The optimization of logistics in large building complexes with many resources, such as hospitals, require realistic facility management and planning. Current planning practices rely foremost on manual observations or coarse unverified assumptions and therefore do not properly scale or provide realistic data to inform facility planning. In this paper, we propose analysis methods to extract knowledge from large sets of network collected WiFi traces to better inform facility management and planning in large building complexes. The analysis methods, which build on a rich set of temporal and spatial features, include methods for noise removal, e.g., labeling of beyond building-perimeter devices, and methods for quantification of area densities and flows, e.g., building enter and exit events, and for classifying the behavior of people, e.g., into user roles such as visitor, hospitalized or employee. Spatio-temporal visualization tools built on top of these methods enable planners to inspect and explore extracted information to inform facility-planning activities. To evaluate the methods, we present results for a large hospital complex covering more than 10 hectares. The evaluation is based on WiFi traces collected in the hospital's WiFi infrastructure over two weeks observing around 18000 different devices recording more than a billion individual WiFi measurements. For the presented analysis methods we present quantitative performance results, e.g., demonstrating over 95% accuracy for correct noise removal of beyond building perimeter devices. We furthermore present detailed statistics from our analysis regarding people's presence, movement and roles, and example types of visualizations that both highlight their potential as inspection tools for planners and provide interesting insights into the test-bed hospital.

This paper introduces an LBS multisensor system that acquires data from different sensors available in commodity smart phones to provide accurate location estimations. Our approach is based on the use of visual structure from motion techniques to run off-line 3D reconstructions of the environment from the correspondences among the SIFT descriptors of the training images. We present several solutions to reduce the deployment cost, in terms of time, and to minimize the interference degree within the environment, but also pursuing a good balance between accuracy and performance. To determine the position of the smartphones, we first obtain a coarse-grained estimation based on WiFi signals, digital compasses, and built-in accelerometers, making use of fingerprinting methods, probabilistic techniques, and motion estimators. Then, using images captured by the camera, we perform a matching process to determine correspondences between 2D pixels and model 3D points, but only analyzing a subset of the 3D model delimited by the coarse-grained estimation. We implement a resection process providing high localization accuracy when the camera has been previously calibrated, that is, we know intrinsic parameters like focal length, but it is also accurate if an auto-calibration process is required. Furthermore, our experimental tests show promising results, since we are able to provide high accuracy with an average error down to 15 cm in less than 0.5 seconds of response time, making this proposal suitable for applications combining location-services and augmented reality.

This paper presents an innovative solution for the deployment and management of vehicular services based on IP Multimedia Subsystem (IMS). We justify our proposal by analysing the advantages of IMS applied in vehicular scenarios. For this purpose we have designed and experimentally developed a platform that consists of an IMS Core, advanced enablers and two IMS Clients. We demonstrate the potential of this solution by evaluating the performance of two vehicular services, explicitly tested in real conditions using our platform. We provide qualitative analysis and quantitative results, including performance metrics of the services using our solution through different access technologies.