Adaptive medium access control for VoIP services in IEEE 802.11 WLANs

Abstract- Voice over Internet Protocol (VoIP) is an important service with strict Quality-of-Service (QoS) requirements in Wireless Local Area Networks (WLANs). The popular Distributed Coordination Function (DCF) of IEEE 802.11 Medium Access Control (MAC) protocol adopts a Binary Exponential Back-off (BEB) procedure to reduce the packet collision probability in WLANs. In DCF, the size of contention window is doubled upon a collision regardless of the network loads. This paper presents an adaptive MAC scheme to improve the QoS of VoIP in WLANs. This scheme applies a threshold of the collision rate to switch between two different functions for increasing the size of contention window based on the status of network loads. The performance of this scheme is investigated and compared to the original DCF using the network simulator NS-2. The performance results reveal that the adaptive scheme is able to achieve the higher throughput and medium utilization as well as lower access delay and packet loss probability than the original DCF

Mining educational data to improve students' performance: a case study

Educational data mining concerns with developing methods for discovering knowledge from data that come from educational domain. In this paper we used educational data mining to improve graduate students’ performance, and overcome the problem of low grades of graduate students. In our case study we try to extract useful knowledge from graduate students data collected from the college of Science and Technology–Khanyounis. The data include fifteen years period [1993-2007]. After preprocessing the data, we applied data mining techniques to discover association, classification, clustering and outlier detection rules. In each of these four tasks, we present the extracted knowledge and describe its importance in educational domain

Thermal – Stress Effects on TE Nonlinear Waveguide Sensors

In recent years, waveguide sensors in dielectric films have received more attention. Many theoretical studies concerning analysis of dispersion equations were introduced for many planar waveguide structures for both linear and nonlinear media. In this work, the TE electromagnetic waves in a three- layer waveguide sensors is studied. The waveguide structure is linear dielectric film bounded by two nonlinear cladding and substrate. The dispersion relation of the electromagnetic field in the proposed structure has been derived. Numerical calculations are carried out. Consequently; effect of stress and thermal- stress on the core of the structure has been studied. Temperature sensitivity is also measured. These results were simulated and presented in graphical form using software program called Maple V

A High Performance Parallel Classifier for Large-Scale Arabic Text

Text classification has become one of the most important techniques in text mining. It is the process of classifying documents into predefined categories or classes based on their content. A number of machine learning algorithms have been introduced to deal with automatic text classification. One of the common classification algorithms is the k-Nearest Neighbor (k-NN) which is known to be one of the best classifiers applied for different languages including Arabic language and it is included in numerous experiments as a basis for comparison. Furthermore, it is a simple classification algorithm and very easy to implement since it does not require a training phase that most classification algorithms must have. However, the k-NN algorithm is of low efficiency because it requires a large amount of computational power for evaluating a measure of the similarity between a test document and every training document and for sorting the similarities. Such a drawback makes it unsuitable to handle a large volume of text documents with high dimensionality and in particular in the Arabic language. In our research, we propose to develop a parallel classifier for large-scale Arabic text that achieves the enhanced level of speedup, scalability, and accuracy. The proposed parallel classifier is based on the sequential k-NN algorithm. We test the parallel classifier using the Open Source Arabic Corpus (OSAC) which is the largest freely public Arabic corpus of text documents. We study the performance of the parallel classifier on a multicomputer cluster that consists of 14 computers. We report both timing and classification results. These results indicate that the proposed parallel classifier has very good speedup and scalability and is capable of handling large documents collections. Also, classification results show that the proposed classifier has achieved accuracy, precision, recall, and F-measure with higher than 95%

An adaptive medium access control scheme for mobile ad hoc networks under self-similar traffic

This article is available through the specified link below - Copyright @ 2010 Springer.An important function of wireless networks is to support mobile computing. Mobile Ad hoc NETworks (MANETs) consist of a collection of mobile stations communicating with each other without the use of any pre-existent infrastructure. The self-organization characteristic of MANETs makes them suitable for many real-world applications where network topology changes frequently. As a result, the development of efficient MAC (Medium Access Control) protocols in MANETs is extremely challenging. Self-similar traffic with scale-invariant burstiness can generate bursty network loads and thus seriously degrade the system performance. This paper presents an adaptive MAC scheme which dynamically adjusts the increasing function and resetting mechanism of contention window based on the status of network loads. The performance of this scheme is investigated in comparison with the legacy DCF (Distributed Coordination Function) under self-similar traffic and different mobility models. The performance results reveal that the proposed scheme is able to achieve the higher throughput and energy efficiency as well as lower end-to-end delay and packet drop probability than the legacy DCF.Hong Liu’s research was supported in part by the National Science Foundation of China under Grant No. 60603058

Energy usage of UDP and DCCP over 802.11n

Date of Acceptance: 24/01/2014We show that the Datagram Congestion Control Protocol (DCCP) provides ~10% - ~40% greater energy efficiency than the User Datagram Protocol (UDP) in a wireless LAN (WLAN) client. Our empirical evaluation uses a testbed comprised of consumer components and opensource software to measure typical performance that can be expected by a user, rather than highly-tuned performance which most users will not be able to configure. We focus our measurements on a scenario using IEEE 802.11n at 5GHz as energy efficiency will be particularly important to mobile and wireless users. We consider overall performance as well as the energy efficiency of the protocol usage to give a rounded comparison of UDP and DCCP. Overall, we see there would be great benefit in many applications using DCCP instead of UDP.Postprin

Design and Evaluation of a Parallel Classifier for Large-Scale Arabic Text

Text classification has become one of the most important techniques in text mining. A number of machine learning algorithms have been introduced to deal with automatic text classification. One of the common classification algorithms is the k-NN algorithm which is known to be one of the best classifiers applied for different languages including Arabic language. However, the k-NN algorithm is of low efficiency because it requires a large amount of computational power. Such a drawback makes it unsuitable to handle a large volume of text documents with high dimensionality and in particular in the Arabic language. This paper introduces a high performance parallel classifier for large-scale Arabic text that achieves the enhanced level of speedup, scalability, and accuracy. The parallel classifier is based on the sequential k-NN algorithm. The classifier has been tested using the OSAC corpus. The performance of the parallel classifier has been studied on a multicomputer cluster. The results indicate that the parallel classifier has very good speedup and scalability and is capable of handling large documents collections with higher classification results