Showing papers by "Yaser Jararweh published in 2016"

The future of mobile cloud computing: Integrating cloudlets and Mobile Edge Computing

Abstract: Extending the coverage area of mobile cloud computing services will allow new services to be provisioned to the mobile users. The main obstacle for achieving this goal is related to the deployments challenges and limitations of the Cloudlets system. Mobile Edge Computing (MEC) system emerged recently providing an opportunity to fill the gap of the Cloudlets system by providing resources-rich computing resources with proximity to the end users. In this paper, we are proposing a hierarchical model that is composed of MEC servers and Cloudlets infrastructures. The objective of the proposed model is to increase the coverage area for the mobile users in which the users can accomplish their requested services with minimal costs in terms of power and delay. An extensive experimental evaluation is conducted showing the superiority of the proposed model.

SDMEC: Software Defined System for Mobile Edge Computing

Abstract: Mobile Edge Computing (MEC) promises a paradigm shift in enabling efficient Mobile Cloud Computing (MCC) services by providing storage and processing capacity within the access range of the mobile devices. In MEC, Mobile Edge (ME) servers are placed at the edge of the mobile networks eliminating the need to offload compute-/storage-intensive tasks of the mobile devices to the core of the network (the centralized cloud data center). This reduces the network latency and enhances the quality of service provided for the mobile end users. Different applications can benefit from the large scale deployments of ME servers such as smart grid applications, content delivery networks, content sharing, traffic management, and E-health applications. This promising paradigm comes with its own downside related to the management complexity of large scale deployments that offers hundreds of applications to millions of users. In this paper, we introduce a software defined based framework to enable efficient MCC services through the integration of different software defined system components with the MEC system.

A Fog Computing Based System for Selective Forwarding Detection in Mobile Wireless Sensor Networks

Abstract: Intrusions detection is one of the major issues that worry organizations in wireless sensor networks (WSNs). Many researchers have dealt with this problem and have proposed many methods for detecting different kinds of intrusions such as selective forwarding, which is a serious attack that may obstruct communications in WSNs. However, as the applications of mobile computing, vehicular networks, and internet of things (IoT) are spreading immensely, selective forwarding detection in Mobile Wireless Sensor Networks (MWSNs) has become a key demand. This paper introduces the problem of selective forwarding in MWSNs, and discusses how available techniques for mitigation this problem in WSNs are not applicable in handling the problem in MWSNs due to sensors mobility. Therefore, the paper proposes a model that provides a global monitoring capability for tracing moving sensors and detecting malicious ones. The model leverages the infrastructure of Fog Computing to achieve this purpose. Furthermore, the paper provides a complete algorithm, a comprehensive discussion and experiments that show the correctness and importance of the proposed approach.

Experimental comparison of simulation tools for efficient cloud and mobile cloud computing applications

Abstract: Cloud computing provides a convenient and on-demand access to virtually unlimited computing resources. Mobile cloud computing (MCC) is an emerging technology that integrates cloud computing technology with mobile devices. MCC provides access to cloud services for mobile devices. With the growing popularity of cloud computing, researchers in this area need to conduct real experiments in their studies. Setting up and running these experiments in real cloud environments are costly. However, modeling and simulation tools are suitable solutions that often provide good alternatives for emulating cloud computing environments. Several simulation tools have been developed especially for cloud computing. In this paper, we present the most powerful simulation tools in this research area. These include CloudSim, CloudAnalyst, CloudReports, CloudExp, GreenCloud, and iCanCloud. Also, we perform experiments for some of these tools to show their capabilities.

Cloud support for large scale e-healthcare systems

Abstract: Rapid development of wearable devices and mobile cloud computing technologies has led to new opportunities for large scale e-healthcare systems. In these systems, individuals’ health information are remotely detected using wearable sensors and forwarded through wireless devices to a dedicated computing system for processing and evaluation where a set of specialists namely, hospitals, healthcare agencies and physicians will take care of such health information. Real-time or semi-real time health information are used for online monitoring of patients at home. This in fact enables the doctors and specialists to provide immediate medical treatments. Large scale e-healthcare systems aim at extending the monitoring coverage from individuals to include a crowd of people who live in communities, cities, or even up to a whole country. In this paper, we propose a large scale e-healthcare monitoring system that targets a crowd of individuals in a wide geographical area. The system is efficiently integrating many emerging technologies such as mobile computing, edge computing, wearable sensors, cloud computing, big data techniques, and decision support systems. It can offer remote monitoring of patients anytime and anywhere in a timely manner. The system also features some unique functions that are of great importance for patients’ health as well as for societies, cities, and countries. These unique features are characterized by taking long-term, proactive, and intelligent decisions for expected risks that might arise by detecting abnormal health patterns shown after analyzing huge amounts of patients’ data. Furthermore, it is using a set of supportive information to enhance the decision support system outcome. A rigorous set of evaluation experiments are conducted and presented to validate the efficiency of the proposed model. The obtained results show that the proposed model is scalable by handling a large number of monitored individuals with minimal overhead. Moreover, exploiting the cloud-based system reduces both the resources consumption and the delay overhead for each individual patient.

