MONAMI 2011 Best Paper Awards

Best Paper

Classification of Hidden Users' Profiles in Wireless Communications

by Eduardo Rocha, Paulo Salvador, and António Nogueira
Instituto de Telecomunicações, University of Aveiro, Portugal

Abstract - The Internet can be seen as a mix of several services and applications running on top of common protocols. The emergence of several web-applications changed the users' interaction paradigm by placing them in a more active role allowing them to share photos, videos and much more. The analysis of the profile of each user, both in wired and wireless networks, becomes very interesting for tasks such as network resources optimization, service personalization and security. In this paper, we propose a promiscuous wireless passive monitoring classification approach that can accurately create users' profiles in terms of the used web-applications and does not require authentication with the wireless Access Point. By extracting appropriate layer 2 traffic metrics, performing a Wavelet Decomposition and analyzing the obtained scalograms, it is possible to analyze the traffic's time and frequency components. An appropriate communication profile can then be defined in order to describe this frequency spectrum which is characteristic to each webbased application. Consequently, it is possible to identify the applications that are being used by the different connected clients and build user-profiles. Wireless traffic generated by several connected clients running some of the most significant web-based applications was captured and analyzed and the obtained results show that it is possible to obtain an accurate application traffic mapping and an accurate user profiling.

Best Student Paper

Enabling Continuously Evolving Context Information in Mobile Environments by Utilizing Ubiquitous Sensors

by Stefan Forsström and Theo G. Kanter
Mid Sweden University, Sweden

Abstract - Context-aware applications require local access to current and relevant views of context information derived from global sensors. Existing approaches provide only limited support, because they either rely on a network broker service precluding open-ended searches, or they adopt a presence model which has scalability issues. To this end, we propose a fully distributed architecture employing context user-agents co-located with data-mining agents. These agents create and maintain local schemas using ranking of global context information based on context proximity. Continually evolving context information thus provides applications with current and relevant context views derived from global sensors. Furthermore, we present an evaluation model for assessing the effort required to present local applications with current and relevant contextual views. We show in a comparison with earlier work that the approach achieves the provisioning of evolving context information to applications within predictable time bounds, circumventing earlier limitations.