Kevin Wiesner

Adaptive traffic light prediction via Kalman filtering

Current fields of research in the automotive sector are dealing with the development of new driving-assistance-functions that aim to improve security, efficiency and comfort of vehicles. A significant field of study represents the prediction of traffic signals ahead that enable innovative functionalities such as Green Light Optimal Speed Advisory (GLOSA) or efficient start-stop control. This paper deals with the challenges of predicting future signals of traffic-adaptive traffic lights. First of all, we extract important characteristics of adaptive traffic lights and the underlying traffic situation at crossings relying on historical data of several Munich traffic lights. Based on these insights, we present and evaluate a generic model to predict future traffic-adaptive traffic signals at crossings. We show that with the proposed model, 95% of future signals can be predicted with an accuracy of 95% at best. On average, 71% of future signals can be predicted with an accuracy of 95% for the considered traffic lights.

A Privacy-Preserving Social P2P Infrastructure for People-Centric Sensing

The rapid miniaturization and integration of sensor technologies into mobile Internet devices combined with Online Social Networks allows for enhanced sensor information querying, subscription, and task placement within People-Centric Sensing networks. However, PCS systems which exploit knowledge about OSN user profiles and context information for enhanced service provision might cause an unsolicited application and dissemination of highly personal and sensitive data. In this paper, we propose a protocol extension to our OSN design Vegas which enables secure, privacy-preserving, and trustful P2P communication between PCS participants. By securing knowledge about social links with standard public key cryptography, we achieve a degree of anonymity at a trust level which is almost good as that provided by a centralized trusted third party.

Privacy-Preserving Calibration for Participatory Sensing

By leveraging sensors embedded in mobile devices, participatory sensing tries to create cost-effective, large-scale sensing systems. As these sensors are heterogeneous and low-cost, regular calibration is needed in order to obtain meaningful data. Due to the large scale, on-the-fly calibration utilizing stationary reference stations is preferred. As calibration can only be performed in proximity of such stations, uncalibrated measurements might be uploaded at any point in time. From the data quality perspective, it is desirable to apply backward calibration for already uploaded values as soon as the device gets calibrated. To protect the user’s privacy, the server should not be able to link all user measurements. In this paper, we therefore present a privacy-preserving calibration mechanism that enables both forward and backward calibration. The latter is achieved by transferring calibration parameters to already uploaded measurements without revealing the connection between the individual measurements. We demonstrate the feasibility of our approach by means of simulation.

PRICAPS: A System for Privacy-Preserving Calibration in Participatory Sensing Networks

By leveraging sensors embedded in mobile devices, participatory sensing tries to create cost-effective, largescale sensing systems. As these sensors are heterogeneous and low-cost, regular calibration is needed in order to obtain meaningful data. Due to the large scale, on-the-fly calibration utilizing stationary reference stations is preferred. As calibration can only be performed in proximity of such stations, uncalibrated measurements might be uploaded at any point in time. From the data quality perspective, it is desirable to apply backward calibration for already uploaded values as soon as the device gets calibrated. To protect the user’s privacy, the server should not be able to link all user measurements. In this article, we therefore present a privacypreserving calibration system that enables both forward and backward calibration. The latter is achieved by transferring calibration parameters to already uploaded measurements without revealing the connection between the individual measurements. We demonstrate the feasibility of our approach by means of simulation.

Private Pooling: A Privacy-Preserving Approach for Mobile Collaborative Sensing

Due to the emergence of embedded sensors in many mobile devices, mobile and people-centric sensing has become an interesting research field. A major aspect in this field is that quality and reliability of measurements highly depend on the device’s position and sensing context. A sound level measurement, for instance, delivers highly differing values whether sensed from inside a pocket or while carried in a user’s hand. Mobile collaborative sensing approaches try to overcome this problem by integrating several mobile devices as information sources in order to increase sensing accuracy. However, sharing data with other devices for collaborative sensing in return raises privacy concerns. By exchanging sensed values and context events, users might give away sensitive data, which should not be linkable to them. In this paper, we present a new mobile collaborative sensing protocol, Private Pooling, which protects the users’ privacy by decoupling the data from its contributors in order to allow for anonymous aggregation of sensing information.