An enhanced framework for aspect-based sentiment analysis of Hotels' reviews: Arabic reviews case study

Abstract: This research proposes a framework for aspect-based sentiment analysis (ABSA) of Hotels' reviews. The proposed framework consists of: (a) a reference human annotated Arabic dataset to support ABSA tasks such as aspect category identification, opinion target expression extraction, and opinion sentiment polarity. The dataset was annotated on both sentence-level and text-level, (b) baseline approach where a Support Vector Machine (SVM) was trained as part of the ABSA tasks, (c) baseline experiments and results, and (d) a common evaluation technique to provide a unified evaluation of future research working on the same dataset and ABSA tasks.

A GPU-based breast cancer detection system using Single Pass Fuzzy C-Means clustering algorithm

Abstract: Breast cancer is the most lethal type of cancer affecting women in the world. To improve the life quality of women, an early detection of this malignancy is always promising because this cancer is one of the cancers that can be managed and treated easily if it is detected earlier. Mammography is the standard screening method to diagnose breast cancer. At these days, there are a lot of researches that have been done to improve this screening method by benefiting from the enormous growth of technology. Graphics Processing Unit (GPU) is a parallel processor that can divide the complex computations tasks into subtasks and run them concurrently. Many medical imaging modalities offloaded their processing to this processor to help in improving the speed of healthcare systems in order to diagnose the illnesses in real time. This research introduces an acceleration method for the segmentation of the mammography images based on the GPU. In order to provide a better detection for the cancerous tumor, we use a modified version of the most common algorithm for image segmentation, which is the Single Pass Fuzzy C-Means (FCM) algorithm. The approach will be applied to a set of mammogram images to distinguish between malignant and benign cases. Additionally, the system is implemented on GPU parallel processor as well as the traditional CPU in order to compare the performance of both implementations. The performance results are compared according to the execution time and the speedup metrics. The proposed implementation on GPU provides a fine speedup compared to its serial implementation on CPU.

A supervised approach for multi-label classification of Arabic news articles

Abstract: Multi-label classification of textual data is an important problem with the growing size of available data and the increasing difficulties in assigning a single label to each piece of text. Examples range from news articles to emails. Most of the existing works consider English text. This work focuses on multi-label classification of Arabic articles. After dataset collection, three multi-label classifiers are considered (DT, RF and KNN). The results show a superiority of DT over the other two classifiers.

Evaluating SentiStrength for Arabic Sentiment Analysis

Abstract: Social networking websites are used today as platforms enabling their users to write down almost anything about everything. Social media users express their opinions and feelings about lots of events occurring in their daily lives. Lots of studies are conducted to study the sentiments presented by social media users regarding different topics. Sentiment Analysis (SA) is a new field that is concerned with measuring the sentiment presented in a given text. Due to their wide set of applications, several SA tools are available. Most of them are designed for English text. As for other languages such as Arabic, the case is different since only few tools are available. In fact, many of these tools were originally designed for English and were later adapted to deal with Arabic. SentiStrength is an example of tools that are successful for English and were later adapted to Arabic. However, the adaptation has been done in a crude manner and no deep studies are available to measure the effectiveness of such tools for Arabic text. In this paper, we perform a comprehensive evaluation of SentiStrength using 11 Arabic datasets consisting of tens of thousands of reviews/comments from different domains and in different dialects. We perform the evaluation in terms of positive and negative sentiments. The evaluation results show that overall SentiStrength achieves 62% accuracy, 83.7% precision, 64% recall (positive correct), 68% F1 measure and 55% negative correct.

