Christian Meurisch

Noisemap: multi-tier incentive mechanisms for participative urban sensing

Noise pollution is a problem increasingly acknowledged by authorities and governments around the globe. At last years PhoneSense we presented Noisemap, a participating sensing application to accurately measure noise pollution. Noisemap incorporated frequency calibration to overcome the limited microphone hardware. The challenge remaining is how to motivate smartphone users to sacrifice their time and battery on measuring noise. A user study was conducted with 49 users divided into three groups. As expected the average measurements taken per user increased from 402 to 3,357 as the number of incentive schemes increased. Over the course of 7 weeks the users captured more than 85, 000 measurements, measuring for more than six hours on average.

Upgrading Wireless Home Routers for Enabling Large-Scale Deployment of Cloudlets

Smartphones become more and more popular over recent years due to their small form factors. However, such mobile systems are resource-constrained in view of computational power, storage and battery life. Offloading resource-intensive tasks (aka mobile cloud computing) to distant (e.g., cloud computing) or closely located data centers (e.g., cloudlet) overcomes these issues. Especially, cloudlets provide computational power with low latency for responsive applications due to their proximity to mobile users. However, a large-scale deployment of range-restricted cloudlets is still an open challenge. In this paper, we propose a novel concept for a large-scale deployment of cloudlets by upgrading wireless home routers. Beside router’s native purpose of routing data packets through the network, it can now offer computing resources with low latency and high bandwidth without additional hardware. Proving our concept, we conducted comprehensive benchmark tests against existing concepts. As result, the feasibility of this concept is shown and provide a promising way to large-scale deploy cloudlets in existing infrastructures.

Adaptive task-oriented message template for in-network processing

In disaster situations or on emergency terrains, Internet and Cloud access may be restricted; it may still be important to process complex resource-intensive tasks and to acquire distributed information for emergency response, using ad-hoc networks among, e.g., first responder mobile devices. Corresponding approaches towards coordination, resource utilization, and interoperability are still challenging. This paper introduces the concept of adaptive task-oriented message templates (ATMT) as a basis for overcoming these issues and for enabling cooperative in-network processing without additional synchronization overhead for mobile devices. An ATMT serves as a self-encapsulated message containing the operation chains that need to be executed as well as the required data. In order to address heterogeneity and interoperability issues, we integrate a lightweight ontology. Depending on the current utilization, devices can autonomously decide whether to participate in the network or not. We evaluate our approach in an indoor testbed with 8 wireless mesh nodes. The results confirm that our approach efficiently supports cooperation among heterogeneous devices towards utilizing available in-network resources while reducing network traffic.

Fitness tracker or digital personal coach: how to personalize training

The use of activity tracking systems promises support in meeting physical fitness goals. This support generally focuses on an improved self-awareness based on the review of own fitness data, sometimes enhanced by social features like performance comparison. We see a demand for a goal-driven support of fitness goal achievement to be addressed by a digital coach. The digital coach identifies strength and weaknesses of the subject, generates a training plan, motivates and helps, just like a real coach. Such a digital coach will highly benefit from the activity tracking system data which is used to personalize the training plan based on performed activities.

Noisemap: Discussing Scalability in Participatory Sensing

Environmental pollutants are an ever increasing problem in dense urban environments. To assess the effect of these pollutants, an unprecedented density of data is needed for large areas (cities, states, countries). In the past, participatory sensing has been proposed as a mean to acquire large sets of data. Since the smartphone is ubiquitous, scalability seems to be no problem anymore. In reality this far from the truth. Measuring their environment, people need to invest their time. For Android and iOS the application needs to compete with more than 700,000 other applications. Measuring large amounts of data is only possible, if we can attract large amounts of casual users. Since 2011, we have been working with and on Noisemap. Noisemap is one of many applications that uses the microphone to measure sound pressure. It then uploads the captured data to our backend, where the data is processed and visualized. Noisemap is officially available since February 2012, has been downloaded over 2,500 times, and has more than 1,000 registered users, which have collected over 500,000 unique data points in 39 countries and 58 cities. We want to share the current state of Noisemap as a multi-platform tool on Android and iOS, as well as our experience in scaling the application.

