Browsing by Author "Wang, Po-Fan"

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Design and Implementation of A more Realistic Radio Propagation Model for Wireless Vehicular Networks over the NCTUns Network Simulator
(2011)
Wang, Shie-Yuan
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Wang, Po-Fan
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Li, Yu-Wei
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Lau, Lee-Chin
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資訊工程學系
Today, Intelligent Transportation System (ITS) applications has made a great impact on improving the quality and efficiency of the transportation system. This achievement has attracted researchers to propose more ITS applications, particularly to improve the transportation safety through Vehicular Ad Hoc Networks (VANETs). The major difficulty faced by these researchers is the verification of their proposals or ideas through field-trials or real-world experiments. Therefore, VANET simulator becomes the best and the most economical alternative to verify the validity and reliability of a proposed ITS application. Among all the VANET simulators, the NCTUns 6.0 appears as one of the most powerful tool due to its integrated platform of network simulation and microscopic traffic simulation. To further enhance the capability of NCTUns 6.0, we design and implement a more realistic radio propagation model, specifically for VANETs simulation. Through extensive simulations, it is shown that the adoption of a more realistic channel model has greatly affected the vehicles' behavior, as compared to the commonly used two-ray ground channel model.
Optimizing the Cloud Platform Performance for Supporting Large-Scale Cognitive Radio Networks
(2012)
Wang, Shie-Yuan
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Wang, Po-Fan
;
Chen, Pi-Yang
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資訊工程學系
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Department of Computer Science
In this paper, we optimize the performance of a cloud platform to effectively support cooperative spectrum sensing in a cognitive radio (CR) cloud network. This cloud uses the Apache Hadoop platform to run a cooperative spectrum sensing algorithm in parallel over multiple servers in the cloud. A cooperative spectrum sensing algorithm needs to process a very large number of spectrum sensing reports per second to quickly update its database that stores the current activities of all primary users of the CR network. Because the updates of the database must be finished as soon as possible to make the CR approach effective, the cloud platform must be able to run the algorithm in real time with as little overhead as possible. In this work, we first measured the execution time of such an algorithm over our own cloud and the Amazon EC2 public cloud, using the original Hadoop platform design and implementation. We found that the original Hadoop platform has too much fixed overhead and incurs too much delay to the cooperative spectrum sensing algorithm, which makes it unable to update the primary user database in just a few seconds. Therefore, we studied the source code and the design and implementation of the Hadoop platform to improve its performance. Our experimental results show that our improvement of the Hadoop platform can significantly reduce the required time of the cooperative spectrum sensing algorithm and make it more suitable for large-scale CR networks.