Augmented reality

About: Augmented reality is a research topic. Over the lifetime, 36039 publications have been published within this topic receiving 479617 citations. The topic is also known as: AR.

Collaborative Augmented Reality in Education: A Review

Abstract: Globalization and innovation in technology have led to the extensive use of the latest technology in almost every sector, and education is no exception. Different technologies have been employed in various disciplines within the educational sector. Studies have shown that technology can enhance teaching and learning experiences. Augmented Reality (AR) is a new technology with vast potentials and great pedagogical value that offers new methods for education. AR enables the overlaying of computer-generated virtual information into the real environment in real time. Thus, researchers believed that the AR has provided new opportunities for designing engaging learning environments. Although the AR may improve educational outcomes, the main factor is to understand the process of designing the AR to support learning activities. Thus, various instructional strategies such as collaborative learning, were considered when designing an AR learning environment. Collaborative learning permits students to engage with other students and the educational content at the same time, resulting in a deeper understanding and higher motivation. Because educational research concerning collaborative AR is still in its infancy, this paper intends to review the literatures concerning collaborative AR, its previous usages and its potential in educational context.

Enabling fine-grained permissions for augmented reality applications with recognizers

Abstract: Augmented reality (AR) applications sense the environment, then render virtual objects on human senses. Examples include smartphone applications that annotate storefronts with reviews and XBox Kinect games that show "avatars" mimicking human movements. No current OS has special support for such applications. As a result, permissions for AR applications are necessarily coarse-grained: applications must ask for access to raw sensor feeds, such as video and audio. These raw feeds expose significant additional information beyond what applications need, including sensitive information such as the user's location, face, or surroundings. Instead of exposing raw sensor data to applications directly, we introduce a new OS abstraction: the recognizer. A recognizer takes raw sensor data as input and exposes higher-level objects, such as a skeleton or a face, to applications. We propose a fine-grained permission system where applications request permissions at the granularity of recognizer objects. We analyze 87 shipping AR applications and find that a set of four core recognizers covers almost all current apps. We also introduce privacy goggles, a visualization of sensitive data exposed to an application. Surveys of 962 people establish a clear "privacy ordering" over recognizers and demonstrate that privacy goggles are effective at communicating application capabilities. We build a prototype on Windows that exposes nine recognizers to applications, including the Kinect skeleton tracker. Our prototype incurs negligible overhead for single applications, while improving performance of concurrent applications and enabling secure offloading of heavyweight recognizer computation.

A Survey on the Computation Offloading Approaches in Mobile Edge/Cloud Computing Environment: A Stochastic-based Perspective

Abstract: Fast growth of produced data from deferent smart devices such as smart mobiles, IoT/IIoT networks, and vehicular networks running different specific applications such as Augmented Reality (AR), Virtual Reality (VR), and positioning systems, demand more and more processing and storage resources. Offloading is a promising technique to cope with the inherent limitations of such devices by which the resource-intensive code or at least a part of it will be transferred to the nearby resource-rich servers. Different approaches have been proposed to help make better decisions in respect of whether, where, when, and how much to offload and to improve the efficiency of the offloading process in the literature. On the other hand, the dynamic behavior of mobile devices running on-demand applications faces the offloading to the new challenges, which could be described as stochastic behaviors. Therefore, various stochastic offloading models have been proposed in the literature. However, to the best of the author’s knowledge, despite the existence of plenty of related offloading studies in the literature, there is not any systematic, comprehensive, and detailed survey paper focusing on stochastic-based offloading mechanisms. In this paper, we propose a survey paper concerning the stochastic-based offloading approaches in various computation environments such as Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Fog Computing (FC) in which to identify new mechanisms, a classical taxonomy is presented. The proposed taxonomy is classified into three main fields: Markov chain, Markov process, and Hidden Markov Models. Then, open issues and future unexplored or inadequately explored research challenges are discussed, and the survey is finally concluded.