Andrea Hess

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.

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.

Data-driven Human Mobility Modeling: A Survey and Engineering Guidance for Mobile Networking

Over the last decades, modeling of user mobility has become increasingly important in mobile networking research and development. This has led to the adoption of modeling techniques from other disciplines such as kinetic theory or urban planning. Yet these techniques generate movement behavior that is often perceived as not “realistic” for humans or provides only a macroscopic view on mobility. More recent approaches infer mobility models from real traces provided by positioning technologies or by the marks the mobile users leave in the wireless network. However, there is no common framework for assessing and comparing mobility models. In an attempt to provide a solid foundation for realistic mobility modeling in mobile networking research, we take an engineering approach and thoroughly discuss the required steps of model creation and validation. In this context, we survey how and to what extent existing mobility modeling approaches implement the proposed steps. This also summarizes helpful information for readers who do not want to develop a new model, but rather intend to choose among existing ones.

Efficient neighbor discovery in mobile opportunistic networking using mobility awareness

To detect peers in mobile opportunistic networks, mobile devices transmit and listen for beacons (“scanning”). If networks are sparse, devices spend quite a bit of energy scanning the vicinity for possible contacts with their radios. Numerous techniques were developed to adapt the scanning intervals as a function of the observed node density. In this paper, we complement such techniques by considering that protocol exchanges between nodes require contacts of a minimal time span and infer scanning opportunities from node mobility. The adaptive beaconing presented in this paper reduces the scanning effort significantly without “losing” many contacts that last long enough to (i) fully establish an ad-hoc connection between two devices and to (ii) transfer a sizeable amount of data. We propose a theoretical model to derive connection probabilities from sojourn times in different mobility settings and evaluate the impact on energy consumption and data forwarding performance using simulations with different mobility models.

Estimating human movement activities for opportunistic networking: A study of movement features

In mobility-assisted, opportunistic networks, data is disseminated in a store-and-forward manner by means of spontaneously connecting mobile devices. Therefore, mobility itself moves in the center of investigation. Knowledge about movement characteristics of single devices can be used to add realism to random mobility models and to understand the likelihood of communication options. This paper contributes to the field of observing movement characteristics of single devices for opportunistic networks by describing movement features and investigating how these features can contribute to human movement activity estimation. Activity descriptions are useful for characterizing the purpose of movement. Additionally, in case movement patterns are uncertain or fragmentary, knowledge about activities may help to estimate average movement characteristics faster. We use activity estimation based on the Naïve Bayes classifier applied to a multi-variate feature set consisting of commonly considered movement features. We investigate the classification success rate experimentally when using all features and when using only a subset of features. Therefore, we conducted a user study collecting real-trip GPS traces labeled by the users. We selected four most frequent urban movement use case activities for classification and achieved a success rate of 80.65%.

VANET mobility modeling challenged by feedback loops

VANET applications are often providing street traffic information to vehicles and drivers, regarding, for instance, traffic conditions and parking space availability. This information influences in turn the driving behavior in real-world settings. Mobility models used in current VANET simulations are mostly ignoring this feedback entirely. In cases the feedback is included, it is mainly based on ad-hoc approaches with lack of generality.

Cognitive Radio Algorithms Coexisting in a Network: Performance and Parameter Sensitivity

This paper studies the performance of cognitive radios in a scenario where different pairs of radios adopt different cognition/decision making approaches. We want to assess: 1) if there is a category of cognitive radio algorithms that consistently outperforms the others and 2) how sensitive different algorithms are to suboptimal parameter setting. Our approach is to take a representative set of well-known classes of cognitive radio algorithms, mix and match them throughout thousands of simulations, and determine which seem to perform better. We find that choosing a cognitive radio algorithm means finding a balance between the best-case performance obtained by optimally setting all parameters, and the behavior in uncontrolled, unknown environments, where suboptimal decisions are likely to be made. The approaches we consider, namely reinforcement learning, optimization metaheuristics, multi-armed bandit solutions, and supervised learning, greatly differ in their performance. For example, schemes that are able to achieve a high throughput in our simulation study are more sensitive to suboptimally set parameters.

Automatic Adaptation and Analysis of SIP Headers Using Decision Trees

Software implementing open standards like SIP evolves over time, and often during the first years of deployment, products are either immature or do not implement the whole standard but rather only a subset. As a result, messages compliant to the standard are sometimes wrongly rejected and communication fails. In this paper we describe a novel approach called Babel-SIP for increasing the rate of acceptance for SIP messages. Babel-SIP is a filter that is put in front of a SIP parser and analyzes incoming SIP messages. It gradually learns which messages are likely to be accepted by the parser, and which are not. Those classified as probably rejected are then adapted such that the probability for acceptance is increased. In a number of experiments we demonstrate that our filter is able to drastically increase the acceptance rate of problematic SIP REGISTER and INVITE messages. Additionally we show that our approach can be used to analyze the faulty behavior of a SIP parser by using the generated decision trees.

Extrapolating sparse large-scale GPS traces for contact evaluation

Human mobility traces are increasingly used for more realistic evaluation of mobile (opportunistic) communication systems. Although GPS traces yield the most detailed data sets, they are often limited in scale and may be incomplete since they are captured using mobile devices carried by volunteers. In this paper, we explore mechanisms to improve completeness and connectivity patterns of sparse GPS traces and assess their impact by means of the GeoLife data set. We also outline insights into geographic propagation that can be gained through these large-scale location measurements.

Mobilität im ,,Future Internet“

ZusammenfassungEine der Herausforderungen im ,,Future Internet“ ist durch die Mobilität der Benutzer aber auch durch die der Ressourcen gegeben. Während Benutzer zunehmend mit tragbaren Geräten auf Internetdienste zugreifen, wurden Internetprotokolle unter der Annahme stationärer Knoten entworfen. Im Artikel wird ein Überblick über Mechanismen zur Unterstützung von Gerätemobilität gegeben, die für eine mobilitätsfreundliche Architektur des Future Internet von Relevanz sind. Darunter fallen Mechanismen zur Verbesserung der Konnektivität und Tolerierung von Verbindungsunterbrechungen sowie Mechanismen zur effizienten Adressierung und für übergangsloses Handoff-Management. Zusätzlich wird das Konzept der Mobility-Awareness vorgestellt, das auf Basis aktueller Bewegung reaktive und proaktive Adaptierungen von Netzwerkprotokollen ermöglicht.Die Mobilität von Netzwerkressourcen wird als zweite Form der Mobilität im Überblick diskutiert. Zusammen mit der Netzwerkvirtualisierung kann die Ressourcenmigration zur Flexibilisierung des Future Internet beitragen. Ressourcen wie virtuelle Links und virtuelle Router können zur Erreichung unterschiedlicher Ziele, wie zum Beispiel zur Verbesserung der Netzwerkqualität, Robustheit oder Energieeffizienz, migriert werden.