A review of cloud oriented mobile learning platform and frameworks

With the continued growth of mobile devices usage, wireless communications improvement, and cloud computing evolution, many educational institutions around the world, especially universities and colleges, began to provide their students with mobile learning systems based on cloud computing. The widespread, ubiquitous, and flexible natures of mobile devices make mobile learning an attractive alternative in education, particularly when integrating it with cloud computing which is the up-to-date technology that delivers computing hardware and software as services. However, the participatory between mobile learning and cloud computing as a cloud based mobile learning (CBML) becomes one of the important methods in the learning process. Many researches have attempted to combine the unique features of CBML in a form of frameworks. These frameworks have been designed to identify, categorize, or evaluate the major components of the CBML system. This paper is an attempt to identify the important role of cloud computing technology in mobile learning, investigate the main advantages and limitations of CBML systems, and explore the previously designed CBML frameworks

On Mobile Cloud Computing in a Mobile Learning System

In the recent years, the nature of the Internet was constantly changing from a place used to read web pages to an environment that allows end-users to run software applications. Interactivity and collaboration have become the keywords of the new web content. This new environment supports the creation of a new generation of applications that are able to run on a wide range of hardware devices, like Mobile Phones or Personal Digital Assistants (PDAs) and this development gives rise to Mobile Cloud Computing. Mobile Cloud Computing at its simplest refers to an infrastructure where both the data storage and the data processing take place outside of the mobile device. Mobile cloud applications move the computing power and data storage away from mobile phones and into the cloud, bringing applications and mobile computing to not just smartphone users but a much broader range of mobile subscribers. In this work, Mobile learning system is designed based on electronic learning (e-learning) and mobility, within the context of mobile cloud computing. However, traditional m-learning applications have limitations in terms of high cost of devices and network, low network transmission rate, and limited educational resources; this cloud-based -learning application is introduced to solve these limitations. A mobile website is developed as well as a mobile application, this services which will be offered free, which will then gather relevant information in relation to the individuals’ topic of interest from a database located on a remote server and also web-links gotten from the cloud (internet) to expand the knowledge and understanding of the individual in the area of interest. Keyword: Cloud Computing, Mobile Learning System, Mobile Device

Cloud-based or On-device: An Empirical Study of Mobile Deep Inference

Modern mobile applications are benefiting significantly from the advancement in deep learning, e.g., implementing real-time image recognition and conversational system. Given a trained deep learning model, applications usually need to perform a series of matrix operations based on the input data, in order to infer possible output values. Because of computational complexity and size constraints, these trained models are often hosted in the cloud. To utilize these cloud-based models, mobile apps will have to send input data over the network. While cloud-based deep learning can provide reasonable response time for mobile apps, it restricts the use case scenarios, e.g. mobile apps need to have network access. With mobile specific deep learning optimizations, it is now possible to employ on-device inference. However, because mobile hardware, such as GPU and memory size, can be very limited when compared to its desktop counterpart, it is important to understand the feasibility of this new on-device deep learning inference architecture. In this paper, we empirically evaluate the inference performance of three Convolutional Neural Networks (CNNs) using a benchmark Android application we developed. Our measurement and analysis suggest that on-device inference can cost up to two orders of magnitude greater response time and energy when compared to cloud-based inference, and that loading model and computing probability are two performance bottlenecks for on-device deep inferences.Comment: Accepted at The IEEE International Conference on Cloud Engineering (IC2E) conference 201

Using cloud computing and mobile devices to facilitate students' learning through e-learning games

The recent advance in cloud computing and mobile devices empowers many innovative e-learning systems or games with increased interactivity and improved features. In this paper, we consider an innovative framework of cloud-based e-learning games that can be assessed through mobile devices to enhance students' learning anytime and anywhere. Being model-based, our proposal is adaptive and highly portable that can be easily customized to any existing cloud platform. Besides, our proposed framework allows course instructors or game designers to flexibly modify any part of an e-learning game, and continuously monitor the performance of individuals who try to compete with each other to attain better results. It is worth noting that this paper reports an on-going work, namely the iGame@Cloud system, for which a thorough evaluation will be conducted later. After all, our proposal stimulates many interesting directions for further exploration. © 2013 IEEE.published_or_final_versio

Using Agent Solutions and Visualization Techniques to Manage Cloud-based Education System

Over the past few years, there are many requests from academic institutions, eLearning developers, education businesses owners, and global enterprises concerning cloud-based education systems. Nowadays, a range of software and applications have been created for managing teaching and learning resources via internet. Many of them have been even trying to integrate all the educational resources into a single cloud system. This paper proposes using agent technologies and visualization solutions to manage cloud-based education systems to match streamline of day to day business and operations. It focuses on adopting agents for University of Westminster’s Cloud computing education system and mobile learning project. It shows how intelligent agents can be used as a good tool for cloud-based education service and associated applications provision and management within Software as Service (SaaS) level

Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps

Due to the popularity of smart phones and mobile apps, a potential privacy risk with the usage of mobile apps is that, from the usage information of mobile apps (e.g., how many hours a user plays mobile games in each day), private information about a user’s living habits and personal activities can be inferred. To assess this risk, this thesis answers the following research question: can the type of a mobile app (e.g., email, web browsing, mobile game, music streaming, etc.) used by a user be inferred from the resource (e.g., CPU, memory, network, etc.) usage patterns of the mobile app? This thesis answers this question for two kinds of systems, a single mobile device and a mobile cloud computing system. First, two privacy attacks under the same framework are proposed based on supervised learning algorithms. Then these attacks are implemented and explored in a mobile device and in a cloud computing environment. Experimental evaluations show that the type of app can be inferred with high probability. In particular, the attacks achieve up to 100% accuracy on a mobile device, and 66.7% accuracy in the mobile cloud computing environment. This study shows that resource usage patterns of mobile apps can be used to infer the type of apps being used, and thus can cause privacy leakage if not protected

Efficient Task Offloading Algorithm for Digital Twin in Edge/Cloud Computing Environment

In the era of Internet of Things (IoT), Digital Twin (DT) is envisioned to empower various areas as a bridge between physical objects and the digital world. Through virtualization and simulation techniques, multiple functions can be achieved by leveraging computing resources. In this process, Mobile Cloud Computing (MCC) and Mobile Edge Computing (MEC) have become two of the key factors to achieve real-time feedback. However, current works only considered edge servers or cloud servers in the DT system models. Besides, The models ignore the DT with not only one data resource. In this paper, we propose a new DT system model considering a heterogeneous MEC/MCC environment. Each DT in the model is maintained in one of the servers via multiple data collection devices. The offloading decision-making problem is also considered and a new offloading scheme is proposed based on Distributed Deep Learning (DDL). Simulation results demonstrate that our proposed algorithm can effectively and efficiently decrease the system's average latency and energy consumption. Significant improvement is achieved compared with the baselines under the dynamic environment of DTs

Developing service-oriented application for the educational cloud

In this paper we present an application which is used for cloud computing infrastructure management. The application is based on web services and it is integrated with an existing e-learning system. Main users of the applications are teachers and students and the application consists of two main parts which are web application and mobile application. The application is successfully implemented at the e-Business Laboratory of the Faculty of Organizational Sciences in Belgrade