Karin Anna Hummel

Optimal deployment of charging stations for electric vehicular networks

In a smart city environment, we look at a new, upcoming generation of vehicles running on electric power supplied by on-board batteries. Best recharging options include charging at home, as well as charging at public areas. In this setting, electric vehicles will be informed about public charging stations using wireless communications. As the charging stations are shared resources, cooperating electric vehicles have the potential to avoid unbalanced use of recharging stations and lengthy waiting times. We present a model for electric vehicles and their battery depletion, vehicle mobility, charging stations, and give a solution for optimal placement of charging stations in a smart city. Our placement approach is based on genetic programming and simulation of electric vehicles which move on a real map of a European city. We show that after a few evolution steps, an optimal solution of the charging infrastructure is derived based on mean trip times of electric vehicles.

Application-driven design of aerial communication networks

Networks of micro aerial vehicles (MAVs) equipped with various sensors are increasingly used for civil applications, such as monitoring, surveillance, and disaster management. In this article, we discuss the communication requirements raised by applications in MAV networks. We propose a novel system representation that can be used to specify different application demands. To this end, we extract key functionalities expected in an MAV network. We map these functionalities into building blocks to characterize the expected communication needs. Based on insights from our own and related real-world experiments, we discuss the capabilities of existing communications technologies and their limitations to implement the proposed building blocks. Our findings indicate that while certain requirements of MAV applications are met with available technologies, further research and development is needed to address the scalability, heterogeneity, safety, quality of service, and security aspects of multi- MAV systems.

Modeling context-aware e-learning scenarios

In the last decade, e-learning has been introduced to a variety of blended learning scenarios, such as life-long learning, university lectures, and game-based learning. In all these scenarios the learners situation or context is an essential asset in designing the learning process. Recent research suggests aiding the design process through the use of visual modeling approaches. Pervasive computing environments particularly call for extending these approaches in terms of enhanced context-awareness. This paper addresses these needs by introducing a UML-based modeling extension for explicitly including relationships between context and learning activities in the learning design models. The feasibility and applicability of our approach is demonstrated by a laboratory lecture case study, and respectively by a context-aware learning prototype that was developed using RFID technology for sensing of nearby persons and physical resources.

Micro aerial vehicle networks: an experimental analysis of challenges and opportunities

The need for aerial networks is growing with the recent advance of micro aerial vehicles, which enable a wide range of civilian applications. Our experimental analysis shows that wireless connectivity among MAVs is challenged by the mobility and heterogeneity of the nodes, lightweight antenna design, body blockage, constrained embedded resources, and limited battery power. However, the movement and location of MAVs are known and may be controlled to establish wireless links with the best transmission opportunities in time and space. This special ecosystem undoubtedly requires a rethinking of wireless communications and calls for novel networking approaches. Supported by empirical results, we identify important research questions, and introduce potential solutions and directions for investigation.

A Robust Decentralized Job Scheduling Approach for Mobile Peers in Ad-hoc Grids

The increasing capabilities and spreading of mobile technology raise the opportunity to integrate potentially unstable mobile devices as resources into grids. In this work, we contribute by proposing and evaluating a robust decentralized job scheduling approach for mobile peers forming an ad-hoc grid. The scheduling approach is based on a first come first serve strategy executed locally by each peer. Additionally, the peer performs matchmaking between a jobs requirements and the devices capabilities autonomously. Coordination between mobile peers is based on job queues shared within a distributed virtual shared memory (VSM). By applying proactive and reactive fault tolerance mechanisms we were able to increase the robustness of the scheduler. A prototypical implementation using the VSM CORSO, Java, and Condor ClassAds demonstrates the feasibility of our approach. The conducted experiments show that the job loss ratio and the average job response time can be decreased by adding fault tolerance mechanisms.