Towards improving resource management in cloud systems using a multi-agent framework

Abstract: With the goal of efficient sharing of resources and services, the cloud computing paradigm has gained a lot of interest recently. This work focuses on improving the resource utilisation by optimising the resource provisioning through a multi-agent framework in which different agents are responsible for different tasks including the monitoring of customers (behaviour, resource usage patterns and QoS requirements as stated in the SLA) and available resources based on customer's requests. Moreover, we introduce the concept of TaskFlow which allows a more elastic resources provisioning to match the customer real usage of the resources. The proposed system is implemented and tested on the CloudSim simulator and the results show it increases resource utilisation and decreases power consumption while avoiding SLA violations. The results also show that the introduction of the concept of TaskFlow into our proposed system leads to more resource saving but with a higher risk of SLA violations.

Using Enhanced Lexicon-Based Approaches for the Determination of Aspect Categories and Their Polarities in Arabic Reviews

Abstract: Sentiment Analysis SA is the process of determining the sentiment of a text written in a natural language to be positive, negative or neutral. It is one of the most interesting subfields of natural language processing NLP and Web mining due to its diverse applications and the challenges associated with applying it on the massive amounts of textual data available online especially, on social networks. Most of the current work on SA focus on the English language and work on the sentence-level or the document-level. This work focuses on the less studied version of SA, which is aspect-based SA ABSA for the Arabic language. Specifically, this work considers two ABSA tasks: aspect category determination and aspect category polarity determination, and makes use of the publicly available human annotated Arabic dataset HAAD along with its baseline experiments conducted by HAAD providers. In this work, several lexicon-based approaches are presented for the two tasks at hand and show that some of the presented approaches significantly outperforms the best-known result on the given dataset. An enhancement of 9% and 46% were achieved in the tasks aspect category determination and aspect category polarity determination respectively.

Authorship attribution of Arabic tweets

Abstract: In tweet authentication, we are concerned with correctly attributing a tweet to its true author based on its textual content. The more general problem of authenticating long documents has been studied before and the most common approach relies on the intuitive idea that each author has a unique style that can be captured using stylometric features (SF). Inspired by the success of modern automatic document classification problem, some researchers followed the Bag-Of-Words (BOW) approach for authenticating long documents. In this work, we consider both approaches and their application on authenticating tweets, which represent additional challenges due to the limitation in their sizes. We focus on the Arabic language due to its importance and the scarcity of works related on it. We create different sets of features from both approaches and compare the performance of different classifiers using them. To the best of our knowledge, this is the first study of its kind to combine these different sets of features for authorship analysis of Arabic tweets. The results show that combining all the feature sets we compute yields the best results.

Energy Optimisation for Mobile Device Power Consumption: A Survey and a Unified View of Modelling for a Comprehensive Network Simulation

Abstract: The need for the analysis of energy consumption has become greater due to the constrained resources of mobile devices afforded by the increased usage of mobile devices and the environmental footprint of large-scale, distributed systems. Energy usage has previously been modelled for a variety of use cases in order to optimise its consumption, through both simulation and real-world use. As computing devices become ubiquitous, more mobile, and highly varied in their components and use; the networks which interconnect them have become highly dynamic in tandem. This is partly due to the mobility of devices and the constantly fluctuating resource requirements. Whilst simulation of energy consumption within networks has been conducted for specific use cases (e.g. Cloud and wireless networks), it is often not examined from a unified view. This paper attempts to review the state-of-the-art in network energy consumption, modelling, and simulation from the perspective of heterogeneous networks but with a focus upon mobile devices, and then propose a gap in which a unified view is needed. Such views will assist in understanding more about the complex relationships between varied, synergistic device types, such as those which compose mobile cloud networks.

Using Dynamic Parallelism to Speed Up Clustering-Based Community Detection in Social Networks

Abstract: Social Network Analysis (SNA) has been gaining a lot of attention lately. One of the common steps in SNA is community detection. SNA literature has many interesting algorithms for community detection. One of the popular ones was proposed by Newman and it is mainly revolved around using a clustering algorithm. Three phases are iteratively applied in this algorithm in order to find the "best" community structure. These phases are: spectral mapping, clustering and modularity computation. Despite its effectiveness, this method suffers greatly in terms of running time when dealing with largescale networks. A parallel implementation using GPUs is one of the feasible solutions to address this problem. Moreover, due to the iterative nature of this algorithm, dynamic parallelism lends itself as a very appealing solution. Dynamic parallelism is a novel parallel programming technique that refers to the ability to launch new grids from the GPU. In this work, we present three implementation of the clustering-based community detection algorithm. In addition to the sequential implementation, we present two implementations: a Hybrid CPU-GPU (HCG) one and a Dynamic Parallel (DP) one. We test our parallel implementations on benchmark datasets to show the speed-up of each parallel implementation compared with the sequential one. The results show that the DP implementation achieves good speed-ups reaching up to 4.45X, however, the speed-ups achieved by HCG are almost twice as much.

