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In the participatory sensing model, humans may serve as opportunistic sensors and flexible actuators while also consuming sensing services. Integrating humans into sensing systems has the potential to increase scale and reduce costs. However, contemporary participatory sensing software provides poor consideration of user dynamism, which includes: mobility across networks, mobility across devices and...
The ubiquitous nature of mobile devices offering high computing capabilities has attracted a large number of users. The sensors available on these devices provide valuable data about user contexts, which when combined with sensor data from other mobile devices can reveal interesting environmental contexts, which can then be used for applications like real-time traffic monitoring, noise mapping, crowdmapping,...
The modern mobile ubiquitous computing environment, with reasonable connectivity, processing power and sensing capabilities on portable devices, present applications and services with the opportunity to be truly context-aware. But building context-aware applications has large development overheads due to complexities of sensing, aggregating and inferencing context information. To reduce the developers'...
The vertiginous penetration and advance of mobile devices in all levels of our society leads towards new technological challenges. This paper is focused on the application of mobile devices in the learning environment, since the current mobile learning development is finding several problems and lacks, such as use of the new features (e.g. location and motion sensors) and the desigh without having...
We present a novel context acquisition and management middleware for mobile phones based on Qt (C++ based cross-plattform API) for S60 called mSense. In our middleware, sensor nodes encapsulate platform level APIs for seamlessly accessing hardware sensors, simulated sensors or web services. Aggregators (channels) combine information from other nodes to generate new knowledge. For example, GPS, accelerometer...
This paper addresses the issue of the creation of context-aware applications inside the Mobile-Learning approach. It tries to clarify aspects like the most suitable environments of application; how new features of mobile devices, such as GPS (Global Positioning System) receivers, motion sensors, etc., must be taken into account. It also addresses why most of the m-Learning applications do not make...
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