Industrial cyber-physical systems require dependable network communication with formal end-to-end reliability guarantees. Striving towards this goal, recent efforts aim to advance the integration of 5 G into Time-Sensitive Networking (TSN). However, we show that IEEE 802.1Qbv TSN schedulers that are unattuned to 5 G packet delay variations may jeopardize any reliability guarantees provided by the 5 G system. We demonstrate this on a case where a 99.99 % reliability in the inner 5G network diminishes to below 10 % when looking at end-to-end communication in TSN. In this paper, we overcome this shortcoming by introducing Full Interleaving Packet Scheduling (FIPS) as a wireless-friendly IEEE 802.1Qbv scheduler. To the best of our knowledge, FIPS is the first to provide formal end-to-end QoS guarantees in wireless TSN. FIPS allows a controlled batching of TSN streams, which improves schedulability in terms of the number of wireless TSN streams by a factor of up to$\times 45$. Even in failure cases, FIPS isolates the otherwise cascading QoS violations to the affected streams and protects all other streams. With formal end-to-end reliability, improved schedulability, and fault isolation, FIPS makes a substantial advance towards dependability in wireless TSN.

Context-awareness is becoming more relevant for smarter modern-day applications. With billions of IoT devices able to monitor a plethora of parameters in near real-time, inferring contextual information at scale while maintaining adequate Quality of Context and delivering in time has been non-trivial for state-of-the-art centralized Context Management Systems. Further, handling complex situations and entity relations based on local awareness are areas that still need investigation. In this paper, we propose a novel edge-computing based architecture for distributed contextual intelligence systems that could address these research problems. First, we critically evaluate the current state-of-the-art in context-awareness and establish the necessity of a distributed architecture. Then, our proposed architecture is introduced along with the protocols and algorithms accompanied by real-world examples. The paper also highlights the future direction for research work in the area.

Brainwaves have demonstrated to be unique enough across individuals to be useful as biometrics. They also provide promising advantages over traditional means of authentication, such as resistance to external observability, revocability, and intrinsic liveness detection. However, most of the research so far has been conducted with expensive, bulky, medical-grade helmets, which offer limited applicability for everyday usage. With the aim to bring brainwave authentication and its benefits closer to real world deployment, we investigate brain biometrics with consumer devices. We conduct a comprehensive measurement experiment and user study that compare five authentication tasks on a user sample up to 10 times larger than those from previous studies, introducing three novel techniques based on cognitive semantic processing. Furthermore, we apply our analysis on high-quality open brainwave data obtained with a medical-grade headset, to assess the differences. We investigate both the performance, security, and usability of the different options and use this evidence to elicit design and research recommendations. Our results show that it is possible to achieve Equal Error Rates as low as 7.2% (a reduction between 68–72% with respect to existing approaches) based on brain responses to images with current inexpensive technology. We show that the common practice of testing authentication systems only with known attacker data is unrealistic and may lead to overly optimistic evaluations. With regard to adoption, users call for simpler devices, faster authentication, and better privacy.

Multimodal systems offer their functionalities through multiple communication channels. A messenger application may take either keyboard or voice input, and present incoming messages as text or audio output. This allows the users to communicate with their devices using the modality that best suits their context and personal preference. Authentication is often the first interaction with an application. The users’ login behavior can thus be used to immediately adapt the communication channel to their preferences. Yet given the sensitive nature of authentication, this interaction may not be representative for the user’s inclination to use speech input in non-critical routine tasks. In this paper, we test whether the interactions during authentication differ from non-critical routine tasks in a smart home application. Our findings indicate that, even in such a private space, the authentication behavior does not correlate with the use, nor with the perceived usability of speech input during non-critical task. We further find that short interactions with the system are not indicative of the user’s attitude towards audio output, independent of whether authentication or non-critical tasks are performed. Since security concerns are minmized in the secure environment of private spaces, our findings can be generalized to other contexts where security threats are even more apparent.

