Dual-Trigger Handover Algorithm for WiMAX Technology

IEEE 802.16e is a Worldwide Interoperability Microwave Access (WiMAX) standard that supports mobility. Handover is one of the most important factors that affect the performance of a WiMAX network. Various handover schemes have been proposed and implemented. In this paper, we propose Dual-Trigger Handover (DTHO) algorithm for WiMAX networks. The proposed handover algorithm depends on the computation of signal to noise ratio (SNR) received at the Mobile Station (MS) from various Base Stations (BSs). Relying on SNR measurements and free capacity measurements of the serving BS and the target BS improves the accuracy of handover decisions. The handover is not triggered by the MS node or the BS node individually. Instead, it is a combined decision between the two nodes. The proposed algorithm is implemented in both MS and BS nodes. We implemented the proposed algorithm using OPNET Modeler version 14 running on Windows operating system. The algorithm was simulated using multiple scenarios with various channel parameters

Comparison of machine learning models for classification of BGP anomalies

Worms such as Slammer, Nimda, and Code Red~I are anomalies that affect performance of the global Internet Border Gateway Protocol (BGP). BGP anomalies also include Internet Protocol (IP) prefix hijacks, miss-configurations, and electrical failures. In this Thesis, we analyzed the feature selection process to choose the most correlated features for an anomaly class. We compare the Fisher, minimum redundancy maximum relevance (mRMR), odds ratio (OR), extended/multi-class/weighted odds ratio (EOR/MOR/WOR), and class discriminating measure (CDM) feature selection algorithms. We extend the odds ratio algorithms to use both continuous and discrete features. We also introduce new classification features and apply Support Vector Machine (SVM) models, Hidden Markov Models (HMMs), and Naive Bayes (NB) models to design anomaly detection algorithms. We apply multi classification models to correctly classify test datasets and identify the correct anomaly types. The proposed models are tested with collected BGP traffic traces from RIPE and BCNET and are employed to successfully classify and detect various BGP anomalies

DCRA : Decentralized Cognitive Resource Allocation model for game as a service

Gaming-as-a-Service (GaaS) has rapidly emerged to the industry of cloud gaming. The power of GaaS lies on having one source code base with multiple users. Several systems were proposed to model GaaS. However, there are no scalable and reliable models for such a service. The importance of having such a model lies on having an Internet-scale platform able to provide flexibility of different types of games genre and lower the barrier of end systems (i.e. mobile clients) while taking into consideration the probability of excessive loads and failures. We present a Distributed Cognitive Resource Allocation (DCRA) model to run mobile games on a large-scale distributed system in which we have improvised a unique distributed hash table (DHT)-based routing to expedite the messaging among servers and to minimize the round trip delay to acceptable levels for the targeted mobile games genre. In contrast to existing centralized models, DCRA scales with the increase of mobile clients to handle high concurrent loads of clients' requests while providing a stable level of gaming experience. The results show that DCRA is able to scale well by providing almost fixed throughput and delay while increasing the clients requests load. Also, the system preserve its key features while simulating failures.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat