The need for low latency, bandwidth efficiency, and privacy has driven the deployment of distributed applications to the network edge. However, edge environments introduce concrete challenges such as limited infrastructure control, constrained connectivity due to NAT or firewalls, and the heterogeneity of devices and network conditions. This paper introduces a software-defined overlay networking (SDON) middleware that addresses these issues by simplifying the development and deployment of edge applications through centralized control and dynamic overlay management. SDON allows applications to define high-level requirements, such as node and link characteristics and the network topology. These requirements are translated into device-specific configurations and enforced across suitable edge devices. We implemented our SDON middleware as a fully functional software and evaluated it in two edge computing use cases: i) routing for video streaming across middleboxed edge devices and ii) computation offloading on heterogeneous edge devices. Our results show that deployments via SDON, with centrally enforced optimizations, improve application performance by reducing mean streaming latency by 20 % and computation times by 22 %.

3D printing has revolutionized DIY (Do-It-Yourself) IoT prototyping, enabling cost-effective, creative custom device creation. However, this freedom also presents challenges due to the interplay between components within an IoT design, which can influence the overall utility and performance of the prototype. Optimizing these designs is difficult due to limited means of estimating their efficacy. To address this, we introduce TOAD, a novel tool for profiling IoT prototypes and gauging their performance impact. TOAD uses thermal imaging and video analysis to extract and compare design performance characteristics. Unlike existing solutions that only profile overall performance, our tool assesses component interactions and overall design effects. It offers an affordable, non-intrusive method without needing device access or code instrumentation. Extensive benchmarks show TOAD accurately extracts performance data, aiding in selecting the best design for IoT applications. Additionally, it provides insights into how casing factors like thickness and material influence thermal behavior and performance. We demonstrate practical applications by optimizing offloading decisions based on thermal behavior, highlighting casing impacts on design performance. TOAD paves the way for efficient IoT prototype designs, offering a better understanding of component interactions and significantly enhancing the utility of custom IoT designs and their effectiveness.

Applications in the Internet of Things often make use of large networks of independent sensor nodes that generate streams of volatile data. A major challenge in these decentralized networks is to efficiently discover relevant data providers, which might be characterized by properties such as their data type, location, or ownership. Most existing approaches use distributed data structures, such as distributed hash tables, for the organization of sensor nodes. However, these systems lack the ability to consider contextual properties when identifying relevant data sources. SkipNet a prominent architecture for data storage and retrieval, provides a scalable overlay network composed of doubly-linked rings. While the data structure allows to locate individual nodes in logarithmic complexity, it fails to identify groups of nodes that share similar characteristics. Thus, in this paper, we propose SkABNet, an attribute-based extension for SkipNet which enhances the semantics of the node identifiers in the network. We introduce additional operators that allow SkABNet to accept complex search queries including multi-attribute selections, ranges, and wildcards to find relevant data providers in its decentralized data structure. Further, we define a search algorithm that performs searches with significantly less messages than comparable searches in SkipNet.

Real-time simulation in augmented reality environments is an important area of research. The simulation model used in this paper simulates the musculoskeletal system, which has many potential applications in physiotherapy, medical education, and rehabilitation. To enable such real-time simulations in augmented reality, high-performance computing clusters are typically required, which can be costly and infeasible in terms of high network delay. This paper proposes SimEdge, a domain-specific edge computing system that enables real-time pervasive simulation in augmented reality environments on heterogeneous providers. This is done by smart offloading decisions based on a continuously updated list of resource providers. SimEdge builds on previous work using surrogate models and leverages scheduling improvements and context-aware data and task placement. Significant improvements in frame rate and input lag are achieved, as well as a reduction in energy consumption. The experimental results show that the proposed approach leads to an 8.3-fold increase in frame rate and a 65.14% reduction in input lag, as well as a 35.07% reduction in energy consumption compared to the local baseline system. These results demonstrate the effectiveness of the approach in improving efficiency and responsiveness.

