Abstract
A theoretical framework and model is presented to study the self-organized behavior of probabilistic routing protocols for computer networks. Such soft routing protocols have attracted attention for delivering packets reliably, robustly, and efficiently. The framework supports several features necessary for emergent routing behavior, including feedback loops and indirect communication between peers. Efficient global operating parameters can be estimated without resorting to expensive monte-carlo simulation of the whole system. Key model parameters are routing sensitivity and routing threshold, or noise, which control the “randomness” of packet routes between source and destination, and a metric estimator. Global network characteristics are estimated, including steady state routing probabilities, average path length, and path robustness.
The framework is based on a markov chain analysis. Individual network nodes are represented as states. Standard techniques are used to find primary statistics of the steady state global routing pattern, given a set of link costs. The use of packets to collect information about, or “sample,” the network for new path information is also reviewed. How the network sample rate influences performance is investigated.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Tannenbaum, A.: Computer Networks. Prentice Hall PTR, Englewood Cliffs (2002)
Boyan, J., Littman, M.: Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach. In: Advances in Neural Information Processing Systems, Morgan Kaufmann, San Francisco (1993)
Schoonderwoerd, R., Holland, O., Bruten, J., Rothkrantz, L.: Ant-Based Load Balancing In Telecommunications Networks. Adaptive Behavior (1996)
Di Caro, G., Dorigo, M.: Mobile Agents for Adaptive Routing. Technical Report, IRIDIA/97-12, Université Libre de Bruxelles, Belgium (1997)
Heusse, M., Snyers, D., Guérin, S., Kuntz, P.: Adaptive Agent-Driven Routing and Local Balancing in Communication Networks. ENST de Bretagne Technical Report RR-98001-IASC (1997)
Baras, J., Mehta, H.: A Probabilistic Emergent Routing Algorithm for Mobile Ad-hoc Networks. In: WiOpt 2003: Modeling and Optimization in Mobile, Ad-Hoc, and Wireless Networks (2003)
Günes, M., Kähmer, M., Bouazizi, I.: Ant Routing Algorithm (ARA) for Mobile Multi-Hop Ad-Hoc Networks - New Features and Results. In: The Second Mediterranean Workshop on Ad-Hoc Networks (2003)
Heissenbüttel, M., Braun, T.: Ants-Based Routing in Large Scale Mobile Ad-Hoc Networks. In: Kommunikation in Verteilten Systemen (KiVS) (2003)
Sim, K.M., Sun, W.H.: 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) (2003)
Roth, M., Wicker, S.: Termite: A Swarm Intelligent Routing Algorithm for Mobile Wireless Ad-Hoc Networks. Springer SCI Series: Swarm Intelligence and Data Mining. Springer, Heidelberg (2005)
Rajagopalan, S., Shen, C.: A Routing Suite for Mobile Ad-hoc Networks using Swarm Intelligence (2004) (Unpublished)
Ducatelle, F., Di Caro, G., Gambardella, L.M.: Using Ant Agents to Combine Reactive and Proactive Strategies for Routing in Mobile Ad-Hoc Networks. Technical Report No. IDSIA-28-04-2004 (2004)
Wedde, H., Farooq, M.: The Wisdom of the Hive Applied to Mobile Ad-hoc Networks. In: IEEE Swarm Intelligence Symposium 2005 (SIS 2005) (2005)
Dowling, J., Curran, E., Cunningham, R., Cahill, V.: Using Feedback in Collaborative Reinforcement Learning to Adaptively Optimise MANET Routing. IEEE Transactions on Systems, Man and Cybernetics (Part A), Special Issue on Engineering Self-Orangized Distributed Systems 35(3), 360–372 (2005)
Perkins, P., Royer, E.: Ad-hoc On-demand Distance Vector. In: Proceedings of the 2nd IEEE Workshop on Mobile Computing Systems and Applications (1999)
Subramanian, D., Druschel, P., Chen, J.: Ants and Reinforcement Learning: A Case Study in Routing in Dynamic Networks. In: Proceedings of the International Joint Conference on Artificial Intelligence (1997)
Roth, M., Wicker, S.: Asymptotic Pheromone Behavior in Swarm Intelligent MANETs: An Analytical Analysis of Routing Behavior. In: Sixth IFIP IEEE International Conference on Mobile and Wireless Communications Networks (MWCN) (2004)
Meuleau, N., Dorigo, M.: Ant Colony Optimization and Stochastic Gradient Descent. Artificial Life 8 (2002)
Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant Algorithms for Discrete Optimization. Artificial Life 5(2) (1999)
Eberhart, R., Kennedy, J.: Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks (1995)
Gaertner, D., Clark, K.: On Optimal Parameters for Ant Colony Optimization algorithms. In: The International Conference on Artificial Intelligence (ICAI) (2004)
Brockwell, P.J., Davis, R.A.: Introduction to Time Series and Forecasting. Springer-Verlag, Heidelberg (2002)
Norris, J.: Markov Chains. Cambridge University Press, Cambridge (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Roth, M. (2006). A Framework and Model for Soft Routing: The Markovian Termite and Other Curious Creatures. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2006. Lecture Notes in Computer Science, vol 4150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11839088_2
Download citation
DOI: https://doi.org/10.1007/11839088_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38482-3
Online ISBN: 978-3-540-38483-0
eBook Packages: Computer ScienceComputer Science (R0)