In the recent past, platooning evolved into an attractive cooperative driving technology, broadly discussed in research and practice. Vehicles in platoons use cooperative adaptive cruise control to drive at close distances to each other. Platooning (i) increases the capacity of the street by a factor of 2; (ii) reduces the fuel consumption and emissions by up to 20%; and (iii) has social implications as it increases driver comfort and safety. As platooning research progresses, platooning coordination becomes a major research focus. The coordination of platoons, including the assignment of vehicles to platoons, the management of inter- and intra-platoon interactions, and the coordination of interactions with other vehicles is an important step towards an effective usage of platooning in practice. Based on a literature review of 1,600 papers, this survey provides an overview of state of the art in platooning coordination research for both cars and trucks. In this paper, we present a novel taxonomy for platooning coordination and classify existing approaches. We use the results of the literature review to discuss challenges and outline avenues for future work such as multi-objectiveness and individualisation.

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.

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 technical maturity of autonomous driving enables the discussion of beneficial use cases to leverage its full potential. In this paper, we target one such use case: Platooning is the efficient convoying of vehicles by making use of self-driving capabilities and inter-vehicle communication. Many advantages arise from grouping vehicles in platoons with a small inter-vehicle distance, such as energy savings, congestion reduction, and safety improvements. However, due to the diversity of involved stakeholders, numerous objectives have to be balanced to leverage the full potential of platooning. Furthermore, these objectives also depend on various factors that influence their optimization. The vast majority of existent literature only targets a subset of related objectives and underlying factors. This paper provides an overview which categorizes objectives and influencing factors. Additionally, metrics for the evaluation of objective attainment are proposed.

Trends such as the Internet of Things lead to a growing number of networked devices and to a variety of communication systems. Adding self-adaptive capabilities to these communication systems is one approach to reducing administrative effort and coping with changing execution contexts. Existing frameworks can help reducing development effort but are neither tailored toward the use in communication systems nor easily usable without knowledge in self-adaptive systems development. Accordingly, in previous work, we proposed REACT, a reusable, model-based runtime environment to complement communication systems with adaptive behavior. REACT addresses heterogeneity and distribution aspects of such systems and reduces development effort. In this article, we propose REACT-ION—an extension of REACT for situation awareness. REACT-ION offers a context management module that is able to acquire, store, disseminate, and reason on context data. The context management module is the basis for (i) proactive adaptation with REACT-ION and (ii) self-improvement of the underlying feedback loop. REACT-ION can be used to optimize adaptation decisions at runtime based on the current situation. Therefore, it can cope with uncertainty and situations that were not foreseeable at design time. We show and evaluate in two case studies how REACT-ION’s situation awareness enables proactive adaptation and self-improvement.

In the quest to devise new alternatives to password-based authentication, behavioral biometrics have become more and more appealing due to the improved usability that comes with their unobtrusiveness. One such type of biometric are brainwaves, which can be nowadays easily measured and used to prove a person's identity. Given the potential for this technology to be adopted in the near future, it is paramount to analyze its security implications. Furthermore, recent advances in brain computer interfaces make feasible the usage of brainwaves to prove users' identity. This work presents a comprehensive framework for assessing the vulnerabilities of brainwave authentication systems, incorporating new attack vectors that target specific features of brain biometrics. Resting on this theoretical groundwork, we analyze the existing literature on attacks and countermeasures, identifying gaps and providing a foundation for future research. Furthermore, we evaluated a subset of attacks identified through the framework and report our preliminary results.

Passwords are an often unavoidable authentication mechanism, despite the availability of additional alternative means. In the case of smartphones, usability problems are aggravated because interaction happens through small screens and multilayer keyboards. While password managers (PMs) can improve this situation and contribute to hardening security, their adoption is far from widespread. To understand the underlying reasons, we conducted the first empirical usability study of mobile PMs, covering both quantitative and qualitative evaluations. Our findings show that popular PMs are barely acceptable according to the standard System Usability Scale, and that there are three key areas for improvement: integration with external applications, security, and user guidance and interaction. We build on the collected evidence to suggest recommendations that can fill this gap.

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.

