Abstract Ad-hoc On Demand Distance Vector (AODV) routing protocol has been one of the prominent routing protocols for Mobile Ad hoc Networks. AODV uses the hop count as a path selection metric. However, routing based on the hop count may not lead to optimal route selection. In this paper we propose a cross layered based routing protocol called Turbo-AODV (TAODV) where... Ad-hoc On Demand Distance Vector (AODV) routing protocol has been one of the prominent routing protocols for Mobile Ad hoc Networks. AODV uses the hop count as a path selection metric. However, routing based on the hop count may not lead to optimal route selection. In this paper we propose a cross layered based routing protocol called Turbo-AODV (TAODV) where we modify AODV to calculate the weight of a certain route based on three factors, and then choose the route with the best weight. The three factors that make the base of route selection are: the received signal strength, the remaining energy and the remaining queue length. The route selection based on the following criteria ensures choosing the most stable route which has the less congested links. Performance evaluation through simulations showed that the proposed protocol performs better than AODV protocol in terms of packet delivery, normalized routing load, discarded packets on queues, and routing overhead. Read More Read Less

Abstract To meet the increased expectations and demand for higher data rates in Wireless LANs (WLANs) several new physical layer technologies were developed. However, the actual achievable throughput of WLANs is restricted by the overheads of IEEE 802.11 medium access control (MAC) layer. Several frame aggregation mechanisms have been proposed to improve the MAC layer performance in 802.11n. This paper models... To meet the increased expectations and demand for higher data rates in Wireless LANs (WLANs) several new physical layer technologies were developed. However, the actual achievable throughput of WLANs is restricted by the overheads of IEEE 802.11 medium access control (MAC) layer. Several frame aggregation mechanisms have been proposed to improve the MAC layer performance in 802.11n. This paper models and analyzes some of the proposed key aggregation mechanisms under ideal and error-prone channel conditions. For analysis we use well known Bianchi’s analytical model and apply it on various aggregation strategies. We also compare the analytical details of various techniques and provide a unified analytical framework for continued research in this direction. Read More Read Less

Abstract This paper describes the use of data mining techniques to solve three important issues in network intrusion detection problems. The first goal is finding the best dimensionality reduction algorithm which reduces the computational cost while still maintains the accuracy. We implement both feature extraction (Principal Component Analysis and Independent Component Analysis) and feature selection (Genetic Algorithm and Particle Swarm Optimization)... This paper describes the use of data mining techniques to solve three important issues in network intrusion detection problems. The first goal is finding the best dimensionality reduction algorithm which reduces the computational cost while still maintains the accuracy. We implement both feature extraction (Principal Component Analysis and Independent Component Analysis) and feature selection (Genetic Algorithm and Particle Swarm Optimization) techniques for dimensionality reduction. The second goal is finding the best algorithm for misuse detection system to detect known intrusion. We implement four basic machine learning algorithms (Naïve Bayes, Decision Tree, Nearest Neighbour and Rule Induction) and then apply ensemble algorithms such as bagging, boosting and stacking to improve the performance of these four basic algorithms. The third goal is finding the best clustering algorithms to detect network anomalies which contains unknown intrusion. We analyze and compare the performance of four unsupervised clustering algorithms (k-Means, k-Medoids, EM clustering and distance-based outlier detection) in terms of accuracy and false positives. Our experiment shows that the Nearest Neighbour (NN) classifier when implemented with Particle Swarm Optimization (PSO) as an attribute selection algorithm achieved the best performance, which is 99.71% accuracy and 0.27% false positive. The misuse detection technique achieves a very good performance with more than 99% accuracy when detecting known intrusion but it fails to accurately detect data set with a large number of unknown intrusions where the highest accuracy is only 63.97%. In contrast, the anomaly detection approach shows promising results where the distance-based outlier detection method outperforms the other three clustering algorithms with the accuracy of 80.15%, followed by EM clustering (78.06%), k-Medoids (76.71%), improved k-Means (65.40%) and k-Means (57.81%). Read More Read Less

Abstract Teaching children with intellectual disabilities is a challenging task. Instructors should repeat the same lessons several times. They should use also different methodologies to introduce the concepts and motivate the children and keep them engaged. These methodologies include: reading texts, showing images, touching items, taking children to sites, and even using the taste and smell senses. The objective of this... Teaching children with intellectual disabilities is a challenging task. Instructors should repeat the same lessons several times. They should use also different methodologies to introduce the concepts and motivate the children and keep them engaged. These methodologies include: reading texts, showing images, touching items, taking children to sites, and even using the taste and smell senses. The objective of this work is to develop a system that can assist the children to improve their understanding of simple educational texts related to specific domain through multimedia technology. We use formal concepts analysis to extract keywords from the texts and generate the graphical user interfaces. We link the keywords with the multimedia elements (i.e., images and clips) which are retrieved from Google search engine by using Google APIs features. We build also databases of keywords with their synonyms in both Arabic and English and the corresponding multimedia elements. Every student can have his/her own database that contains the objects that he/she learnt. The instructors can select the appropriate multimedia elements according to the levels of theirs students. They can input the education text, set the domain, and build specific multimedia contents that can fit with every student in the classroom. Read More Read Less

Abstract Server based computing performance can be affected by several factors. Some previous researches on performance of Server based computing came up with factors which falls to the category of server itself and on the network infrastructure. This research explores the performance behaviour of Thin Client terminals by measuring the performance while changing the performance optimisation configurations on the client side.... Server based computing performance can be affected by several factors. Some previous researches on performance of Server based computing came up with factors which falls to the category of server itself and on the network infrastructure. This research explores the performance behaviour of Thin Client terminals by measuring the performance while changing the performance optimisation configurations on the client side. These measurements are measured at default and with performance configurations set while analysing the response of performance metrics to observe their impact. The analysis shows that compression, caching and merging tends to have significant impact to the overall performance of server based computing. The measurement approach used is slow-motion benchmarking. In some cases, performance behaviour is compared with the previous performance measurement research done nearly 8 years ago for more discussions. Read More Read Less