QoS-aware ant routing with colored pheromones in wireless mesh networks

ABSTRACT

Inspired by the collective foraging behavior of specific ant species, ant-based routing algorithms are able to find optimal or near optimal packet routes for Wireless Mesh Networks. Ant-based algorithms work by deploying artificial pheromone at the network paths, which is then used for future routing decisions.

Using this approach, the routing can be optimized according to different criteria like packet delay, delay jitter, or maximum bandwidth. For a typical mesh network, we assume to have different classes of traffic posing different requirements on the quality of service of the communication. Therefore, we propose a concept for ant routing with colored pheromones (CPANT), where a color corresponds to a particular class of traffic. Thus, the network will treat the packets of an application according to the specific application requirements packet delay, delay jitter, and bandwidth. We show that this approach can outperform ant routing approaches that are not aware of different traffic classes when the specific traffic requirements are taken into account.

REFERENCES

• 1: 3GPP, Technical Specification Group (TSG) Services and System Aspects. Universal mobile telecommunications system (UMTS); QoS concept and architecture. Technical Specification 3G TR 23.107, ETSI, 2000.
• 2: D. Câmara and A. A. F. Loureiro. A novel routing algorithm for hoc networks. Baltzer Journal of Telecommunications Systems, Kluwer Academic Publishers, 18(1--3):85--100, 2001.
• 3: G. di Caro and M. Dorigo. Antnet: Distributed stigmergetic control for communication networks. Journal of Artificial Intelligence Research, 9:371--365, 1998.
• 4: G. di Caro, F. Ducatelle, and L. M. Gambardella. Anthocnet: An adaptive nature-inspired algorithm for routing in mobile ad hoc networks. Springer Lecture Notes in Computer Science, LNCS 3242:461--470, 2004.
• 5: C. B. et al. Distribution of Agent based Simulation with Colored Ant Algorithm. In A. Verbraeck and W. Krug, editors, Proc. 14^th European Simulation Symposium, 2002.
• 6: Imed Bouazizi, ARA - The Ant-Colony Based Routing Algorithm for MANETs, Proceedings of the 2002 International Conference on Parallel Processing Workshops, p.79, August 18-21, 2002 [doi>10.1109/ICPPW.2002.1039715]
• 7: O. Hussein, T. Saadawi, and M. Lee. Probability routing algorithm for mobile ad hoc networks. Journal on Selected Areas in Communications, 23(12):2248--2259, 2005.
• 8: Thomas Halva Labella , Falko Dressler, A bio-inspired architecture for division of labour in SANETs, Proceedings of the 1st international conference on Bio inspired models of network, information and computing systems, December 11-13, 2006, Cavalese, Italy [doi>10.1145/1315843.1315878]
• 9: V. Laxmi, L. Jain, and M. S. Gaur. Ant Colony Optimisation Based Routing on NS-2. In Intl. Conference on Wireless Communication and Sensor Networks (WCSN), India, December 2006. www.lavina.scibrary.com/antnet.
• 10: Ns-2 Network Simulator Homepage, Sept. 2008. http://nsnam.isi.edu/nsnam/index.php/Main_Page.
• 11: One Laptop per Child (OLPC), a low-cost, connected laptop for the world's children's education, Sept. 2008. http://www.laptop.org/en/laptop/hardware/index.shtml.
• 12: K. M. Sim and W. H. Sun. Ant Colony Optimization for Routing and Load-Balancing: Survey and New Directions. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 33(5), September 2003.
• 13: M. Umlauft and E. Michlmayr. Ant algorithms for routing in wireless multi-hop networks. In Y. Xiao and F. Hu, editors, Bio-inspired Computing and Communication Networks. Taylor and Francis, 2008. to appear.

INDEX TERMS

Primary Classification:
  C. Computer Systems Organization
  C.2 COMPUTER-COMMUNICATION NETWORKS

General Terms:
Algorithms

Keywords:
QoS, ant routing, bio-inspiration, self-organizing networking