Countless novel approaches to communication protocols, overlay networks, and distributed middleware are published every year, yet the adoption of such novel findings in the global Internet landscape progresses at a slow pace. Many of such new communication mechanisms excel (only) under specific deployment conditions, while user mobility and application usage patterns lead to dynamic operation conditions. This mismatch is one reason that makes a wide deployment of new specialized mechanisms particularly hard as observed, for example, for multipath transport protocol extensions until the emergence of multipath transmission control protocol (TCP). This paper formalizes the concept of Transitions, i.e., a method to instrumentalize adaptivity at runtime in communication systems. It allows to exchange communication mechanisms in a running system to optimize the communication quality. In the following, we describe the building blocks required to: 1) capture the features and relations within a communication system and 2) express and optimize the decision making process in such a system. We show how this concept maps intuitively to the Internet model which makes a protocol-independent deployment of applications feasible in the future Internet.

While user representations are extensively used on public displays, it remains unclear how well users can recognize their own representation among those of surrounding users. We study the most widely used representations: abstract objects, skeletons, silhouettes and mirrors. In a prestudy (N=12), we identify five strategies that users follow to recognize themselves on public displays. In a second study (N=19), we quantify the users' recognition time and accuracy with respect to each representation type. Our findings suggest that there is a significant effect of (1) the representation type, (2) the strategies performed by users, and (3) the combination of both on recognition time and accuracy. We discuss the suitability of each representation for different settings and provide specific recommendations as to how user representations should be applied in multi-user scenarios. These recommendations guide practitioners and researchers in selecting the representation that optimizes the most for the deployment's requirements, and for the user strategies that are feasible in that environment.

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.

Today's adaptive software systems (i) are often highly configurable product lines, exhibiting hundreds of potentially conflicting configuration options; (ii) are context dependent, forcing the system to reconfigure to ever-changing contextual situations at runtime; (iii) need to fulfill context-dependent performance goals by optimizing measurable nonfunctional properties. Usually, a large number of consistent configurations exists for a given context, and each consistent configuration may perform differently with regard to the current context and performance goal(s). Therefore, it is crucial to consider nonfunctional properties for identifying an appropriate configuration. Existing black-box approaches for estimating the performance of configurations provide no means for determining context-sensitive reconfiguration decisions at runtime that are both consistent and optimal, and hardly allow for combining multiple context-dependent quality goals. In this paper, we propose a comprehensive approach based on Dynamic Software Product Lines (DSPL) for obtaining consistent and optimal reconfiguration decisions. We use training data obtained from simulations to learn performance-influence models. A novel integrated runtime representation captures both consistency properties and the learned performance-influence models. Our solution provides the flexibility to define multiple context-dependent performance goals. We have implemented our approach as a standalone component. Based on an Internet-of-Things case study using adaptive wireless sensor networks, we evaluate our approach with regard to effectiveness, efficiency, and applicability.

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.

In today's world, smart devices - such as laptops, smartphones, or smart watches - may substitute the need for physical presence of people. By offering a virtual representation, these devices support social interactions, e.g., telecommuting, remote studying through e-learning, or long-distance relationships. With audio and video streaming platforms like Skype, Apple FaceTime, or Google Hangouts it is possible to stay in contact with friends and colleagues. However, these platforms only allow communication between two peers or at most in very small groups. Additionally, they are very inflexible regarding content sharing and support only limited, defined use cases. In this paper, we present an extension to our pervasive display service for a remote virtual environment. This allows an immersive participation at remote activities in larger groups, while retaining the flexibility regarding joining devices of a pervasive middleware. Our contributions are threefold. First, we present a reusable design for integrating an interactive display service into virtual environments. Second, we implement our approach using the BASE middleware and the Unity 3D engine. Third, we evaluate our approach in a smart meeting room scenario.

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.

Applications in intelligent environments are context-aware and context-altering in order to support users in their everyday tasks. In multi-user environments with shared context, interferences are likely to occur. An interference is an application-induced context that forces other applications to react. In our COMITY project, we developed interference detection and resolution algorithms. However, especially the constraint satisfaction-based resolution algorithm is computationally expensive. It requires a full-fledged machine in order to achieve runtimes suitable for interactive systems. In spontaneously formed smart peer groups, such infrastructure is not given. In this paper, we present an approach for detecting and resolving interferences in smart peer groups. The approach is inspired by two coordination mechanisms from nature. We map the problems of interference detection and resolution to the rules of the flocking mechanism, show how to implement a flocking-based interference management, and present a local inhibition-based leader election for smart peer groups in order to elect the coordinating entity. Finally, we show the feasibility of our approach by evaluating our prototype.

