Neural networks based on adjustable-order statistic filters for multimedia multicast routing

Multicast routing in communication networks is to transmit information from a single source to multiple destinations, using the network resources very effectively, and respecting several constraints, such as delay, cost, bandwidth or other. To guarantee optimal diffusion, it is necessary to determine a tree that connects the source node to all destination nodes minimizing the use of resources. In this paper, we propose an artificial neural network for the construction of the multicast tree, based on adjustable-order statistic filters. Our approach for solving this problem differs from the conventional approach used in the field of neural networks. Our primary concern is how to organize neurons into a network so that it can solve a specific problem, with an emphasis on fully utilizing the massive parallelism property offered by neural networks.

[1]  Pallapa Venkataram,et al.  Neural network based optimal routing algorithm for communication networks , 2002, Neural Networks.

[2]  Jie Zhu,et al.  A genetic algorithm for finding a path subject to two constraints , 2013, Appl. Soft Comput..

[3]  Richard W. Prager,et al.  Limitations of neural networks for solving traveling salesman problems , 1995, IEEE Trans. Neural Networks.

[4]  Piet Van Mieghem,et al.  MAMCRA: a constrained-based multicast routing algorithm , 2002, Comput. Commun..

[5]  Qing Zhu,et al.  An iterative algorithm for delay-constrained minimum-cost multicasting , 1998, TNET.

[6]  Mohammed Mestari,et al.  An analog neural network implementation in fixed time of adjustable-order statistic filters and applications , 2004, IEEE Transactions on Neural Networks.

[7]  Erdun Zhao,et al.  Bandwidth-delay-constrained least-cost multicast routing based on heuristic genetic algorithm , 2001, Comput. Commun..

[8]  Wang Zhengying,et al.  Bandwidth-delay-constrained least-cost multicast routing based on heuristic genetic algorithm , 2001 .

[9]  Lipo Wang,et al.  A noisy chaotic neural network for solving combinatorial optimization problems: stochastic chaotic simulated annealing , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Hiroshi Nozawa,et al.  A neural network model as a globally coupled map and applications based on chaos. , 1992, Chaos.

[11]  Ron Widyono.  The Design and Evaluation of Routing Algorithms for Real-time Channels , 1994 .

[12]  John J. Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities , 1999 .

[13]  George C. Polyzos,et al.  Multicast routing for multimedia communication , 1993, TNET.

[14]  C. Edward Chow,et al.  On multicast path finding algorithms , 1991, IEEE INFCOM '91. The conference on Computer Communications. Tenth Annual Joint Comference of the IEEE Computer and Communications Societies Proceedings.

[15]  H.E. Rauch,et al.  Neural networks for routing communication traffic , 1988, IEEE Control Systems Magazine.

[16]  Yueh-Min Huang,et al.  Ant colony-based algorithm for constructing broadcasting tree with degree and delay constraints , 2008, Expert Syst. Appl..

[17]  Vachaspathi P. Kompella,et al.  Multicast routing algorithms for multimedia traffic , 1993 .

[18]  Sushma Jain,et al.  Delay Bound Multicast Routing Using Hopfield Neural Network , 2010 .

[19]  Sadiq M. Sait,et al.  QoS-driven multicast tree generation using Tabu search , 2002, Comput. Commun..

[20]  Yueh-Min Huang,et al.  Genetic algorithm for delay- and degree-constrained multimedia broadcasting on overlay networks , 2006, Comput. Commun..

[21]  Norio Shiratori,et al.  A neural network approach to multicast routing in real-time communication networks , 1995, Proceedings of International Conference on Network Protocols.

[22]  Ray-I Chang,et al.  Niched ant colony optimization with colony guides for QoS multicast routing , 2014, J. Netw. Comput. Appl..