Edge computing brings computing and storage resources closer to (mobile) end users and data sources, thus bypassing expensive and slow links to distant cloud computing infrastructures. Often leveraged opportunistically, these heterogeneous resources can be used to offload data and computations, enabling upcoming demanding applications such as augmented reality and autonomous driving. Research in this direction has addressed various challenges, from architectural concerns to runtime optimizations. As of today, however, we lack a widespread availability of edge computing-partly because it remains unclear which of the promised benefits of edge computing are relevant for what types of applications. This article provides a comprehensive snapshot of the current edge computing landscape, with a focus on the application perspective. We outline the characteristics of edge computing and its postulated benefits and drawbacks. To understand the functional composition of applications, we first define common application components that are relevant w.r.t. edge computing. We then present a classification of proposed use cases and analyze them according to their expected benefits from edge computing and which components they use. Furthermore, we illustrate existing products and industry solutions that have recently surfaced and outline future research challenges. INDEX TERMS Edge computing, heterogeneous networks, next generation networking mobile applications, Internet of Things, ubiquitous computing.

Figure 1: We present Let's Frets, a modular guitar learning concept with three feedback modules: (1) visual indicators, (2) finger position capturing, and (3) a combination of both modules.

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Virtual 3D object galleries on the Web nowadays often use real-time, interactive 3D graphics. However, this does usually still not hold for their preview images, sometimes referred to as thumbnails. We provide a technical analysis on the applicability of so-called 3D thumbnails within the context virtual 3D object galleries. Like a 2D thumbnail for an image, a 3D thumbnail acts as a compact preview for a real 3D model. In contrast to an image series, however, it enables a wider variety of interaction methods and rendering effects. By performing a case study, we show that such true 3D representations are, under certain circumstances, even able to outperform 2D image series in terms of bandwidth consumption. We thus present a complete pipeline for generating compact 3D thumbnails for given meshes in a fully automatic fashion

Direct policy search has been successful in learning challenging real-world robotic motor skills by learning open-loop movement primitives with high sample efficiency. These primitives can be generalized to different contexts with varying initial configurations and goals. Current state-of-the-art contextual policy search algorithms can however not adapt to changing, noisy context measurements. Yet, these are common characteristics of real-world robotic tasks. Planning a trajectory ahead based on an inaccurate context that may change during the motion often results in poor accuracy, especially with highly dynamical tasks. To adapt to updated contexts, it is sensible to learn trajectory replanning strategies. We propose a framework to learn trajectory replanning policies via contextual policy search and demonstrate that they are safe for the robot, can be learned efficiently, and outperform non-replanning policies for problems with partially observable or perturbed context. Index Terms-Learning and adaptive systems, reactive and sensor-based planning, motion control, policy search, replaning.