Pervasive applications are designed to support users in their daily lives. In order to provide their services, these applications interact with the environment, i.e. their context. They either adapt themselves as a reaction to context changes, or adapt the context via actuators according to their needs. If multiple applications are executed in the same context, interferences are likely to occur. In this paper, we present COMITY-a framework for interference management in multi-platform pervasive systems. Based on contracts specifying an application's interaction with the context, the framework automatically detects interferences and resolves them through a coordinated application adaptation. We analyze the problem of interference resolution, discuss respective algorithms and extensively evaluate our prototype.

A key challenge for Peer-to-Peer-based Massively Multi-user Virtual Environments is efficient delivery of updates. In contrast to centralized approaches, which can efficiently calculate changes and deliver these, peer-based approaches have to generate updates from arbitrary participating peers. In this paper we present an approach to reduce update messages in Peer-to-Peer-based Massively Multi-user Virtual Environments introducing a distinct class of update events, which we call continuous events. A continuous event results in a series of updates that can be pre-computed. By distributing the event series as a change function, affected peers can calculate and perform updates after receiving a continuous event without further update messages. We present a system design enabling the distribution and computation of continuous events and discuss basic algorithms for continuous event management. We also show simulation results which confirm a substantial reduction of update messages.

Distributed virtual worlds encompass a number of different possible user interactions. These are realized as interaction events that need to be reflected in all user's views in a timely and synchronous fashion. This is difficult because of two aspects: A) user requirements vary for different events and due to B) restricted system resources, mainly network delay. In this paper we present a framework that allows the processing of events using different synchronization protocols depending on the user requirements. We integrate existing protocols into the framework allowing different handling of events.

A major characteristic of pervasive computing applications is their ability to adapt themselves to changing execution environments and physical contexts. In this article, we analyze different kinds of adaptations and introduce a multidimensional classification for them. On this basis, we propose a novel approach for peer-to-peer-based pervasive computing that provides support for the identified classes and integrates them in a multilevel architecture. We give a comprehensive overview of this architecture and its current realization in the Peer-to-Peer Pervasive Computing (3PC) project, discussing what adaptation is realized on each level, how the levels interact with each other, and how the overall system benefits from the integrated treatment of adaptation.

Discovering semantic knowledge is significant for understanding and interpreting how people interact in a meeting discussion. In this paper, we propose a mining method to extract frequent patterns of human interaction based on the captured content of face-to-face meetings. Human interactions, such as proposing an idea, giving comments, and expressing a positive opinion, indicate user intention toward a topic or role in a discussion. Human interaction flow in a discussion session is represented as a tree. Tree-based interaction mining algorithms are designed to analyze the structures of the trees and to extract interaction flow patterns. The experimental results show that we can successfully extract several interesting patterns that are useful for the interpretation of human behavior in meeting discussions, such as determining frequent interactions, typical interaction flows, and relationships between different types of interactions.

This report summarizes the International Workshop on Situation, Activity and Goal Awareness held at the 13th ACM International Conference on Ubiquitous Computing, on September 18 in Beijing, China.

Peer-to-Peer-based Massively Multi-user Virtual Environments (P2P-MMVEs) often use coordinator peers to enhance the system's capabilities. The success of this approach depends on selecting suitable peers as coordinators. However, existing approaches either do not select coordinators based on their suitability or simply assume that techniques for such a selection exist. In this paper we argue that these techniques are not currently available for P2P- MMVEs. We believe that developing such techniques is one of the few remaining challenges for the real- world feasibility of P2P-MMVEs. As a first step towards this goal, we identify and discuss the challenges associated with this research in detail.

Many peer-to-peer (P2P) systems require accurate information about their peer's available bandwidth, e.g., for load balancing. Determining this information is difficult, as a suitable approach must address two challenges. First, it must be able to deal with fluctuating bandwidth. Second, it must incur low overhead to prevent interference with the operation of the P2P system. In this paper we present an approach to estimate the available bandwidth of peers in a P2P system, based on a combination of traffic observation and the strategic injection of traffic into the system. We evaluate our approach and show that it is accurate and responsive in settings with variable bandwidth while resulting in limited interference with the system.