Digitization is currently infiltrating all daily processes, forcing casual computer users to become acquainted with unfamiliar tools. In order to avoid overstraining these users, simplified interfaces that are reduced to the functionality and content which are relevant to the individual user are imperative. Gaze-contingent systems thus monitor viewing behavior during natural system interactions to predict relevant interface elements. The prediction performance is highly dependent on the underlying features and algorithm, especially when the interface consist of dynamic elements such as videos. In this paper, we conduct two studies with a total of 233 subjects in which we record the viewers' gaze while watching videos. We then compare the quality of preference predictions for video elements of majority voting to the performance of machine learning. Our results indicate that (1) majority voting can predict preferences with an accuracy of up to 73% (66%) for two (four) elements, (2) machine learning improves the performance to 82% (74%), (3) prediction accuracy depends on the strength of the user's preference for an element, and (4) we can rank preferences for individual elements.

The nature of the surface has a significant effect on how wheelchair users experience locomotion. The preferred surfaces for wheeled mobility must be even, firm and smooth while generating adequate friction. The development of accessible road maps that include ground conditions is therefore of utmost importance. Our prior work has shown how such maps can be created using surface-induced vibration data collected by motion sensors embedded in smartphones and then classifying them with machine learning algorithms. To make data collection scalable, participatory crowd-sensing can be used, where users collect and transmit sensor data while traveling on wheelchairs. The complexity here is that wheelchairs widely vary in type (manual, power-assist, power), weight, number and nature of wheels, therefore the sensor data generated by different wheelchairs varies greatly. Collecting training data on each individual wheelchair type to develop classification models is not feasible. To address this problem, in this paper we explore the possibility of transferring knowledge from known wheelchairs to unknown types. We develop a transfer learning algorithm to classify 15 surfaces with minimal training data from different wheelchairs. Our experiments with 47 subjects show that surface classification knowledge, learned from sensor data generated by manual wheelchairs, can be transferred to a power wheelchair with up to 90.02% accuracy. This allows crowd-sensing to be used effectively for data collection for generating accessible route maps. We integrate our transfer learning approach into our system for accessible routing, which we developed in previous work.

End users can mutually share their computing resources in ad-hoc computing environments with code offloading. This augments the computational power of resource-constrained mobile devices and enables interactive user-facing applications that would otherwise exceed single device capabilities. However, ad-hoc computing comes along with new challenges such as heterogeneity and unreliability of devices. Resource consumers have to make task scheduling decisions without relying on a centralized scheduler to facilitate sub-second response times in environments with communication latencies that are in the order of the task execution times. In this paper, we present a decentralized low-latency task scheduling approach that minimizes job execution times in heterogeneous ad-hoc environments. We propose two decentralized task scheduling algorithms that select powerful computing resources for parallel task execution while avoiding delays that arise from congested devices. We provide an analytical model of the performance of these algorithms before conducting an extensive evaluation based on real-world applications and a realistic computing infrastructure. Our results show that decentralized scheduling can dynamically adapt to varying system load and outperform a central scheduler in both task and job execution times, which enables low-latency task offloading in ad-hoc environments.

Watching TV has become a side event rather than a deliberate pastime. Movie directors thus struggle to find new ways to sustain the attention of their audience. Interactive movies usually require the viewer to actively decide how the plot progresses, creating an experience more akin to video games than film. In this paper, we propose a system that analyses gaze data to personalize the plot of a video without the viewer’s active intervention. User preferences are inferred from their gaze allocation to different elements in a scene. The subsequent scene is then dynamically tailored towards the user’s predicted preference. In a user study (N = 175), we evaluate the effectiveness of the system with regard to user engagement. Our findings show that personalized videos have a positive effect on focused attention and involvement, whereas novelty perception is not significantly affected.

Code offloading enables resource-constrained devices to leverage idle computing power of remote resources. In addition to performance gains, offloading helps to reduce energy consumption of mobile devices, which is a key challenge in pervasive computing research and industry. In today's distributed computing systems, the decision whether to execute a task locally or remotely for minimal energy usage is non-trivial. Uncertainty about the task complexity and the result data size require a careful offloading decision. In this paper, we present Voltaire- a novel scheduler for sophisticated energy-aware code offloading decisions. Voltaire applies machine learning methods on crowd-sourced data about past executions to accurately predict the complexity and the result data size of an upcoming task. Combining these predictions with device-specific energy profiles and context knowledge allows Voltaire to estimate the energy consumption on the mobile device. Thus, Voltaire makes well-informed offloading decisions and carefully selects local or remote execution based on the expected energy consumption. We integrate Voltaire into the Tasklet distributed computing system and perform extensive experiments in a real-world testbed. Our results with three real-world applications show that Voltaire reduces the energy usage of task executions by 12.5% compared to a baseline scheduler.

