) is proposed for underwater multimedia data transmission. In L2-LBMT, we use layered and load-balance Ad Hoc Network to transmit data, and adopt segmented data reliable transfer (SDRT) protocol to improve the data transport reliability. And a 3-node variant of tornado (3-VT) code is also combined with the Ad Hoc Network to transmit little emergency data more quickly. The simulation results show that the proposed protocol can balance energy consumption of each node, effectively prolong the network lifetime and reduce transmission delay of marine multimedia data.
Key wordsunderwater wireless multicast multimedia data transmission load balance
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This work is supported by the National Natural Science Foundation of China (No. 61401413), and the Digital Home Industry Cluster Oriented Technology Service Innovation Pilot Project in 2015. We are grateful to the anonymous reviewers for comments on the original manuscript.
- Byers, J. W., Luby, M., and Mitzenmacher, M., 1999. Accessing multiple mirror sites in parallel: Using Tornado codes to speed up downloads. INFOCOM’99, Eighteenth Joint Conference of the IEEE Computer and Communications Societies. New York, USA, 275–283.Google Scholar
- Byers, J. W., Luby, M., Mitzenmacher, M., and Rege, A., 1998. A digital fountain approach to reliable distribution of bulk data. Proceedings of the ACM SIGCOMM’98 Conference on Applications, Technologies, Architectures and Protocols for Computer Communication. Vancouver, British Columbia, Canada, 56–67.Google Scholar
- Capellano, V., 1997. Performance improvement of a 50 km acoustic transmission through adaptive equalization and spatial diversity. IEEE Oceans, 1: 569–573.Google Scholar
- Capellano, V., Loubet, G., and Jourdain, G., 1996. Adaptive multichannel equalizer for underwater communications. Oceans, 2: 994–999.Google Scholar
- Cui, J. H., Kong, J. J., Gerla, M., and Zhou, S. L., 2006. Challenges: Building scalable mobile underwater wireless sensor networks for aquatic applications. Network, Special Issue on Wireless Sensor Networking, 20 (3): 12–18.Google Scholar
- Heinzelman, W. R., Chandrakasan, A., and Balakrishnan, H., 2000. Energy-efficient communication protocol for wireless microsensor networks. Proceedings of the 33rd Annual Hawaii International Conference on System Sciences. Honolulu Hawaii, USA, 3005–3014.Google Scholar
- Li, X., Liu, T. J., and Liu, Y., 2014. Optimized multicast routing algorithm based on tree structure in MANETs. Transaction on Network Technology and Applications, 11 (2): 90–99.Google Scholar
- Lucani, D., Medard, M., and Stojanovic, M., 2008. Underwater acoustic networks: Channel models and network coding based lower bound on transmission power for multicast. Journal on Selected Areas in Communications, Special Issue on Underwater Wireless Communications and Networks, 1 (11): 1–12.Google Scholar
- Xiao, F., Yang, X., Sun, L., Wang, R., and Tang, X., 2016. Node selection approach for data compression in wireless multimedia sensor networks. Journal of Beijing Post and Telecommunications, 39 (2): 15–19.Google Scholar