Ahmad Al-Khasawneh

Enhanced Fast Spread Replication strategy for Data Grid

Data replication is used in Data Grid to enhance data availability and fault tolerance. However, replication should be used wisely because the storage size of each node reside on the Data Grid is limited. Thus, the node must accommodate only the important replicas. In this paper, a dynamic replication strategy that takes into account the number and frequency of requests, the size of the replica, and the last time the replica was requested is proposed. This strategy is an enhanced version of Fast Spread replication strategy. The simulation results show that the new proposed strategy attained better performance than Fast Spread with Least Recently Used (LRU) and Fast Spread with Least Frequently Used (LFU) in terms of total response time and total bandwidth consumption.

An Energy-Efficient Threshold-Based Clustering Protocol for Wireless Sensor Networks

Energy-constrained wireless sensor networks (WSNs) have been deployed widely for monitoring and surveillance purposes. Since sensor nodes (SNs) have significant power constraints (battery life), energy-efficient protocols must be employed to prolong the network lifetime. In this paper, we propose an energy-efficient protocol which provides a new way of creating distributed clusters. This protocol is a modified version of Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. The experimental results show that our protocol that takes into account both the residual energy at each SN and the distance between the SNs outperforms LEACH protocol in terms of first node death time and average residual energy.

A threshold-based dynamic data replication strategy

Data replication is the creation and maintenance of multiple copies of the same data. Replication is used in Data Grid to enhance data availability and fault tolerance. One of the main objectives of replication strategies is reducing response time and bandwidth consumption. In this paper, a dynamic replication strategy that is based on Fast Spread but superior to it in terms of total response time and total bandwidth consumption is proposed. This is achieved by storing only the important replicas on the storage of the node. The main idea of this strategy is using a threshold to determine if the requested replica needs to be copied to the node. The simulation results show that the proposed strategy achieved better performance compared with Fast Spread with Least Recently Used (LRU), and Fast Spread with Least Frequently Used (LFU).

Enhancing mobile advertising via Bluetooth technology

This paper presents a Multimedia Message Transmitter Tool (MMTT) that provides businesses with the opportunity to use Bluetooth wireless connectivity delivering the relevant information to relevant people in the right places and time. MMTT empowers any business to market their services or products for unlimited time with a one-time fee. It provides the ability to deliver customised content on a permission-based manner to all devices in the range of 100 m to 1 km. MMTT supports users right of privacy and ensures spam-free delivery of content to interested users. Experimental results show that MMTT provides a viable solution for realising mobile advertising for the advertisers as well as the consumers.

Models for using internet technology to support flexible e-learning

Information and Communication Technologies (ICTs) have been diffused in higher education slower than it has been expected. Especially for developing countries, using ICT in the most efficient and effective ways has crucial importance for the sustainable development and effective use of resources. This paper presents a theory-based design framework for e-learning that emphasises the transformative interaction between technology readiness and innovativeness. This study is a theory-building investigation and offers a theory-into-practice framework that characterises the implications of innovativeness and motivation in the recent trend of the higher education use of internet technology to facilitate its own operations, in the form of e-learning.

A Predictive E-Health Information System: Diagnosing Diabetes Mellitus Using Neural Network Based Decision Support System

Diabetes Mellitus is a chronic disease and a major cause of several severe complications and death in both developing and developed countries. The number of diabetes cases world-wide has been climbed up drastically over last decades. Hence, it was of utmost important to manage this illness and to develop tools that help clinicians do their job professionally. Artificial neural networks play a major role herein. In this research, a clinical decision support system that helps in diagnosing diabetes has been developed. The system was implemented using a multilayer perceptron artificial neural network. Due to the fact that there is no systematic way to follow in order to determine the number of hidden layers and neurons in MLP, an algorithm was proposed and followed based on the rules-of-thumb previously defined around this issue. As a result, two different topologies were trained and verified using cross validation technique. The topology that exhibited the best averaged accuracy was that of one hidden layer. The data set was obtained from King Abdullah University Hospital in Jordan.

A dynamic replication strategy based on categorization for Data Grid

Data Replication is copying the data from a certain location to another location. Replication is used in Data Grid to have two or more copies of the same data at different locations. In this paper, a Category-based dynamic replication strategy (CDRS) is proposed. The strategy takes into account that the replicas exist on a node belong to different categories. Each of these categories is given a value that determines its importance for the node. When the nodes storage is full, the node starts to store only the replicas that belong to the category with the highest value. The results of the simulations show that the new proposed strategy achieved better performance than Plain Caching and Fast Spread strategies in terms of total transit time and total bandwidth consumption.

Optimal spectrum utilisation in cognitive network using combined spectrum sharing approach: overlay, underlay and trading

Cognitive radio technology enables unlicensed users (secondary users, SUs) to access the unused spectrum. In the literature, there are three spectrum sharing paradigms that enable SUs to access the licensed spectrum. These access techniques include underlay, overlay and spectrum trading, and have their own drawbacks. To combat these drawbacks, we propose a new approach for each of them and merge them into one combined system. Our overlay scheme provides quick access to the unused spectrum. We propose a new cooperative sensing protocol to reduce the likelihood of interfering with PUs. In order to enable SUs for transmitting simultaneously with PUs, we suggest using our underlay scheme. Our trading scheme allows PUs to trade the unused spectrum for the SUs that require better quality of service. The new combined scheme increases the size of spectrum in the cognitive network. Simulation results show the ability of the new scheme to serve extra traffic.

Resource trading in cloud environments for utility maximisation using game theoretic modelling approach

We develop a novel auction-based scheme for trading free processors in cloud computing environment. Our scheme allows clients to fairly compete for the offered processors. Second-price auction is used where each client bids for the processors. The client that makes the higher bid wins the auction and starts using the processors for executing its jobs. The winner pays the second highest bid. Maximising profit is the key objective for the cloud provider (CP). The optimisation problem of renting processors on the cloud market is a complex and challenging task. The proposed mechanism takes into account the market demand and the economic concerns for the CP and clients. Within this context, we show the existence of a Nash equilibrium which leads to a unique allocation under certain processors distribution. Numerical analysis stresses the ability of our approach to maximise the clients utility under varying cloud environment conditions.

A survey of the satisfiability-problems solving algorithms

The propositional satisfiability SAT problem is one of the most fundamental problems in computer science. SAT solvers have been successfully applied to a wide range of practical applications, including hardware model checking, software model finding, equivalence checking, and planning, among many others. Empirical research has been very fruitful for the development of efficient methods for SAT problems, such as classical Davis-Putnam method, greedy SAT GSAT method and neural network SAT method. This paper gives a survey about the methods used for solving the SAT problems with an emphasis on surveying the local search algorithms.