Informing drivers about the predicted state of upcoming traffic lights is considered a key solution to reduce unneeded energy expenditure and dilemma zones at intersections. However, newer traffic lights can react to traffic demand, resulting in spontaneous switching behavior and poor predictability. To assess whether future traffic light assistance services are viable, it is crucial to understand how strongly predictability is affected by such spontaneous switching behavior. Previous studies have so far only reported percentages of adaptivity-capable traffic lights, but the actual switching behavior has not been measured. Addressing this research gap, we conduct a large-scale predictability evaluation based on 424 million recorded switching cycles over four weeks for 18,009 individual traffic lights in Hamburg. Two characteristics of predictability are studied: cycle discrepancy and wait time diversity. Results indicate that fewer traffic lights exhibit hard-to-predict switching behavior than suggested by previous work, considering a reported number of 90.7% adaptive traffic lights in Hamburg. Contrasting previous work, we find that not all traffic lights capable of adaptiveness may necessarily exhibit low predictability. We critically review these results and derive avenues for future research.

Dense urban areas like Hamburg strive to increase the attractiveness of cycling to overcome mobility-related problems such as space limitations, air pollution, and noise levels. To address this issue, smart mobility solutions can encourage more people to choose cycling. Green Light Optimal Speed Advisory for bikes (bike-GLOSA) can reduce the number of stops at red lights, allow smoother traveling, and convey a digital advantage to cyclists. However, the city-wide implementation of bike-GLOSA introduces new challenges, including the need for automated lane matching. Humans may employ spatial reasoning to determine the most logical sequence of lanes and associated traffic lights across intersection topologies for a given route. In this paper, we aim to replicate this spatial reasoning using Geospatial Artificial Intelligence (GeoAI). The proposed Machine Learning (ML) model not only overcomes limitations associated with location- and camera-based lane matching approaches. It also outperforms a previous route-based approach by 8%, with an F1 score of 92% on our test dataset. We critically examine our approach and real-world results to identify potential limitations and avenues for future research.

Bluetooth Low Energy (BLE) beacons raised high expectations when introduced as a cheap, easy to handle indoor positioning solution that is widely supported by smartphones. Experiments soon showed that RSSI values measured by devices vary significantly even when conducted under identical setups. As a result, BLE beacons can be used for what they are designed, i.e., proximity detection. However, setting up indoor positioning systems that cover complete floors and buildings with acceptable accuracy and out of the box is far from being straightforward. This paper evaluates setups for BLE indoor positioning at an inhouse fair to position dedicated smartphones among visitors and exhibition stands. It investigates how established positioning approaches, namely trilateration, fingerprinting, and centroid, respond to the impact of the crowd. Experiments show for the .95 percentile that the weighted centroid technique WCWCL achieves the highest position accuracy of 5.7 meters in the crowded space. Fingerprinting, on the other hand, is heavily impacted by the crowd. While most accurate in an empty/uncrowded space, its accuracy decreases to 11 meters in the crowded space. Further, the paper shows that for a setup with randomly distributed beacons a density of one beacon per 40 square meter is a sweet spot beyond which accuracy does not further increase.

The ability of service systems to compose their structure on-demand at run time and to change their application behavior in response to changes in their computational environment will become increasingly important. Approaches related to self-organizing software systems focus on automated composition of dependent service systems at run time but lack the specification of complex service structures whereas context-dependent system changes are usually limited to adaptations local to a subsystem and lack the ability to perform complex modifications in a decentralized infrastructure. Hence, in this paper, we bridge the gap between the specification of complex, context-aware service structures at design time and their reliable adaptation at run time. We propose a context-aware, role-based collaboration specification to model adaptable, collaborative smart service systems as well as an integrated middleware architecture for on-demand composition and subsequent reliable adaptation of such. We validate our approach using a case-study from the tech- enhanced teaching domain.

No abstract available.