Pervasive applications have been designed to support users in their everyday life. For this purpose, they are able to interact with their physical environment, their context. They are aware of their context and use this information for configuration decisions. Furthermore, they can actively modify the context to meet their user's needs. This leads to new challenges in multi-user environments as applications which are executed simultaneously share a common context and thus can directly impact each other. In this paper we present a framework to coordinate multiple pervasive applications explicitly considering their context-interactivity. We show how application coordination can be integrated in an existing component-based system exemplified by our system PCOM. We conduct measurements for the extended system and discuss the obtained results.

Pervasive computing systems are highly complex distributed systems. Due to their intrinsic heterogeneity and dynamism, applications must reconfigure themselves continuously to adapt to changes in their execution context. In addition, applications influence their context themselves, e.g., by outputting audio. This may lead to conflicts, when applications interfere with each other or disturb users in the vicinity. To handle such conflicts, we propose a dynamic conflict management system that detects and resolves conflicts at runtime. In this paper we focus on how the system can be used by end-users in order to tailor a pervasive system to the userpsilas preferences. By defining individual conflicts in a conflict specification our system ensures that system states which are unacceptable for the user are automatically detected and resolved. We present our approach, discuss our proposed conflict notation and evaluate the users' acceptability of our notation with a short user study.

Massively multi-user virtual environments (MMVEs) are highly interactive systems. They require the propagation of state updates to users with little delay. In this paper we propose a novel update propagation approach for MMVEs that enables such low-latency propagation while offering the scalability needed to support MMVEs with massive user numbers. Our approach combines peer-based and server-based update propagation into a hybrid system. It adapts itself dynamically to the available system resources and the current situation in the virtual world. We describe our approach in detail, evaluate it and discuss further steps towards a low-latency update propagation system.

Pervasive Computing dates back to Mark Weiser's vision of Ubiquitous Computing. Nearly two decades ago he envisioned a world populated with computers that invisibly weave into our daily environment. Extensive research has been done in the past and a number of developments have taken place, though most of them are confined to research laboratories.

Energy efficiency in pervasive computing is crucial for devices operated by battery. To provide energy efficiency we created an energy efficient middleware, called SANDMAN. It selects energy-efficient protocol stacks dynamically and switches idle devices in an energy-efficient sleep mode. In this paper we present an overview of the challenges we met when realizing this approach, possible solutions and lessons learned.

Massively multiplayer online games have become increasingly popular. However, their operation is costly, as game servers must be maintained. To reduce these costs, we aim at providing a communication engine to develop massively multiplayer online games based on a peer-to-peer system. In this paper we analyze the requirements of such a system and present an overview of our current work.

Human behavior and housing resist every standardization effort. Many aspects such as different technical equipment, furniture, and usage patterns make our surroundings as individual as ourselves. Thus, the personalization of pervasive applications is a fundamental requirement. To enable the development of custom pervasive applications, we propose a software development process. This process is based on the successful process for modern desktop applications. There, developers create extensible applications and components. Customizers use the resulting artifacts to develop custom applications. Finally, users configure applications to their individual needs by adjusting predefined settings. To adopt this process for pervasive computing, we present a component system for developers, a graphical toolkit for customizers, and self-configuration algorithms to ease the deployment

Geocast is used to send messages to all hosts located in a geographic area. This target area can be defined either by geometric figures like polygons or by symbolic addresses like city names or room numbers. Geographic routing algorithms, which forward messages based on geographic information, can be used to forward geocast messages. If routing of symbolically addressed messages is based on geometric coordinates, complex mappings between symbolic addresses and their geometric extent as well as complex geometric operations are required. Therefore, we propose a routing algorithm for symbolically addressed geocast messages that operates directly on a symbolic location model. This approach does not require any geometric information for message forwarding, and forwarding decisions can be realized efficiently by comparably simple operations.

