Restructured Ant Colony Optimization Routing Protocol for Next Generation Network

  • B. Chandramohan Anna University

Abstract

Wireless network is a major research domain in the past few decades. Wireless network evolves in many forms like cellular communication, ad hoc network, vehicular network, mesh network and sensor network. Next generation network is a recent cellular communication which provides heterogeneous connectivity on cellular communication. The routing in next generation wireless networks is an important research issue which requires many constraints than wired networks. Hence, Ant Colony Optimization (ACO) is applied in this paper for routing in heterogeneous next generation wireless network. The ACO is a swarm intelligence technique which applied for many engineering applications. ACO is an optimal technique for routing and travelling salesman problem. This paper proposed Restructured ACO which contains additional data structures for reducing packet loss and latency. Therefore, the proposed RACO provides higher throughput.

References

[1] Ali M. and Babak (2010); A. A new clustering algorithm based on hybrid global optimization based on a dynamical systems approach algorithm, Expert Systems with Applications, 37: 5645-5652.
http://dx.doi.org/10.1016/j.eswa.2010.02.047

[2] Chandramohan, B., Prasanna Kumar P, Anantha Venkata Ramana, Sridharan D (2007); Real time routing protocol (Antnet) using ACO and performance comparison with OSPF, IEEE Int. Conf. on Emerging Trends in High Performance Architecture Algorithms and Computing, 47-53.

[3] Chandramohan, B. and Baskaran, R.(2010); Improving Network Performance using ACO Based Redundant Link Avoidance Algorithm, International Journal of Computer Science Issues, 7(3): 27-35.

[4] Chandramohan, B. and Baskaran, R. (2011); Survey on Recent Research and Implementation of Ant Colony Optimization in Various Engineering Applications, International Journal in Computational Intelligent Systems, 4(4): 566-582.

[5] Dorigo, M., Maniezzo, V. and Colorni, A. (1996); Ant System: Optimization by a colony of cooperating agents, IEEE Transactions on Systems, Man, and Cybernetics, Part B, 26(1): 29-41.
http://dx.doi.org/10.1109/3477.484436

[6] Dorigo, M. and Luca, M.G.(1997); Ant Colony System: A Cooperative Learning Approach to the Traveling Salesman Problem, IEEE Transactions on Evolutionary Computation, 1(1): 53-66.
http://dx.doi.org/10.1109/4235.585892

[7] Dorigo, M. and Stutzle, T. (2004); Ant Colony Optimization, MIT Press, Cambrige MA.

[8] Hsin-Yun, L., Hao-Hsi, T., Meng-Cong, Z. and Pei-Ying, L. (2010); Decision support for the maintenance management of green areas, Expert Systems with Applications, 37: 4479-4487.
http://dx.doi.org/10.1016/j.eswa.2009.12.063

[9] Kwang, M. S. and Weng, H.S. (2003); Ant Colony Optimization for Routing and Load- Balancing: Survey and New Directions, IEEE Transactions on Systems, Man, and Cybernetics, 33(5): 60-572.

[10] Li-Ning, X., Ying-Wu, C., Peng, W., Qing-Song, Z. and Jian, X.(2010); A Knowledge- Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems, Applied Soft Computing, 10: 888-896.
http://dx.doi.org/10.1016/j.asoc.2009.10.006

[11] NS2, available online at: www.isi.edu/nsnam/ns/

[12] Osama, H.H., Tarek, N.S. and Myung, J.L. (2005); Probability Routing Algorithm for Mobile Ad Hoc Networks Resources Management, IEEE Journal on Selected Areas in Communications, 23(12): 2248-2259.
http://dx.doi.org/10.1109/JSAC.2005.857205

[13] Wang Chen, Yan-jun, S., Hong-fei, T., Xiao-ping, L. and Li-chen, H. (2010); An efficient hybrid algorithm for resource-constrained project scheduling, Information Sciences, 180: 1031-1039.
http://dx.doi.org/10.1016/j.ins.2009.11.044

[14] Wei-Neng, C., Jun Zhang, Henry Shu-Hung, C., Rui-Zhang, H. and Ou Liu (2010); Optimizing Discounted Cash Flows in Project Scheduling An Ant Colony Optimization Approach, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, DOI:10.1109/TSMCC.2009.2027335, 40(1): 64-77.
http://dx.doi.org/10.1109/TSMCC.2009.2027335

[15] Xiao-ming, Y., Sheng, L. and Yu-ming, W. (2010); Quantum Dynamic Mechanism-based Parallel Ant Colony Optimization Algorithm, International Journal of Computational Intelligence Systems, Suppl. 1, 101-113.

[16] Zhiding, Y., Oscar, C.A., Ruobing, Z., Weiyu, Y. and Jing, T. (2010); An adaptive unsupervised approach toward pixel clustering and color image segmentation, Pattern Recognition, 43: 1889-1906.
http://dx.doi.org/10.1016/j.patcog.2009.11.015
Published
2015-06-22
How to Cite
CHANDRAMOHAN, B.. Restructured Ant Colony Optimization Routing Protocol for Next Generation Network. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 10, n. 4, p. 492-499, june 2015. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/665>. Date accessed: 10 aug. 2020. doi: https://doi.org/10.15837/ijccc.2015.4.665.

Keywords

Wireless Network, Next Generation Network, Routing, Swarm Intelligence, Ant Colony Optimization