Web application frameworks are a proven means to accelerate the development of interactive web applications. However, implementing collaborative real-time applications like Google Docs requires specific concurrency control services (i.e. document synchronization and conflict resolution) that are not included in prevalent general-purpose frameworks like jQuery or Knockout. Hence, developers have to get familiar with specific collaboration frameworks (e.g. ShareJS) which substantially increases the development effort. To ease the development of collaborative web applications, we propose a set of source code annotations representing a lightweight mechanism to introduce concurrency control services into mature web frameworks. Those annotations are interpreted at runtime by a dedicated collaboration engine to sync documents and resolve conflicts. We enhanced the general-purpose framework Knockout with a collaboration engine and conducted a developer study comparing our approach to a traditional concurrency control library. The evaluation results show that the effort to incorporate collaboration capabilities into a web application can be reduced by up to 40 percent using the annotation-based solution.

As pervasive computing meets social networks, there is a fast growing research field called pervasive social computing. Applications in this area exploit the richness of information arising out of people using sensor-equipped pervasive devices in their everyday life combined with intense use of different social networking services. We call this set of information pervasive social context. We provide a taxonomy to classify pervasive social context along the dimensions space, time, people, and information source (STiPI) as well as commenting on the type and reason for creating such context. A survey of recent research shows the applicability and usefulness of the taxonomy in classifying and assessing applications and systems in the area of pervasive social computing. Finally, we present some research challenges in this area and illustrate how they affect the systems being surveyed.

In the light of the Web 2.0 movement, web-based collaboration tools such as Google Docs have become mainstream and in the meantime serve millions of users. Apart from established collaborative web applications, numerous web editors lack multi-user support even though they are suitable for collaborative work. Enhancing these single-user editors with shared editing capabilities is a costly endeavor since the implementation of a collaboration infrastructure (accommodating conflict resolution, document synchronization, etc.) is required. In this paper, we present a generic transformation approach capable of converting single-user web editors into multi-user editors. Since our approach only requires the configuration of a generic collaboration infrastructure (GCI), the effort to inject shared editing support is significantly reduced in contrast to conventional implementation approaches neglecting reuse. We also report on experimental results of a user study showing that converted editors meet user requirements with respect to software and collaboration qualities. Moreover, we define the characteristics that editors must adhere to in order to leverage the GCI.

Disclosing personal and contextual information enables a widespread use of services in the internet in a comfortable working environment adapted to individual user needs and preferences. On the other hand, information disclosure raises serious privacy issues. In this paper, we propose a novel integrated approach for achieving a good balance between convenience and privacy. We integrate privacy- enhancing identity management (PIM) with extended role- based access control mechanisms (RBAC) in an extended concept and architecture. In combination, both mechanism enable a fine grained control of what private and contextual information is disclosed to whom in what situation. We have implemented a privacy-enhanced adaptive communicator application (PEAC) to validate our approach.

Following Mark Weiser's vision of ubiquitous computing and calm technology, computer systems should run in the background, preferably without the user noticing it at all. The gathering and disclosure of contextual information on the one hand enables the improvement of system behaviour towards a more autonomous and adaptive behaviour but on the other hand raises privacy issues by disclosing personal data. Thus, a major challenge in ubiquitous computing environments is achieving a good balance between convenience and control over personal data. In this paper we describe an access control mechanism for context data that enables the user to control his personal data in a convenient and non-intrusive way. The approach is based on existing role-based access control mechanisms but extends them as follows. Firstly, our approach is owner-centric, i.e. it is under control of each user, to whom his context is propagated throughout the system. Secondly, our approach does not only control the access to context data but also utilizes context information to simplify the management of these control mechanisms to make the handling of access control more convenient to the user. And thirdly, it introduces individual roles for each user and thus replaces the centrally defined role model of common role-based access control by distinct models for each user. We have validated our approach based on an extended instant messaging system called Adaptive Multimedia Messenger, providing varying buddy information dependent on the access permission of the requesting user

Context-awareness is a key technology for adaptive software systems. While past and recent research effort investigated the gathering of low-level information, implementation- and domain-driven approaches for context frameworks and conceptual models of context, vertical approaches combining the aspects of modeling, and implementation are still missing. In this paper, we propose a scalable and distributed context service for integrating heterogeneous context sources in large-scale mobile environments based on a layered context model enabling the modeling and support of multiple domain-specific context models. We implemented an adaptive multimedia messaging system on top of a 4G mobile communication testbed which supports the context-dependent selection of codecs and communication modes to validate our approach.