Many context-aware smartphone applications depend on specific conditions for gathering data, e.g., specific phone locations or orientations. As a result, the significant overhead of keeping all this information in mind is imposed on their users. Besides averting the interest of potential application users, these requirements defeat one of the main purposes of these mobile data collection, namely simplifying life through mobile sensing applications. This is not a problem that solely affects the users, but the developers of the applications alike. As even the most diligent users often do not manage to follow the strict data collection guidelines at all times, errors in the collected data may ultimately lead to the provision of wrong services and thus to degraded application quality. In this paper, we thus present a solution to determine the location of a phone in order to support context-aware applications. It offers the possibility to detect the position of the phone with an accuracy of 97 %, as well as being able to correlate it with the type of the location of the user. Our system can be used to improve existing mobile sensing applications by facilitating various services that depend on the phone location, e.g., seamlessly adapting the ringtone volume or setting a phone's flight mode

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As a consequence of rising energy prices, manifold solutions to create user awareness for the unnecessary operation of electric appliances have emerged, e.g., real-time consumption displays or timer-based switchable wall outlets. A common attribute of these solutions is the need to buy and install additional hardware, although their acquisition costs often diminish the attainable savings. Furthermore these solutions only permit to retrieve accumulated figures of the energy consumption. Especially in households or office spaces with multiple persons, however, attributing electricity consumption to individuals provides enormous potential to determine possible savings. We therefore propose a system that allows to identify the energy demand incurred by a user's action based on audio recordings using smartphones. More precisely, we capture the user's ambient sounds and applying suitable filtering steps in order to determine the user's current activity. Our results indicate that our system is capable of detecting 16 typical household activities at an accuracy of 92%. By annotating the detectable household activities with information about typical energy consumptions, extracted from 950 real-world power consumption traces, a good estimate of the energy intensity of the users' lifestyles can be made. Our novel personalized energy monitoring system shows people their personal energy consumption, while maintaining their user comfort and relinquishing the need for additional hardware.

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Every day, we carry our mobile phone in our pocket or bag. When arriving at work or to a meeting, we may display it on the table. Most of the time, we however do not change the ringtone volume based on the new phone location. This may result in embarrassing situations when the volume is too loud or missed calls and alarms when it is too low. In order to prevent such situations, we propose a non-intrusive opportunistic approach to determine the phone location and later adapt the ringtone accordingly. In our approach, we analyze the attenuation of the played ringtones to determine the nature of the surrounding elements. We evaluate our approach based on a prototypical implementation using different mobile phones and show that we are able to recognize the sole phone location with a precision of more than 94%. In a second step, we consider different surrounding environments and reach a precision of 89% for the phone position and 86% for the combination of the phone position