With the growing production and access of resources managed by sensor-rich ubiquitous devices, such as photos, videos, and services, critical issues arise from access control point of view. Sensor-rich devices offer users new opportunities for creating, accessing, and sharing resources from anywhere and at anytime, interacting dynamically with other surrounding devices, users, and the environment. In this scenario, resources could be automatically annotated with information characterizing situations (context) to facilitate retrieval, organization, and enrichment of their contents. Moreover, resources may be available by users only on a specific situation (e.g., location and season), such as surveys asking visitors about touristic attractions while visiting them. However, existing ubiquitous applications do not exploit contextual and social annotation as central concept for defining access control policies to protect resources. Therefore, this paper proposes a context-aware rule-based access control model for protecting ubiquitous resources, presenting a prototype that implements this model, a case study based on an ubiquitous survey application, as well as performance evaluation results that demonstrate the effectiveness and scalability of the proposed solution.

Context-awareness has been successfully included in the mobile device applications due mainly to the presence of numerous sensors and the access to several communication networks. Therefore, this paper presents a context-aware framework based on geolocation for a health care governance system. While enabling remote health diagnosis of a patient using his/her own mobile device, the proposed system enriches the patient's health data with geolocation before sending it to the Lariisa framework, an intelligent platform to support decision-making in public health governance. In order to collect information about a patient's health status, the proposed application uses mobile devices equipped with medical sensors. While remotely registering a patient's health diagnosis using a mobile device, the system allows the creation of an enriched description of the user context (e.g. weather, location, and date). The main contributions related to our framework include patient health diagnosis provided remotely, support for decision-making health systems, and context information for context-aware health systems.

In recent years, there has been a change in society behavior regarding the way people interact with each other. Particularly, there is a tendency for people to change from real to virtual communities. Consequently, social opportunities are frequently missed because users have to describe manually their daily routines on virtual communities. Nevertheless, mobile social applications emerge to improve social connectivity in real communities by capturing context information of people, points of interest, and places taking into account user trajectories. In this paper, we present a Trajectory Correlation Algorithm based on Users' Daily Routines. The key idea is to provide a solution to capture daily routines in order to find related information of users and, consequently, increase social interactions in real communities. We introduce an algorithm to execute the trajectory correlation process, taking into account daily trajectories and points of interest of users. To validate our proposal, we implemented and tested a mobile social application for tracking daily routines. Besides that, we developed a plug-in on a virtual community platform to execute an optimized trajectory correlation algorithm, which is based on Minimum Bounding Rectangles (MBRs) and Hausdorff Distance. The results show that our proposal is efficient to increase social interactions in real communities by using a mobile social application and a well-known social network platform.

Pervasive Environments offer new opportunities for users to dynamically access resources and services based on information characterizing their situation (context), generally assuming that this information is correct and trustworthy. In this scenario, the Quality of Context information (QoC) plays an important role for improving context-based adaptation processes and for ensuring the correct behavior of context-aware applications and services. Some research attempts have been done for modeling and measuring the quality of raw context information sensed from the environment. However, so far no attention has been paid to the quality evaluation of derived and inferred context information. This paper describes an approach for modeling and measuring quality of raw, inferred, and derived context information based on the following points of view: privacy, security, precision, completeness, and resolution. We propose a context management framework that support QoC in their various layers, protecting and providing QoC-enriched context information of users to context-aware applications and services.

This paper proposes a governance decision-making framework for public health care systems. It encompasses and integrates data about family homes in a new intelligent health care information system. In order to support end-user interactions, the framework has been built on the GINGA middleware developed for the Brazilian Digital TV, whose full access will be country-wide in 2015. Based on five governance fields, namely knowledge, normative, clinical-epidemiological, administrative, and shared management, the framework relies on an Optical-WiMAX communication infrastructure (Brazilian Digital Belt), which will reach 82% of urban population in the Ceará State. In addition, we present a case study showing how the framework could be used for improving health care governance decisions.

In the last few years, a number of spatial and spatio-temporal access control models have been developed especially in the framework of pervasive computing and location-aware applications. Yet, how useful and effective those models are in real applications is still to be proved. The goal of this panel is to discuss access control requirements in mobile applications, trying to link research to real business problematic.

In the recent years, photo context metadata (e.g.,date, GPS coordinates) have been proved to be useful in the management of personal photos. However, these metadata are still poorly considered in photo retrieving systems. In order to overcome this limitation, we propose an approach to incorporate contextual metadata in a keyword-based photo retrieval process.We use metadata about the photo shot context (address location, nearby objects, season, light status...) to generate a bag of words for indexing each photo. We extend the Vector Space Model in order to transform these shot context words into document-vector terms. In addition, spatial reasoning and geographical ontologies are used to infer new indexing terms. This facilitates the query-document matching process and also allows performing semantic comparison between the query terms and photo annotations.

This paper presents an approach for incorporating contextual metadata in a keyword-based photo retrieval process. We use our mobile annotation system PhotoMap in order to create metadata describing the photo shoot context (e.g., street address, nearby objects, season, lighting, nearby people...). These metadata are then used to generate a set of stamped words for indexing each photo. We adapt the Vector Space Model (VSM) in order to transform these shoot context words into document-vector terms. Furthermore, spatial reasoning is used for inferring new potential indexing terms. We define methods for weighting those terms and for handling a query matching. We also detail retrieval experiments carried out by using PhotoMap and Flickr geotagged photos. We illustrate the advantages of using Wikipedia georeferenced objects for indexing photos.

In this paper, we present a framework for context-based awareness support designed for Web-based groupware systems. Nowadays, such systems can be accessed through Web-enabled mobile devices, such as PDAs and cellular phones, which allow their users to perform simple tasks, such as contacting colleagues or reading messages. However, these devices present some constraints related to their intrinsic characteristics (reduced display size, limited memory capacity). These constraints make more acute the need for information filtering. We present a framework for awareness support which is able to adapt the awareness information to the user's context, in order to reduce the risk of information overload. This framework, called BW-M, selects among the available awareness information that which matches the user's, represented through pre-defined profiles, according to her/his current context.

The progress and the continuous evolution of computer capacities, as well as the emergence of the X3D standard have recently boosted the 3D domain. Associating some semantics with 3D contents becomes a major issue specially for reusing such contents or pieces of content after having extracted them from existing 3D scenes. In this paper, we address this issue by proposing a generic semantic annotation model for 3D, called 3DSEAM (3D semantics annotation model). 3DSEAM aims at indexing 3D contents considering visual, geometric and semantic aspects. 3DSEAM is instantiated using MPEG 7 that we have extended with 3D specific locators. These locators link some visual, geometric and semantic features to the corresponding X3D fragments. These features can then be used for indexing and querying.

The evolving desktop computer capacities and the emergence of the X3D standard offer a new boost to 3D domain. Giving sense to 3D content becomes a major issue specially for reusing such a content extracted from existing 3D scenes. In this paper, we address this issue by proposing a generic semantic annotation model for 3D called 3DSEAM (3D SEmantics Annotation Model). 3DSEAM aims at indexing 3D content considering visual, geometric and semantic aspects. 3DSEAM is instantiated using MPEG-7 extended with 3D specific locators. These locators link the visual, geometric and semantic features of a 3D content to the corresponding X3D fragment.