SCAMPI: service platform for social aware mobile and pervasive computing

Allowing mobile users to find and access resources available in the surrounding environment opportunistically via their smart devices could enable them to create and use a rich set of services. Such services can go well beyond what is possible for a mobile phone acting alone. In essense, access to diverse resources such as raw computational power, social networking relationships, or sensor readings across a set of different devices calls for distributed task execution. In this paper, we discuss the SCAMPI architecture designed to support distributed task execution in opportunistic pervasive networks. The key elements of the architecture include leveraging human social behavior for efficient opportunistic interaction between a variety of sensors, personal communication devices and resources embedded in the local environment. The SCAMPI architecture abstracts resources as service components following a service-oriented model. This enables composing rich applications that utilize a collection of service components available in the environment.

Cellular data meet vehicular traffic theory: location area updates and cell transitions for travel time estimation

Road traffic can be monitored by means of static sensors and derived from floating car data, i.e., reports from a sub-set of vehicles. These approaches suffer from a number of technical and economical limitations. Alternatively, we propose to leverage the mobile cellular network as a ubiquitous mobility sensor. We show how vehicle travel times and road congestion can be inferred from anonymized signaling data collected from a cellular mobile network. While other previous studies have considered data only from active devices, e.g., engaged in voice calls, our approach exploits also data from idle users resulting in an enormous gain in coverage and estimation accuracy. By validating our approach against four different traffic monitoring datasets collected on a sample highway over one month, we show that our method can detect congestions very accurately and in a timely manner.

Energy-efficient wireless mesh infrastructures

The Internet comprises access segments with wired and wireless technologies. In the future, we can expect wireless mesh infrastructures (WMIs) to proliferate in this context. Due to the relatively low energy efficiency of wireless transmission, as compared to wired transmission, energy consumption of WMIs can represent a significant part of the energy consumption of the Internet as a whole. We explore different approaches to reduce energy consumption in WMIs, taking into account the heterogeneity of the technologies and the interaction with wired networks. Finally, we present an example scenario where the application of these methods is discussed.

Environmental context sensing for usability evaluation in mobile HCI by means of small wireless sensor networks

In usability evaluations, experiments are often conducted in closed laboratory situations to avoid disturbing influences. Due to non-realistic usage assumptions, this approach has important shortcomings when mobile Human Computer Interactions (m-HCI) have to be evaluated. Field studies allow to perform experiments close to real-world conditions, but potentially introduce influences caused by the environment. In this paper, we aim at distinguishing application shortcomings from environmental disturbances which both potentially cause decreased user performance. Our approach is based on monitoring environmental conditions during the usability experiment, such as light, acceleration, sound, temperature, and humidity, and relating them to user actions. Therefore, a mobile context-framework has been developed based on a small Wireless Sensor Network (WSN). First results are presented that point at increased delays and error rates of user tasks under induced environmental disturbances. Additionally, we demonstrate the potential of environmental monitoring for understanding user performance.

The Cellular Network as a Sensor: From Mobile Phone Data to Real-Time Road Traffic Monitoring

Mobile cellular networks can serve as ubiquitous sensors for physical mobility. We propose a method to infer vehicle travel times on highways and to detect road congestion in real-time, based solely on anonymized signaling data collected from a mobile cellular network. Most previous studies have considered data generated from mobile devices active in calls, namely Call Detail Records (CDR), an approach that limits the number of observable devices to a small fraction of the whole population. Our approach overcomes this drawback by exploiting the whole set of signaling events generated by both idle and active devices. While idle devices contribute with a large volume of spatially coarse-grained mobility data, active devices provide finer-grained spatial accuracy for a limited subset of devices. The combined use of data from idle and active devices improves congestion detection performance in terms of coverage, accuracy, and timeliness. We apply our method to real mobile signaling data obtained from an operational network during a one-month period on a sample highway segment in the proximity of a European city, and present an extensive validation study based on ground-truth obtained from a rich set of reference datasources - road sensor data, toll data, taxi floating car data, and radio broadcast messages.