The plethora of ubiquitous information devices create smart environments with new ways to interact. Environments such as pervasive classrooms or smart offices provide diverse services and connect a variety of heterogeneous and mostly mobile devices. Besides all the advantages of this seamless communication, it is a complex task to control the information flow within these environments. As an example, the information flow could control that students in a classroom are allowed to share their screen on a projector or the lecturers laptop. For that, we propose PerFlow, a middleware that allows to configure the information flow within a pervasive environment at runtime. The middleware is designed to enable technically unskilled personnel, such as a lecturer in a classroom might be, to define rules about who is able to send what to whom. Therefore, we have implemented a visual scripting tool into PerFlow. Our evaluation shows that PerFlow enforces the information flow within a pervasive environment without noticeable overhead. Further, we demonstrate the usability of the visual scripting tool in a two-stage user study.

Computationally intensive tasks of IoT applications can be offloaded to powerful devices in the edge. Code offloading reduces energy consumption and increases performance. However, applications that use face recognition, machine learning, or image rendering, rely on large amounts of data. The transfer of this data leads to latencies which contradicts the responsiveness required by many pervasive applications. As a solution, decoupling the data from the tasks allows to apply new scheduling strategies that place data on remote devices before the actual task execution. Grid computing approaches use this technique effectively, however, edge computing introduces further challenges such as device fluctuation and heterogeneity.In this paper, we propose a data management approach for edge computing environments that decouples data placement from task scheduling. We present a multi-level scheduler, which places data on resource providers in the system considering multiple context dimensions. The scheduler allocates tasks according to the current context and observes the state during runtime. If required, the system adjusts the number of data copies to optimize the trade-off between execution latencies and data management overhead. The paper has three contributions: (1) a context-aware multi-level scheduler, (2) the integration of four data placement, three task scheduling, and three runtime adaptation algorithms, (3) an evaluation in a real-world testbed.

In the past decade, cloud computing has shown its potential to provide powerful and reliable resources at the core of the network. Many applications can benefit from the wide range of cloud services. However, as applications in the Internet of Things become more common, the computing environment faces new requirements and challenges that cloud computing cannot meet. Fog and edge computing paradigms can fill this gap by moving computation from the core to the edge of the network. While multiple solutions for edge- centric networks have been proposed, there is still confusion about the terminology and classification of edge-centric architectures. In this paper, we summarize the current discussion about fog and edge computing systems. Further, we identify application areas of these systems in the Internet of Things.

In edge computing systems, computation is rather offloaded to nearby resources than to the cloud, due to latency reasons. However, the performance demand in the edge grows steadily, which makes nearby resources insufficient for many applications. Additionally, the amount of parallel tasks in the edge increases, based on trends like machine learning, Internet of Things, and artificial intelligence. This introduces a trade- off between the performance of the cloud and the communication latency of the edge. However, many edge devices have powerful co-processors in form of their graphics-processing unit (GPU), which are mostly unused. These processing units have specialized parallel architectures, which are different from standard CPUs and complex to use. In this paper, we present GPU-accelerated task execution for edge computing environments. The paper has four contributions. First, we design and implement a GPU system extension for our Tasklet system - a distributed computing system, which supports edge- and cloud-based task offloading. Second, we introduce a computational abstraction for GPUs in form of a virtual machine, which exploits parallelism while considering device heterogeneity and maintaining unobtrusiveness. Third, we offer an easy-to-use programming interface for the rather complex architecture of GPUs. Fourth, we evaluate our prototype in a real- world testbed and compare the GPU performance to standard edge resources.