Integrated sensors system based on IoT and mobile cloud computing

Abstract: The advances in IT sector, cloud computing, the wide usage of sensors and mobile devices, and the Internet-of-Things (IoT) made our world looks like a small town. These rapid developments keep us connected all the day and seven days a week. Also, the IoT enables the connection of the devices around us (including different sensors) to the internet via different wireless and wired communication technologies. These networked sensors can be used to collect different types of data from different applications (healthcare, agriculture, civil and social life) and send it for processing and extraction of appropriate decisions. The mobile cloud computing technology is an efficient solution to process different types of collected data and respond with the required answer in real time situations where the quick response is very important. In this paper, we build a multipurpose integrated sensors system. This integrated system consists of networked sensors for different purposes and applications. For example, the sensors can be health sensors to measure the pulse and blood pressure of patients, or it can be sensors to measure the temperature to indicate a fire accident. The networked sensors will transfer the sensed data through wireless technologies to a Cloud for processing and notifying the listed users to take the proper action.

Using GPUs to speed-up FCM-based community detection in Social Networks

Abstract: One of the important features of Social Networks (SNs) is community structure detection. Several methods have been proposed to address this problem. One of the interesting methods is based on the famous Fuzzy C-Means (FCM) clustering algorithm. This method consists of three phases: spectral mapping, FCM clustering and modularity computation. Despite being very effective, this method is actually inefficient to deal with large-scale networks. A parallel implementation using GPUs is one of the feasible solutions to address this problem. Hence, this research presents a parallel implementation of FCM and modularity components of the algorithms. The implementation follows the hybrid CPU-GPU approach. We study the many factors affecting the performance speedups, such as the number of dimensions/features and the network size.

Mitigating insider threat in cloud relational databases

Abstract: Cloud security has become one of the emergent issues because of the immense growth of cloud services. A major concern in cloud security is the insider threat because of the harm that it poses. Ther...

GA-Based Resource Augmentation Negotation for Energy-Optimised Mobile Ad-hoc Cloud

Abstract: Mobile and cloud computing are two areas which are rapidly expanding in terms of use case and functionality. An ad-hoc mobile cloud can be constructed upon local devices, where devices work together and share resources, aspects such as energy and costs may be minimised. This paper builds on previous work which proposed an end-to-end system whereby user applications may be profiled for their resource consumption locally and then if augmentation is required, they may negotiate with a local mobile ad-hoc cloud for optimum energy and resource utilisation. The contribution of this paper focuses upon a novel and adapted Genetic Algorithm approach to resource negotiation in order to further enable energy-optimised resource augmentation for Mobile Ad-hoc clouds.

Using the Panama Papers to explore the financial networks of the Middle East

Abstract: In what has been described as the WikiLeaks of the financial world, the release of millions of documents (known as the “Panama Papers”) have placed at the center of global media attention the elaborate ways used by some of the elite to hide their financial assets leading to serious allegation of financial corruption. In this work, we explore the information contained in these documents using social network analytics. Due to the large size of the network constructed from the Panama Papers, we limit our attention to a specific region, which is the Middle East. The analysis reveals that while the constructed network enjoys some typical characteristics, there are many interesting observations and properties worth discussing. Specifically, using the extracted network consisting of 37,442 nodes and 79,544 edges, our social network analysis finding show that, perhaps surprisingly, the nodes or the social network are not necessarily directly correlation with perceived financial influence.

Parallel implementation of FCM-based volume segmentation of 3D images

Abstract: Parallel programming has many benefits that can help developers and researchers to improve the performance of some algorithms to become more efficient in real life. This is especially true for systems involving medical images. Image segmentation for volume extraction is a famous segmentation process that takes long time to finish execution. In this paper, we consider a new version of the Fuzzy C-Means (FCM) segmentation algorithm (known as IT2FPCM) and provide a parallel implementation of it that is 12X time faster than the sequential implementation. The considered algorithm is based on Interval Type-2 FCM and combines fuzzy and possibilistic ideas in order to obtain higher accuracy. We conduct our experiments using two different machines and the results show that the improvement gains for both machines 11X and 12X, respectively.

