Basil Hess

Toward location-aware Web: extraction method, applications and evaluation

Location-based services (LBS) belong to one of the most popular types of services today. However, a recurring issue is that most of the content in LBS has to be created from scratch and needs to be explicitly tagged to locations, which makes existing Web content not directly usable for LBS. In this paper, we aim at making Web sites location-aware and feed this information to LBS. Our approach toward location-aware Web is threefold: First, we present a location extraction method: SALT. It receives Web sites as input and equips them with location tags. Compared to other approaches, SALT is capable of extracting locations with a precision up to the street level. Performance evaluations further show high applicability for practice. Second, we present three applications for SALT: Webnear.me, Local Browsing and Local Facebook. Webnear.me offers location-aware Web surfing through a mobile Web site and a smartphone app. Local Browsing adds the feature to browse by nearby tags, extracted from Web sites delivered by SALT. Local Facebook extends location tagging to social networks, allowing to run SALT on one’s own and one’s friends’ timeline. Finally, we evaluate SALT for technology acceptance of Webnear.me through a formative user study. Through real user data, collected during a 3 months pilot field deployment of Webnear.me, we assess whether SALT is a proper instance of “location of a Web site”.

Discovering the web by location with Webnear.me

While mobile apps for smartphones are consistently popular, the most common way for users to explore the Internet is still through the WWW. Websites are however not explicitly equipped with information regarding their location -- an issue for use on mobile devices. With this work we aim at bridging this gap by adding a location layer to websites and letting users browse by nearby websites. We present a mobile demonstrator called Webnear.me. Once installed on the smartphone, users are able to list websites in proximity their location: Near.me. Websites in proximity to another one can be browsed too: Near.this. With our application we strive to give answers to the question of how web content should be tagged with locations. We approach this question with several content- and user-centric location-tagging methods.

Achieving Targeted Mobile Advertisements While Respecting Privacy

Broadcasted mobile advertisements are increasingly being replaced by targeted mobile advertisements through consumer profiling. However privacy is a growing concern among consumers who may eventually prevent the advertising companies from profiling them. This paper proposes an agent-based targeting algorithm that is able to guarantee full consumer privacy while achieving mobile targeted advertising. We implemented a grocery discount-discovery application for iPhone that makes use of the new approach. We show that on modern hardware like on the iPhone, it’s feasible to run a client-based and privacy-preserving targeting algorithm with minimal additional computational overhead compared to a random advertising approach. We evaluated the targeting method by conducting a large-scale field-experiment with 903 participants. Results show that the computational overhead on user devices is well tolerated, compared to the control group with randomized advertising the targeting group showed a significantly increased application usage of 18%.

Building Location-Aware Web with SALT and Webnear.Me

Abstract Location Based Services (LBS) belong to the most popular type of mobile applications today. Most of the content in LBS has to be created from scratch and needs to be explicitly location-tagged, which makes existing web content not directly usable for LBS. In this paper we aim at making websites location-aware and feed this information to LBS. For this purpose we first present SALT, an engine that receives websites as input and equips them with location-tags. Compared to other approaches, SALT is capable of extracting locations with a precision up to the street level. While the engine basically works unsupervised, it is also capable to handle user-feedback to improve the results. Second, we present Webnear.me as a use case of SALT. Webnear.me offers location-aware web surfing through a mobile website. It displays nearby websites depending on: 1) the users current location and 2) the currently visited website. Finally we show that our application has a high acceptance from users through a formative user study.

Challenges and Current Results of the TWISNet FP7 Project

Over the past years, the deployment of sensor networks in industrial environments has attracted much attention in several business domains. An increasing number of applications have been developed, ranging from defense, public security, energy management, traffic control to health care. Sensor networks are particularly interesting due to their ability to control and monitor physical environments. Nevertheless, several technical (e.g. remote management, deployment) and security (e.g. user’s privacy, data confidentiality and reliability) challenges deter their integration in industrial processes. This extended abstract presents an overview of the current research results on an architecture aiming at supporting and securing the integration of sensor networks into large scale industrial environments. This work is carried out in the “TWISNet: Trustworthy Wireless Industrial Sensor Networks” project financially supported by the EC under grant agreement FP7-ICT-258280.