Today's modern computing landscape consists of a huge amount of heterogeneous devices, including powerful, stable desktop computers as well as lightweight, unreliable mobile edge devices. This heterogeneity in terms of computation power and reliability increases the complexity for fault tolerance in distributed computing systems. When tasks are offloaded, slow resource providers easily become the bottleneck of a parallel computation. Further, unstable edge devices can leave the system spontaneously, discontinue remote tasks executions, and therefore lose the computation progress. These two effects increase the response time for remote task executions. In this paper, we introduce two mechanisms to avoid delayed or lost task executions caused by edge devices. This paper has five contributions. First, we define a failure model and identify the parameters that determine the magnitude of delays caused by faults and performance bottlenecks. Second, we present reactive and proactive task migration to handle system leaves. Third, we show how computational bottlenecks can be avoided by two-dimensional context-aware task partitioning. Fourth, we integrate these two solutions into an existing heterogeneous distributed computing system. Fifth, we run an evaluation on a real- world testbed to show the benefits of the solutions in practice. The evaluation shows, that we can improve systems with device fluctuation and heterogeneity by up to 39% and 53% respectively.

Ambient Assisted Living using mobile device sensors is an active area of research in pervasive computing. Multiple approaches have shown that wearable sensors perform very well and distinguish falls reliably from Activities of Daily Living. However, these systems are tested in a controlled environment and are optimized for a given set of sensor types, sensor positions, and subjects. In this work, we propose a self-adaptive pervasive fall detection approach that is robust to the heterogeneity of real life situations. Therefore, we combine sensor data of four publicly available datasets, covering about 100 subjects, 5 devices, and 3 sensor placements. In a comprehensive evaluation, we show that our system is not only robust regarding the different dimensions of heterogeneity, but also adapts autonomously to spontaneous changes in the sensor's position at runtime.

Mobile devices are ubiquitous but their resources are limited. However, they must be capable to run computationally intensive software, for example for image stitching, face recognition, and simulation-based artificial intelligence. As a solution, mobile devices can use nearby resources to offload computation. Distributed computing environments provide such features but ignore the nature of mobile devices, such as mobility, network, or battery changes. This leads to long delays, which reduce the quality of experience for the user. In this paper, we present Mobile Tasklets, a mobile extension of our distributed computing middleware. The design of Mobile Tasklets includes context monitoring, context-aware scheduling mechanisms, and an Android API for application integration. We identify the challenges of the integration of mobile devices into our distributed computing environment. We evaluate Mobile Tasklets in a realworld testbed with different context settings.

Scheduling in distributed computing systems is the process of allocating resources to a computational task. The complexity of this allocation process increases with the amount of criteria that are considered for the scheduling decision. Pervasive computing systems show a high degree of heterogeneity and dynamism. The constant joining and leaving of devices makes the system error-prone and less predictable. The involved devices differ in various properties that we subsume as their context. We argue, that these context dimensions can be used to implement fault-avoidant scheduling strategies. In this paper, we introduce the concept of context-aware scheduling for pervasive computing systems. The schedulers in these systems consider multiple context dimensions to avoid failing resource providers. We discuss relevant context dimensions, develop context-aware scheduling strategies and implement them into an existing distributed computing system. We show how to monitor the context dimensions and evaluate the fault-avoidant scheduling strategies in a large-scale simulation.

The modern computing landscape consists of numerous heterogeneous devices, all of which can contribute to a distributed environment as generic computation resources. In unstructured environments, resources can easily be shared and consumed at the cost of certainty. While some applications can handle such a best-effort service, many others require execution qualities, e.g., reliability or speed. We introduce Quality of Computation (QoC) as a thin layer on top of uniformly abstracted best-effort resources, which allows to tailor computation tasks to application-specific needs. The QoC layer provides execution guarantees for reliability, speed, precision, privacy, cost, and energy. We demonstrate QoC on the basis of the Tasklet system. Tasklets are fine-grained units of computation that can be issued for local or remote execution. The Tasklet system has two layers. Below is the best-effort execution layer, a virtual machine that provides raw computation. Above is the orchestration layer, which federates these virtual machines to one distributed computing environment and enforces the mechanisms that guarantee the requested QoC. We evaluated the performance of QoC in the Tasklet system in various scenarios. Results indicate that our system provides QoC guarantees at minimal performance cost.