Investigation of Vertical Handoff Techniques in Integrated WLAN/Cellular Networks and Performance Analysis of Horizon Handoff in WiMax Networks

A thesis presented to the faculty of the College of Science and Technology at Morehead State University in partial fulfillment of the requirement for the Degree of Master of Science by Elaheh Arabmakki May 9, 2011

A reduced labeled samples (RLS) framework for classification of imbalanced concept-drifting streaming data.

Stream processing frameworks are designed to process the streaming data that arrives in time. An example of such data is stream of emails that a user receives every day. Most of the real world data streams are also imbalanced as is in the stream of emails, which contains few spam emails compared to a lot of legitimate emails. The classification of the imbalanced data stream is challenging due to the several reasons: First of all, data streams are huge and they can not be stored in the memory for one time processing. Second, if the data is imbalanced, the accuracy of the majority class mostly dominates the results. Third, data streams are changing over time, and that causes degradation in the model performance. Hence the model should get updated when such changes are detected. Finally, the true labels of the all samples are not available immediately after classification, and only a fraction of the data is possible to get labeled in real world applications. That is because the labeling is expensive and time consuming. In this thesis, a framework for modeling the streaming data when the classes of the data samples are imbalanced is proposed. This framework is called Reduced Labeled Samples (RLS). RLS is a chunk based learning framework that builds a model using partially labeled data stream, when the characteristics of the data change. In RLS, a fraction of the samples are labeled and are used in modeling, and the performance is not significantly different from that of the 100% labeling. RLS maintains an ensemble of classifiers to boost the performance. RLS uses the information from labeled data in a supervised fashion, and also is extended to use the information from unlabeled data in a semi supervised fashion. RLS addresses both binary and multi class partially labeled data stream and the results show the basis of RLS is effective even in the context of multi class classification problems. Overall, the RLS is shown to be an effective framework for processing imbalanced and partially labeled data streams

An Algorithm for Optimizing Vertical Handoff between WLAN and Cellular Networks

Today, the advent of heterogeneous wireless networks has caused a revolution in the telecommunication systems. As IP-based wireless networking increases in popularity, the handoff issue is taken into the consideration. In the vertical handoff in which users switch between networks under different technologies, many factors should be considered in order to increase the efficiency of the network. An analytical model using absolute signal strength has been previously developed for evaluating the handoff algorithm. In this paper we designed a vertical handoff algorithm and extend that analytical model by adding two different models. The models are for handoff from Wireless Local Area Network (WLAN) to cellular network for the voice session and for handoff from cellular network to WLAN for the data session based on Received Signal Strength (RSS) and application type. Since the RSS is considered in this model, it is predicted this algorithm reduces the number of vertical handoffs

A Comparison of Different Vertical Handoff Algorithms between WLAN and Cellular Networks

Today, the advent of heterogeneous wireless networks is revolutionizing the telecommunications industry. As IP based wireless networking increases in popularity, handoff issues must be taken into consideration. When a user switches between networks with different technologies, many issues have to be considered in order to increase the efficiency of the network during vertical handoff. In this paper, several algorithms for optimizing vertical handoff between WLAN and the cellular networks are discussed. In each of these algorithms, specific factors were considered and then a comparison made in order to see the effect of each factor on vertical handoff. It was found that when both received signal strength and service history information are taken into account in algorithm design, the number of handoffs would be reduced and the throughput of the network increased