MANETs are used in situations where networks need to be deployed immediately but no network infrastructure is available. If MANET nodes have sensing capabilities, they can capture and communicate the state of their surroundings, including environmental conditions or objects in their proximity. If the sensed state information is propagated to a database to build a consistent model of the real world, a variety of promising context-aware applications becomes possible. We introduce a novel consistency concept that preserves the chronological ordering of sensed state transition events. Based on this concept, we propose a data replication algorithm for MANETs that guarantees the consistency concept without relying on synchronized clocks and show its correctness. Our simulation experiments show that replicated copies are updated regularly even if the network load in the system is high.

Applications in the pervasive computing domain are challenged by the dynamism in which their execution environment changes, e.g. due to user mobility. As a result, applications have to adapt to changes regarding their required resources. In this paper we present PCOM, a component system for pervasive computing. PCOM offers application programmers a high-level programming abstraction which captures the dependencies between components using contracts. The resulting application architecture is a tree formed by components and their dependencies. PCOM supports automatic adaptation in cases where the execution environment changes to the better or to the worse. User supplied as well as system provided strategies take users out of the control loop while offering flexible adaptation control.

In the vision of pervasive computing smart everyday objects communicate and cooperate to provide services and information to users. Interoperability between devices and applications not only requires common protocols but also common context management. In this paper we discuss requirements on the context management based on the Georgia Tech's Aware home environment and the global context management perspective of the Nexus project. Our experiences with integrating the aware home spatial service into the Nexus platform show how federation concepts and a common context model can provide applications with uniform context information in different administrative and application domains.

Distribution of data in mobile ad hoc networks is challenged when the mobility of nodes leads to frequent topology changes. Existing approaches so far address either the network partitioning problem or are capable of handling large amounts of data, but not both at the same time. In this paper, a novel approach is presented which is based on a negotiation scheme enhanced by an adaptive repetition strategy. Different strategies for the selection of repeated data are presented and evaluated. Simulation results show a reduction of data transfer volume compared to hyper-flooding by 30% to 40% even in the presence of frequent network partitions.

Pervasive computing environments add a multitude of additional devices to our current computing landscapes. Specialized embedded systems provide sensor information about the real world or offer a distinct functionality, e.g. presentation on a "smart wall". Spontaneous networking leads to constantly changing availability of services. This requires middleware support to ease application development. Additionally, we argue that an extensible middleware platform covering small embedded systems to fill-fledged desktop computers is needed. Such a middleware should provide easy-to-use abstractions to access remote services and device-specific capabilities. We present a micro-broker-based approach which meets these requirements by allowing uniform access to device capabilities and services through proxies and the integration of different interoperability protocols. A minimum configuration of the middleware can be executed on embedded systems. Resource-rich execution environments are supported by the extensibility of the middleware.

Choosing appropriate information dissemination strategies is crucial in mobile ad hoc networks (MANET) due to the frequent topology changes. Flooding-based approaches like diffusion have a strong similarity with epidemic spreading of diseases. Applying epidemiological models to information diffusion allows the evaluation of such strategies depending on the MANET characteristics, e.g. the node density. In order to choose appropriate strategies at run time, the model should be easily evaluated.In this paper, an epidemic model is developed for a simple information diffusion algorithm based on simulation results. We analytically investigate the impact of node density on information diffusion. The analytical model allows the evaluation at runtime, even on devices with restricted resources, and thus enables mobile nodes to dynamically adapt their diffusion strategies depending on the local node density.

Quality of service (QoS) management is a popular demand on distribution infrastructures. We present MAQS, a generic framework for QoS management based on standard CORBA middleware. The framework facilitates the specification and generation of QoS-enabled client/server applications. Architectural support for re-use and maintenance of implementations of QoS mechanisms is one of our main objectives. Our approach is based on the aspect-oriented programming paradigm. We discuss the design of the framework and how AOP facilitates the re-use and maintenance of QoS components.

Quality of service (QoS) management is an increasing demand on distribution infrastructures. While most of the existing QoS management architectures support a fixed set of QoS only, larger distributed systems encounter a variety of QoS requirements. This leads to a mix of different platforms and increased interoperability and management efforts. Generic QoS management could help to reduce this effort and provide flexibility towards new application requirements. We have developed a generic QoS management architecture based on CORBA. We present the motivations and requirements for the architecture, and we discuss the necessary extensions to the CORBA.