Nenad Kojić
Natalija Vugdelija
ICT College for vocational studies, Belgrade, Serbia
DOI: https://doi.org/10.31410/ITEMA.2018.37
2nd International Scientific Conference on Recent Advances in Information Technology, Tourism, Economics, Management and Agriculture – ITEMA 2018 – Graz, Austria, November 8, 2018, CONFERENCE PROCEEDINGS published by the Association of Economists and Managers of the Balkans, Belgrade, Serbia; ISBN 978-86-80194-13-4
2nd International Scientific Conference on Recent Advances in Information Technology, Tourism, Economics, Management and Agriculture – ITEMA 2018 – Graz, Austria, November 8, 2018, CONFERENCE PROCEEDINGS published by the Association of Economists and Managers of the Balkans, Belgrade, Serbia; ISBN 978-86-80194-13-4
Abstract
When users of websites have the need to access some real-time audio or video signal into a stream, this can cause delays and interruptions in the transmission. This is especially pronounced when a large number of users want to access the server at the same time and look at the same content, thus increasing the linkwave’s load. In this paper, one solution of the algorithm, based on artificial intelligence, is presented. The proposed algorithm finds Steineer tree and Pareto an optimal path for transmitting multimedia signals in topology and conditions of the network that is dynamically changing.
Key words
Artificial intelligence, neural network, multimedia streaming, web sites
References
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[5] Lee, H. R., Jung, T. J., Seo, K. D., & Kim, C. K. (2015). Delay constrained ARQ mechanism for MPEG media transport protocol based video streaming over Internet. AFIN 2015, 68.
[6] Xu, J., Andrepoulos, Y., Xiao, Y., & van Der Schaar, M. (2014). Non-stationary resource allocation policies for delay-constrained video streaming: Application to video over Internet-of-Things-enabled networks. IEEE Journal on Selected Areas in Communications, 32(4), 782-794.
[7] Kumar, N., Zeadally, S., & Rodrigues, J. J. (2015). QoS-aware hierarchical web caching scheme for online video streaming applications in internet-based vehicular ad hoc networks. IEEE Transactions on Industrial Electronics, 62(12), 7892-7900.
[8] L. A. Giuliano, B. M. Edwards, B. R. Wright, Interdomain Multicast Routing, Addison-Wesley, 2002.
[9] D. Chakraborty, G. Chakraborty, C. Pornovalai, N. Shiratori, “Cost Minimization for Dynamic Multicast without Rerouting”, Proceeding of the Internet Global Summit 1999, San Jose, June 22-25, 1999
[10] J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities”, Proc. Nat. Acad. Sci., Vol. 79, pp. 2554-2558, 1982.
[11] J. J. Hopfield, D. W Tank, “’Neural’ computations of decision in optimization problems”, Biol. Cybern., Vol. 52, pp. 141-152, 1985
[12] M. Ali, F. Kamoun, “Neural networks for shortest path computation and routing in computer networks”, IEEE Trans. on Neural Networks, Vol. 4, No. 6, pp. 941-953, 1993.
[13] N. Kojić, I. Reljin, B. Reljin, “Primena neuralnih mreža za dinamičko multicast rutiranje video signala”, TELFOR, Beograd , Nov. 20-22, 2007.
[14] C. Pornavalai, G. Chakraborty, N. Shiratori, “A neural network approach to multicast routing in real-time communication networks”, Third International Conference on Network Protocols (ICNP’95), pp. 332-339, 1995.
[15] Kojic, N., Reljin, I., & Reljin, B. (2013). Neural network based dynamic multicast routing. Elektronika ir Elektrotechnika, 19(3), 92-97.
