105 research outputs found
Architectural aspects of QoS-aware personal networks
Personal Networks (PN) are future communication systems that combine wireless and infracuture based networks to provide users a variety of services anywhere and anytime. PNs introduce new design challenges due to the heterogeneity of the involved technologies, the need for self-organization, the dynamics of the system composition, the application-driven nature, the co-operation with infrastructure-based networks, and the security hazards. This paper discusses the challenges of security and QoS provisioning in designing self-organized personal networks and combines them all into an integrated architectural framework
Prediction-based Decentralized Routing Algorithm
We introduce a new efficient routing algorithm called Prediction-based Decentralized Routing algorithm (PDR), which is based on the Ant Colony Optimization (ACO) meta-heuristics. In our approach, an ant uses a combination of the link state information and the predicted link load instead of the ant's trip time to determine the amount of pheromone to deposit. A Feed Forward Neural Network (FFNN) is used to build adaptive traffic predictors which capture the actual traffic behaviour. We study two performance parameters: the rejection ratio and the percentage of accepted bandwidth under two different network load conditions. We show that our algorithm reduces the rejection ratio of requests and achieves a higher throughput when compared to Shortest Path First and Widest Shortest Path algorithms
Dynamic routing optimization using traffic prediction
In this dissertation, a new efficient routing maintenance algorithm, called Predicting of Future Load-based Routing (PFLR), is introduced for optimizing the routing performance in IP-based networks. The main idea of PFLR algorithm is combing the predicted link load with the current link load with an effective method to optimize the link weights and so reduce the network congestions. Another research objective is introducing a new efficient Traffic Engineering (TE) algorithm, called Prediction-based Decentralized Routing (PDR) algorithm, which is fully decentralized and self-organized approach
Prediction-based decentralized routing algorithm
We introduce a new efficient routing algorithm called Prediction-based Decentralized Routing algorithm (PDR), which is based on the Ant Colony Optimization (ACO) meta-heuristics. In our approach, an ant uses a combination of the link state information and the predicted link load instead of the ant’s trip time to determine the amount of pheromone to deposit. A Feed Forward Neural Network (FFNN) is used to build adaptive traffic predictors which capture the actual traffic behaviour. We study two performance parameters: the rejection ratio and the percentage of accepted bandwidth under two different network load conditions. We show that our algorithm reduces the rejection ratio of requests and achieves a higher throughput when compared to Shortest Path First and Widest Shortest Path algorithms
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