Mobile edge computing (MEC) is an emergent architecture where cloud computing services are extended to the edge of networks leveraging mobile base stations. As a promising edge technology, it can be applied to mobile, wireless, and wireline scenarios, using software and hardware platforms, located at the network edge in the vicinity of end-users. MEC provides seamless integration of multiple application service providers and vendors toward mobile subscribers, enterprises, and other vertical segments. It is an important component in the 5G architecture which supports variety of innovative applications and services where ultralow latency is required. This paper is aimed to present a comprehensive survey of relevant research and technological developments in the area of MEC. It provides the definition of MEC, its advantages, architectures, and application areas; where we in particular highlight related research and future directions. Finally, security and privacy issues and related existing solutions are also discussed.

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Mobile Edge Computing: A Survey

Although augmented reality (AR) has gained much research attention in recent years, the term AR was given different meanings by varying researchers. In this article, we first provide an overview of definitions, taxonomies, and technologies of AR. We argue that viewing AR as a concept rather than a type of technology would be more productive for educators, researchers, and designers. Then we identify certain features and affordances of AR systems and applications. Yet, these compelling features may not be unique to AR applications and can be found in other technological systems or learning environments (e.g., ubiquitous and mobile learning environments). The instructional approach adopted by an AR system and the alignment among technology design, instructional approach, and learning experiences may be more important. Thus, we classify three categories of instructional approaches that emphasize the ''roles,'' ''tasks,'' and ''locations,'' and discuss what and how different categories of AR approaches may help students learn. While AR offers new learning opportunities, it also creates new challenges for educators. We outline technological, pedagogical, learning issues related to the implementation of AR in education. For example, students in AR environments may be cognitively overloaded by the large amount of information they encounter, the multiple technological devices they are required to use, and the complex tasks they have to complete. This article provides possible solutions for some of the challenges and suggests topics and issues for future research.

Current status, opportunities and challenges of augmented reality in education

This survey summarizes almost 50 years of research and development in the field of Augmented Reality AR. From early research in the1960's until widespread availability by the 2010's there has been steady progress towards the goal of being able to seamlessly combine real and virtual worlds. We provide an overview of the common definitions of AR, and show how AR fits into taxonomies of other related technologies. A history of important milestones in Augmented Reality is followed by sections on the key enabling technologies of tracking, display and input devices. We also review design guidelines and provide some examples of successful AR applications. Finally, we conclude with a summary of directions for future work and a review of some of the areas that are currently being researched.

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A Survey of Augmented Reality

We address the highly challenging problem of real-time 3D hand tracking based on a monocular RGB-only sequence. Our tracking method combines a convolutional neural network with a kinematic 3D hand model, such that it generalizes well to unseen data, is robust to occlusions and varying camera viewpoints, and leads to anatomically plausible as well as temporally smooth hand motions. For training our CNN we propose a novel approach for the synthetic generation of training data that is based on a geometrically consistent image-to-image translation network. To be more specific, we use a neural network that translates synthetic images to "real" images, such that the so-generated images follow the same statistical distribution as real-world hand images. For training this translation network we combine an adversarial loss and a cycle-consistency loss with a geometric consistency loss in order to preserve geometric properties (such as hand pose) during translation. We demonstrate that our hand tracking system outperforms the current state-of-the-art on challenging RGB-only footage.

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GANerated Hands for Real-Time 3D Hand Tracking from Monocular RGB

SLAM is an abbreviation for simultaneous localization and mapping, which is a technique for estimating sensor motion and reconstructing structure in an unknown environment. Especially, Simultaneous Localization and Mapping (SLAM) using cameras is referred to as visual SLAM (vSLAM) because it is based on visual information only. vSLAM can be used as a fundamental technology for various types of applications and has been discussed in the field of computer vision, augmented reality, and robotics in the literature. This paper aims to categorize and summarize recent vSLAM algorithms proposed in different research communities from both technical and historical points of views. Especially, we focus on vSLAM algorithms proposed mainly from 2010 to 2016 because major advance occurred in that period. The technical categories are summarized as follows: feature-based, direct, and RGB-D camera-based approaches.

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Visual SLAM algorithms: a survey from 2010 to 2016

Recently there has been an increase in research of hand gestures for interaction in the area of Augmented Reality (AR). However this research has focused on developer designed gestures, and little is known about user preference and behavior for gestures in AR. In this paper, we present the results of a guessability study focused on hand gestures in AR. A total of 800 gestures have been elicited for 40 selected tasks from 20 partic-ipants. Using the agreement found among gestures, a user-defined gesture set was created to guide design-ers to achieve consistent user-centered gestures in AR.

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User-defined gestures for augmented reality

Recently there has been an increase in research towards using hand gestures for interaction in the field of Augmented Reality (AR). These works have primarily focused on researcher designed gestures, while little is known about user preference and behavior for gestures in AR. In this paper, we present our guessability study for hand gestures in AR in which 800 gestures were elicited for 40 selected tasks from 20 participants. Using the agreement found among gestures, a user-defined gesture set was created to guide designers to achieve consistent user-centered gestures in AR. Wobbrock’s surface taxonomy has been extended to cover dimensionalities in AR and with it, characteristics of collected gestures have been derived. Common motifs which arose from the empirical findings were applied to obtain a better understanding of users’ thought and behavior. This work aims to lead to consistent user-centered designed gestures in AR.

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User-Defined Gestures for Augmented Reality

In order for natural interaction in Augmented Reality (AR) to become widely adopted, the techniques used need to be shown to support precise interaction, and the gestures used proven to be easy to understand and perform. Recent research has explored free-hand gesture interaction with AR interfaces, but there have been few formal evaluations conducted with such systems. In this paper we introduce and evaluate two natural interaction techniques: the free-hand gesture based Grasp-Shell, which provides direct physical manipulation of virtual content; and the multi-modal Gesture-Speech, which combines speech and gesture for indirect natural interaction. These techniques support object selection, 6 degree of freedom movement, uniform scaling, as well as physics-based interaction such as pushing and flinging. We conducted a study evaluating and comparing Grasp-Shell and Gesture-Speech for fundamental manipulation tasks. The results show that Grasp-Shell outperforms Gesture-Speech in both efficiency and user preference for translation and rotation tasks, while Gesture-Speech is better for uniform scaling. They could be good complementary interaction methods in a physics-enabled AR environment, as this combination potentially provides both control and interactivity in one interface. We conclude by discussing implications and future directions of this research.

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Grasp-Shell vs gesture-speech: A comparison of direct and indirect natural interaction techniques in augmented reality

Creating entertaining and educational books not only requires providing visually stimulating content but also means for students to interact, create, and express themselves. In this paper we present a new type of mixed-reality book experience, which augments an educational coloring book with user-generated three dimensional content. We explore a “pop-up book” metaphor and describe a process by which children's drawing and coloring is used as input to generate and change the appearance of the book content. Our system is based on natural feature tracking and image processing techniques that can be easily exploited for other AR publishing applications.

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Adrian Clark

Papers

A Survey of Augmented Reality

User-defined gestures for augmented reality

User-Defined Gestures for Augmented Reality

Grasp-Shell vs gesture-speech: A comparison of direct and indirect natural interaction techniques in augmented reality

An interactive augmented reality coloring book