Using GPUs to speed-up Levenshtein edit distance computation

Abstract: Sequence comparison problems such as sequence alignment and approximate string matching are part of the fundamental problems in many fields such as natural language processing, data mining and bioinformatics. However, the algorithms proposed to address these problems suffer from high computational complexities prohibiting them from being widely used in practical large-scale settings. Many researchers used parallel programming to reduce the execution time of these algorithms. In this paper, we follow this approach and use the parallelism capabilities of the Graphics Processing Unit (GPU) to accelerate one of the most common algorithms to compute the edit distance between two strings, which is known as the Levenshtein distance. To take full advantage of the large number of cores in a GPU, we employ a diagonal-based tracing technique which results in even greater improvements in terms of the running time. In fact, our CUDA implementation of the Levenshtein algorithm is about 11X faster than the sequential implementation. This is achieved without affecting the accuracy.

Efficient and secure software-defined mobile cloud computing infrastructure

Abstract: This paper proposes an efficient software-based data possession mobile cloud computing framework. The proposed design combines the characteristics of two frameworks. The first one is the provable data possession design built for resource constrained mobile devices and it uses the advantage of trusted computing technology, and the second framework is a lightweight resilient storage outsourcing design for mobile cloud computing systems. The proposed solution utilises the strength aspects found in each framework in order to gain better performance and maintain adequate and comparable security. The proposed framework is a software-defined system, encompassing software-defined storage SDStore and software-defined security SDSec. The evaluation and comparison results showed that our proposed system has better flexibility and efficiency than other related frameworks, individually.

Minimum spanning tree-based multicast routing protocol for dynamic spectrum access networks: A multi-layer probabilistic approach

Abstract: In this paper, we propose a multi-layer multicast routing protocol for multi-hop mobile ad hoc cognitive radio (CR) networks. The proposed protocol employs a probabilistic approach in performing the channel assignment and path selection process based on the concept of minimum spanning tree (MST). Our protocol accounts for the unique features of CR operating environment. Simulations experiments are conducted to investigate the effectiveness of the our protocol under various network conditions in terms of network throughput and packet delivery rate. Compared with reference CR multicast protocols, simulations results show that our multi-layer multicast protocol signif cantly improves network performance.

Framework for affective news analysis of Arabic news: 2014 Gaza attacks case study

Abstract: This paper aims at fostering the domain of Arabic affective news analysis through providing: (a) a benchmark annotated Arabic dataset of news for affective news analysis, (b) an aspect-based sentiment analysis (ABSA) approach for evaluating the sentimental affect of Arabic news posts on the reader, and (c) a baseline approach with a common evaluation framework to compare future research results with the baseline ones.

Measuring the controversy level of Arabic trending topics on Twitter

Abstract: Social micro-blogging systems like Twitter are used today as a platform that enables its users to write down about different topics. One important aspect of such human interactions is the existence of debate and disagreement. The most heated debates are found on controversial topics. Detecting such topics can be very beneficial in understanding the behavior of online social networks users and the dynamics of their interactions. Such an understanding leads to better ways of handling and predicting how the "online crowds" will act. Several approaches have been proposed for detecting controversy in online communication. Some of them represent the interactions in the form of graphs and study their properties in order to determine whether the topic of interaction is controversial or not. Other approaches rely on the content of the exchanged messages. In this study, we focus on the former approach in identifying the controversy level of the trending topics on Twitter. Unlike many previous works, we do not limit ourselves to a certain domain. Moreover, we focus on social content written in Arabic about hot events occurring in the Middle East. To the best of our knowledge, ours is the first work to undertake this approach in studying controversy in general topics written in Arabic. We collect a large dataset of tweets on different trending topics from different domains. We apply several approaches for controversy detection and compare their outcomes to determine which one is the most consistent measure.

On the Use of Arabic Tweets to Predict Stock Market Changes in the Arab World

Abstract: Social media users nowadays express their opinions and feelings about many event occurring in their lives. For certain users, some of the most important events are the ones related to the financial markets. An interesting research field emerged over the past decade to study the possible relationship between the fluctuation in the financial markets and the online social media. In this research we present a comprehensive study to identify the relation between Arabic financial-related tweets and the change in stock markets using a set of the most active Arab stock indices. The results show that there is a Granger Causality relation between the volume and sentiment of Arabic tweets and the change in some of the stock markets.