Labels: quantified self app for human activity sensing

An in-depth understanding of human activity is essential for building personalized systems like Google Now to support users in everyday life. For that understanding, a comprehensive user-annotated human activity corpus is highly relevant. However, getting a personal dataset for data mining tasks is always a big challenge for researchers. Our approach is Labels, a self-tracking mobile application to collect a human activity corpus with ground-truth data. Providing a user interface users are able to annotate automatically collected sensor data from mobile, desktop and social with valuable metadata (ground-truth), e.g., their performed activities. In this paper, we will present Labels, its functionalities and quality assurance mechanisms for annotated data. We evaluated this app within a four-week field study with 163 participants. The collected dataset with over 43 thousand manual annotated data will also be presented.

NICER911: Ad-hoc Communication and Emergency Services Using Networking Smartphones and Wireless Home Routers

Reliable communication and emergency services are crucial for the success of crisis response and management. However, todays emergency technologies used by rescuers and civilians mainly rely on either centralized or specialized approaches, which reveal individual issues in infrastructure-less crisis scenarios. In this paper, we propose an emergency communication system called NICER911 that uses ad-hoc device-to-device communications enabling efficient data exchanges and delay-tolerant networking. On top of that, we integrate three types of emergency services allowing cooperations between rescuers and civilians: a social network, rescue instructions, and a self-rescue system. We implement a proof-of-concept prototype to show the feasibility, resource efficiency, and ease of use by preliminary quantitative and qualitative evaluations.

Decision Support for Computational Offloading by Probing Unknown Services

Mobile Cloud Computing (MCC) leverages resourceful data centers that are distant (aka the cloud) or closely located (aka edge servers) for computational offloading to overcome resource limitations of modern mobile systems like smartphones or IoT devices. Many research works investigate context-aware offloading decision algorithms aiming to find the best offloading system at runtime. However, all approaches require prior knowledge of the offloading systems or a running service profiler on the backend system. In this paper, we present a novel approach that overcomes this issue by first probing available unknown services such as nearby cloudlets or the distant cloud, and networks in an energy-efficient way at runtime to make better offloading decisions. For that, we investigate a probing strategy to assess these unknown services by offloading micro tasks and accurately predicting the performance for larger offloading tasks using regression models. Our evaluation on three algorithms with different time complexities shows that we achieve high prediction accuracies up to 85.5%, already after probing of two micro tasks running in the range of few milliseconds. To the best of our knowledge, this is the first supplement approach for offloading decision support that can handle unknown third-party services requiring no prior knowledge about these offloading systems and making no assumptions for real-world deployments.

Router-Based Brokering for Surrogate Discovery in Edge Computing

In-network processing pushes computational capabilities closer to the edge of the network, enabling new kinds of location-aware, real-time applications, while preserving bandwidth in the core network. This is done by offloading computations to more powerful or energy-efficient surrogates that are opportunistically available at the network edge. In mobile and heterogeneous usage contexts, the question arises how a client can discover the most appropriate surrogate in the network for offloading a task. In this paper, we propose a brokering mechanism that matches a client with the best available surrogate, based on specified requirements and capabilities. The broker is implemented on standard home routers, and thus, leverages the ubiquity of such devices in urban environments. To motivate the feasibility of this approach, we conduct a coverage analysis based on collected access point locations in a major city. Furthermore, the brokering functionality introduces only a minimal resource overhead on the routers and can significantly reduce the latency compared to distant, cloud-based solutions.

An Extensible Pervasive Platform for Large-Scale Anticipatory Mobile Computing

Anticipatory mobile computing is an emerging research field in pervasive environments. However, building multiple anticipatory applications to proactively support a user on his behalf still involves a disproportionate effort through their interdisciplinary nature and individual complex development from scratch. In this paper, we present architectural concepts and a reference implementation of a distributed platform acting as base frame for various anticipatory mobile applications to provide cooperative personal assistants. We demonstrate that our proof of concept prototype enables fast and time-saving development of various cooperating intelligent assistants through a hierarchical modular approach. We further show that this approach makes energy-efficient mobile applications currently available for iOS and Android possible while the platform is horizontal scalable to growing number of users and assistance use cases. Feedback from end-users and researchers indicates a high user experience for using our apps and developing new assistants. Our proposed open source platform provides the scaffolding for future research in personal collaborative assistance systems which proactively guide and autonomously support users.