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The problem of network selection across heterogeneous wireless networks has recently received much attention because of a drive for converged communication systems. However, since the selection of an access network depends on several parameters with different relative importance, such as the network and the application characteristics, the user preferences, the service cost, etc, it is a difficult task to be achieved. In this paper an effective access network selection algorithm for heterogeneous wireless networks is proposed that combines two Multi Attribute Decision Making (MADM) methods, the Analytic Hierarchy Process (AHP) method and the fuzzy Total Order Preference by Similarity to the Ideal Solution (TOPSIS) method. More specifically, the AHP method is used to determine weights of the criteria, and the fuzzy TOPSIS method is used to obtain the final access network ranking. [ABSTRACT FROM AUTHOR]

RFID is an emerging technology intends to replace traditional barcode. Compared to barcode, RFID has the practical advantages of improving total product traceability and increasing accuracy. Especially healthcare service is life-critical, so that any careless mistakes may result in reversible loss. Due to the huge demand for automatic identification, many corporations and researchers devoted their efforts in RFID approaches for better achieving their goals. However, since there are various RFID solutions on the markets, it is critical to select the optimal solution to fit the desired scenarios. RFID deployment and implementation require not only technology concern, but also financial investment and user involvement. In this paper, we applied fuzzy TOPSIS to effectively evaluate suitable RFID solution providers. The proposed framework intends to help decision makers better evaluate the important factors affecting RFID implementation, forecasting the probability of a successful RFID project, as well as identifying the actions necessary before implementation. In addition, an experimental case study is presented to illustrate the application of the proposed approach. [ABSTRACT FROM AUTHOR]

Web sources are open, dynamic, and autonomous. They contain a great deal of incomplete, imprecise, and unqualified information. These issues result in unacceptable Web source quality. Evaluating and selecting high quality Web source/information is a key for the success of Web-based applications. In this paper, Web quality is modeled by using a Web quality model, WebQM. Fuzzy TOPSIS (FTOPSIS) is applied to evaluate and screen Web sources for advanced Web applications, such as data warehousing, OLAP, and data mining. In addition, an expert-average group evaluation strategy is combined with FTOPSIS to obtain more objective and more precise results. To illustrate our evaluation process, an example is discussed. [ABSTRACT FROM AUTHOR]