A Genetic-based Profit Maximization Algorithm for Delivering Multicast Streams in Battery-Powered Wireless Mesh Networks

碩士 === 國立臺南大學 === 資訊工程學系碩士班 === 102 === In recent years, with the growth of the mobile user, many multimedia stream services which depend on the wireless network technology become more and more popular, so that the importance of Wireless Mesh Networks (WMNs) is increasing. Multicast routing can p...

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Bibliographic Details
Main Authors: Peiheng Wang, 王培衡
Other Authors: Wen-Lin Yang
Format: Others
Language:zh-TW
Published: 2014
Online Access:http://ndltd.ncl.edu.tw/handle/52786772972395051321
Description
Summary:碩士 === 國立臺南大學 === 資訊工程學系碩士班 === 102 === In recent years, with the growth of the mobile user, many multimedia stream services which depend on the wireless network technology become more and more popular, so that the importance of Wireless Mesh Networks (WMNs) is increasing. Multicast routing can provide the service of wireless network which delivered to several destinations where a number of clients may subscribe the stream simultaneously. Therefore, the application of interference-free multicast routing is one of the most important problems in battery-powered multi-channel multi-radio WMNs. For various multicast routing problems studied on the battery-based ad hoc wireless networks, the traditional design aim is to maximize the life-time of the network. In our problem, however, we are required to maximize both the life-time "T" and the number serviced clients "N " at the same time. Given one unit of profit per second per client, it would be more reasonable to maximize "T * N" instead of "T" for the owners of networks to have maximum profit. Hence, to support this business model, a term called profit which is defined to be "T * N" is used as the optimization goal for constructing the multicast tree. A genetic-based algorithm is devised in this study to solve this problem. In this paper, we propose a function, which called ”Total Profit” to compute profit efficiency for the multicast tree. According to our experimental results, the GA-based approach can significantly outperform the other previously proposed methods.