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Trafic Prediction in MANET by Computational Intelligence Techniques

Torkamanian Afshar, Mahsa | 2010

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 40582 (52)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Manzuri, Mohammad Taghi
  7. Abstract:
  8. Mobile Ad-Hoc Networks (MANETs) have been studied as one of the most important technologies in the mid to late 1990s. There are several research works on types of network traffic modeling and prediction. Therefore, a very important issue is to make prediction on traffic-flows that each node handles. Because of this, prediction permits us to improve and increase the performance of the network. This project is a contributing effort to improve the traffic packets prediction by Neural Networks in MANET. The main goal of this thesis is about the recovery of data after crisis in phenomenal roads and highways. Our goal is recognizing phenomenal crisis-points in roads. In this thesis packets are assumed to be of constant length. Besides, along the roads the number of the cars and the speed of the cars are modeled to occur according to a Poisson processes. We show that, with a fixed scenario and obtaining traffic packets at time (t) for each node in MANET, we can completely train a Neural Network and successfully predict the traffic at time (t+1) for each node. Moreover, the results obtained shows that the prediction traffic in MANET by Neural Network can be applied to different roads structures with distinct traffic. Finally the results of both the methods (Mathematical models and Neural Networks) are compared. The traffic prediction accuracy of Neural Networks and Mathematical models is in the range of 90.6% to 99.9% and 78.5% to 80.2% respectively.
  9. Keywords:
  10. Neural Network ; Mobile Ad Hoc Network ; Computer Network Traffic Prediction

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