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Papers accepted at MONAMI

posted Jul 22, 2015, 7:35 AM by M Seufert
The papers "Energy Considerations for WiFi Offloading of Video Streaming" by Valentin Burger, Fabian Kaup, Michael Seufert, Matthias Wichtlhuber, David Hausheer, Phuoc Tran-Gia and "Analysis of Group-based Communication in WhatsApp" by Michael Seufert, Anika Schwind, Tobias Hoßfeld, Phuoc Tran-Gia were accepted for publication at the 7th EAI International Conference on Mobile Networks and Management (MONAMI) from September 16-18, 2015 in Santander, Spain.

Energy Considerations for WiFi Offloading of Video Streaming
The load on cellular networks is constantly increasing. Especially video streaming applications, whose demands and requirements keep growing, put high loads on cellular networks. A solution to mitigate the cellular load in urban environments is offloading mobile connections to WiFi access points, which is followed by many providers recently. Because of the large number of mobile users and devices there is also a high potential to save energy by WiFi offloading. In this work, we develop a model to assess the energy consumption of mobile devices during video sessions. We evaluate the potential of WiFi offloading in an urban environment and the implications of offloading connections on energy consumption of mobile devices. Our results show that, although WiFi is more energy efficient than 3G and 4G for equal data rates, the energy consumption increases with the amount of connections offloaded to WiFi, due to poor data rates obtained for WiFi in the streets. This suggests further deployment of WiFi access points or WiFi sharing incentives to increase data rates for WiFi and energy efficiency of mobile access.

Analysis of Group-based Communication in WhatsApp
This work investigates group-based communication in WhatsApp based on a survey and the analysis of messaging logs. The characteristics of WhatsApp group chats in terms of usage and topics are outlined. We present a classification based on the topic of the group and classify anonymized messaging logs based on message statistics. Finally, we model WhatsApp group communication with a semi-Markov process, which can be used to generate network traffic similar to real messaging logs.