Participatory sensing utilized by an advanced meteorological nowcasting system

The increasing spread of high-capacity sensor-equipped mobile devices provides new possibilities for scientific ambit. Many possible applications have been made accessible in the recent past. In this paper, the benefit of mobile sensing data collection for science is presented, using as example the combination of algorithmic weather forecasting and user contributed observations associated with participatory sensing. The newly developed DLR winter weather nowcasting system WHITE for the Munich airport intends a close cooperation with users and it utilizes data collected by mobile devices for validation. For these purposes, a mobile web application has been developed and operated during a campaign in the winter of 2012/2013. The technical structure of the application and its performance during the campaign as well as the main features of the WHITE system are the basic components of this paper.

An Analysis of Query Forwarding Strategies for Secure and Privacy-Preserving Social Networks

Decentralized Online Social Networks (OSNs) attempt to improve user privacy and security. One example is Vegas, a Peer-to-Peer (P2P) OSN which attempts to bring its users back into complete control of the data they share. Due to its decentralized characteristics, P2P OSNs cannot support social search functions in the same way users of centralized OSNs like Facebook are familiar with. Well-known and efficient P2P search algorithms cannot always be applied as knowledge about the structure of the social graph can be very limited. In this paper, we present an in-depth analysis of forwarding strategies to enable social search for secure and privacy preserving P2P OSNs. We compare well-known metrics from the field of unstructured P2P networks with metrics from the area of social network analysis and evaluate their applicability for P2P OSNs like Vegas. We simulate all metrics on four distinct datasets which were generated artificially from the ER- and the BA-model and from crawling data of Lastfm and Flickr. Our evaluation shows that prioritization based on knowledge from the ego network often yields the best results.

Collaborative Sensing Platform for Eco Routing and Environmental Monitoring

During the past decades, ecological awareness has been steadily gaining popularity. Especially in so called Megacities, the burden caused by air pollution is very high, as millions of people live together in a localized manner. To be aware of the current pollution status, selective measuring stations where deployed in the past. The idea of this work is to enable the masses to participate in obtaining and using their own measurements, e.g. with future generations of mobile phones that are equipped with adequate sensors. The proposed platform allows for a high resolution environmental monitoring and provides additional services such as Eco Routing or visualization. Furthermore, we will present the results of the platform’s performance as well as a comparison between the traditional (shortest/fastest) routing and the novel (shortest/fastest) Eco Routing approach.

Technologische Herausforderungen für kontextsensitive Geschäftsanwendungen

Der Einfluss der Informationstechnologie auf die Wirtschaft nimmt standig zu. Der geschickte Einsatz von Information und Kommunikation kann wesentlich zum Erfolg eines Unternehmens beitragen. Die standig wachsende Leistungsfahigkeit von mobilen Endgeraten ermoglicht durch den Einsatz von Kontextinformationen wie dem Aufenthaltsort des Endgerates revolutionare Anwendungen. In diesem Artikel erlautern wir, wie Geschaftsanwendungen mit Kontextinformationen umgehen konnen und weshalb sich dennoch der Einsatz von Kontext in Geschaftsanwendungen noch nicht durchgesetzt hat. Daruber hinaus zeigen wir aktuelle Entwicklungen und Trends im Umgang mit mobilen Endgeraten und Kommunikation auf, die sich schlieslich zu einem Software-Okosystem kombinieren lassen, welches die derzeitigen Probleme uberwinden kann.

Preventing Restricted Space Inference in Online Route Planning Services

Online route planning services compute routes from any given location to a desired destination address. Unlike offline implementations, they do so in a traffic-aware fashion by taking into consideration up-to-date map data and real-time traffic information. In return, users have to provide precise location information about a route’s endpoints to a not necessarily trusted service provider. As suchlike leakage of personal information threatens a user’s privacy and anonymity, this paper presents PrOSPR, a comprehensive approach for using current online route planning services in a privacy-preserving way, and introduces the concept of k-immune route requests to avert inference attacks based on restricted space information. Using a map-based approach for creating cloaked regions for the start and destination addresses, our solution queries the online service for routes between subsets of points from these regions. This, however, might result in the returned path deviating from the optimal route. By means of empirical evaluation on a real road network, we demonstrate the feasibility of our approach regarding quality of service and communication overhead.