Abstract
In this paper, we introduce an adaptive traffic prediction approach for self-optimizing the performance of a Prediction-based Decentralized Routing (PDR) algorithm. The PDR algorithm is based on the Ant Colony Optimization (ACO) meta-heuristics in order to compute the routes. In this approach, an ant uses a combination of the link state information and the predicted available bandwidth instead of the ant’s trip time to determine the amount of deposited pheromone. A Feed Forward Neural Network (FFNN) is used to build adaptive traffic predictors which capture the actual traffic behavior. Our contribution is a new self-optimizing mechanism which is able to locally adapt the prediction validity period depending on the prediction accuracy in order to efficiently predict the link traffic. We study three performance parameters: the rejection ratio, the percentage of accepted bandwidth and the effect of prediction use. In general, our new algorithm reduces the rejection ratio of requests, achieves higher throughput when compared to the AntNet and Trail Blazer algorithms.
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© 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Turky, A.A., Liers, F., Mitschele-Thiel, A. (2012). Self-optimizing Mechanism for Prediction-Based Decentralized Routing. In: Zhang, X., Qiao, D. (eds) Quality, Reliability, Security and Robustness in Heterogeneous Networks. QShine 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 74. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29222-4_32
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DOI: https://doi.org/10.1007/978-3-642-29222-4_32
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