The processing capabilities of mobile devices coupled with portable and wearable sensors provide the basis for new context-aware services and applications tailored to the user environment and its daily activities. In this article, we describe the approach developed within the UPCASE project, which makes use of sensors available in the mobile device as well as sensors externally connected via Bluetooth to provide user contexts. We describe the system architecture from sensor data acquisition to feature extraction, context inference and the publication of context information in web-centered servers that support well-known social networking services. In the current prototype, context inference is based on decision trees to learn and to identify contexts dynamically at run-time, but the middleware allows the integration of different inference engines if necessary. Experimental results in a real-world setting suggest that the proposed solution is a promising approach to provide user context to local mobile applications as well as to network-level applications such as social networking services.
Abstract. The growing processing capabilities of mobile devices coupled with portable and wearable sensors have enabled the development of context-aware services tailored to the user environment and its daily activities. The problem of determining the user context at each particular point in time is one of the main challenges in this area. In this paper, we describe the approach pursued in the UPCASE project, which makes use of sensors available in the mobile device as well as sensors externally connected via Bluetooth. We describe the system architecture from raw data acquisition to feature extraction and context inference. As a proof of concept, the inference of contexts is based on a decision tree to learn and identify contexts automatically and dynamically at runtime. Preliminary results suggest that this is a promising approach for context inference in several application scenarios.
Today we use smartphones and personal computers everywhere to communicate in ways made possible by the ubiquity of the Internet: we exchange emails and instant messages, make voice and video calls and participate in social media. WebRTC furthers the possibilities of audio and video communications and true peer-to-peer communications as it brings these capabilities also to an universal platform: the browser. But communication is more than just audio or video sharing, communication is about providing features capable of filling the distance void. Today we have sensors that can provide data to enrich communication by enabling the usage of information about the context of the remote peer, e.g.: providing temperature information, speed or acceleration. Mobile devices, being equipped with a range of sensors, such as accelerometers, gyroscopes and magnetometers are prime terminals to enrich communications with context data, but all types of devices can make use of external sensing devices or even reach out to sensor networks. As the browser becomes a fundamental platform, sensor data is also relevant to non WebRTC web applications.In this paper, we present an API proposal to enable web applications to access sensor data and an extension to WebRTC to enable peer-to-peer exchange of sensor data, bringing nearby sensor streams to web applications and multimedia communication over the web. Telemedicine, Meteorology or Seismology are prime examples of the applicability of this technology, that also enables new types of context-aware and context-based applications and communications.
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