Abstract
Using the metaphor of swarm intelligence, ant-based routing protocols deploy control packets that behave like ants to discover and optimize routes between pairs of nodes. These ant-based routing protocols provide an elegant, scalable solution to the routing problem for both wired and mobile ad hoc networks. The routing problem is highly nonlinear because the control packets alter the local routing tables as they are routed through the network. We mathematically map the local rules by which the routing tables are altered to the dynamics of the entire networks. Using dynamical systems theory, we map local protocol rules to full network performance, which helps us understand the impact of protocol parameters on network performance. In this paper, we systematically derive and analyze global models for simple ant-based routing protocols using both pheromone deposition and evaporation. In particular, we develop a stochastic model by modeling the probability density of ants over the network. The model is validated by comparing equilibrium pheromone levels produced by the global analysis to results obtained from simulation studies. We use both a Matlab simulation with ideal communications and a QualNet simulation with realistic communication models. Using these analytic and computational methods, we map out a complete phase diagram of network behavior over a small multipath network. We show the existence of both stable and unstable (inaccessible) routing solutions having varying properties of efficiency and redundancy depending upon the routing parameters. Finally, we apply these techniques to a larger 50-node network and show that the design principles acquired from studying the small model network extend to larger networks.
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References
Bean, N., & Costa, A. (2005). An analytic modelling approach for network routing algorithms that use “ant-like” mobile agents. Computer Networks, 49(2), 243–268.
Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence. From natural to artificial systems. New York: Oxford University Press.
Di Caro, G., & Dorigo, M. (1998). AntNet: distributed stigmergetic control for communications networks. Journal of Artificial Intelligence Research, 9, 317–365.
Di Caro, G., Ducatelle, F., & Gambardella, L.M. (2005). AntHocNet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks. European Transactions on Telecommunications, Special Issue on Self-organization in Mobile Networking, 16(5), 443–455.
Ducatelle, F., Di Caro, G., & Gambardella, L.M. (2010). Principles and applications of swarm intelligence for adaptive routing in telecommunications networks. Swarm Intelligence, 4(3). doi:10.1007/s11721-010-0040-x.
Purkayastha, P., & Baras, J. S. (2007). Convergence results for ant routing algorithms via stochastic approximation and optimization. In Proceedings of the 46th IEEE conference on decision and control (pp. 340–345). New York: IEEE Press.
Rajagopalan, S., & Shen, C. C. (2006). ANSI: a swarm intelligence-based unicast routing protocol for hybrid ad hoc networks. Journal of System Architecture, Special Issue on Nature Inspired Applied Systems, 52(8–9), 485–504.
Roth, M. (2007). The Markovian termite: a soft routing framework. In Proceedings of the 2007 IEEE swarm intelligence symposium (SIS). New York: IEEE Press.
Roth, M., & Wicker, S. (2004). Asymptotic pheromone behavior in swarm intelligent MANETs. In Proceedings of the sixth IFIP/IEEE international conference on mobile and wireless communication networks (pp. 335–346). Boston: Springer.
Saleem, M., & Farooq, M. (2007). A framework for empirical evaluation of nature inspired routing protocols for wireless sensor networks. In Proceedings of the IEEE congress on evolutionary computing (pp. 751–758). New York: IEEE Press.
Saleem, M., Khayam, S., & Farooq, M. (2008a). Formal modeling of BeeAdHoc: a bio-inspired mobile ad hoc network routing protocol. In 6th international conference on Ant Colony Optimization and Swarm Intelligence (pp. 315–322). Berlin: Springer.
Saleem, M., Khayam, S., & Farooq, M. (2008b). A formal performance modeling framework for bio-inspired ad hoc routing protocols. In ACM genetic and evolutionary computation conference (GECCO) (pp. 103–110). New York: ACM.
Strogatz, S. H. (1994). Nonlinear dynamics and chaos. Reading: Perseus Books Publishing.
Yoo, J. H., La, R. J., & Makowski, A. M. (2004). Convergence results for ant routing. In Conf. info. sci. and systems. New York: IEEE Press.
Zahid, S., Shahzad, M., Ali, S., & Farooq, M. (2007). A comprehensive formal framework for analyzing the behavior of nature-inspired routing protocols. In IEEE congress on evolutionary computation, 2007. CEC 2007 (pp. 180–187). New York: IEEE Press. doi:10.1109/CEC.2007.4424470.
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Torres, C.E., Rossi, L.F., Keffer, J. et al. Modeling, analysis and simulation of ant-based network routing protocols. Swarm Intell 4, 221–244 (2010). https://doi.org/10.1007/s11721-010-0043-7
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DOI: https://doi.org/10.1007/s11721-010-0043-7