Articles AI MAGAZINEM obile applications that automatically adapt to their surrounding circumstances will lead to an enhanced user experience. Emerging mobile applications exploit a user's location to deliver personalized services. In current practice, the user's location is captured at the level of position, that is, geospatial (latitude-longitude) coordinates. However, what often matters for experience is the user's place -a location in conceptual terms, such as home, work, gym, or grocery shopping, that combines positions with the user's activities, properties of the user's environment, and the activities of people surrounding or interacting with the user.The Platys project seeks to realize the above notion of place and enable the construction of a rich variety of applications that take advantage of place to render relevant content and functionality and, thus, improve user experience. Examples include proactively (1) changing phone settings (for example, turn ringer off during a meeting and turn it back on at the end of the meeting); (2) downloading relevant information (for example, the map of an amusement park, museum, or any place the user visits); (3) annotating images or other media; (4) filtering content such as alerts, notifications, and customized ads; (5) changing the ambiance (for example, playing music); (6)
Predicting the location of a user in indoor settings in a practical and energy-efficient manner is (still) a very non-trivial task. The latest challenge in indoor localization is not to design specialized sensors but to design and implement practical data fusion methods using the already available technologies. Current state-of-the-art indoor localization techniques utilize Wi-Fi and a variety of sensors inside smart phones to predict user location. Some also require site-specific input such as indoor floor plans or the location of Wi-Fi access points. In this paper, we propose to use physical (PHY) layer information from 4G cellular network signals such as Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ) to logically predict user location. Since the cellular signals are received by the smart phones at no additional cost, our methodology is very energyefficient. We implement a prototype system in Android and evaluated it over 60 indoor locations. The prediction accuracy ranged up to 91% with an average localization error of less than 2.3 m for any combination of 4G PHY layer information. The results show promise for improvements in current indoor localization systems using cellular signals.
The Internet of Things (IoT) paradigm aims to interconnect a variety of heterogeneous Smart Objects (SO) using energy-efficient methodologies and standard communication protocols. A majority of consumer devices sold today come equipped with wireless LAN and cellular technology to connect with the world-wide network. To discover Wi-Fi hot spots, there is a need for constant scanning of Wi-Fi radio in these devices and results in significant battery drain. We present PRiSM, a practical system to automatically locate Wi-Fi hotspots while Wi-Fi radio is turned off, by using the statistical characteristics of cellular signals. Cellular signals are received at zero extra cost in mobile devices and hence PRiSM is highly energy-efficient. It is a lightweight client-side only implementation and needs no prior knowledge on floor plans or wireless infrastructure. We implement PRiSM on Android-based devices and show up to 96% of energy savings in Wi-Fi sensing operations which is equivalent to saving up to 16% of total battery capacity, together with an average prediction accuracy of up to 98%.
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