André C. Santos

Providing user context for mobile and social networking applications

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 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.

Context Inference for Mobile Applications in the UPCASE Project

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.

Towards Automatic Conflict Detection in Home and Building Automation Systems

Home and Building Automation Systems (HBAS) are becoming of widespread adoption. When distinct users interact with such systems, their intentions are likely to be different, often resulting in conflicting situations, which the systems ought to recognize and resolve automatically. This work aims at investigating conflict in HBAS and creating a solution to detect and resolve them. Herein, we review the literature concerning conflict detection and resolution, and propose a formal framework based on constraint solving that enables detecting and solving conflict situations automatically.

Conflict detection and resolution in home and building automation systems: a literature review

The evolution and increasing commoditization of home and building automation systems (HBAS) is contributing to their widespread adoption. However, an effort must still be made to render them usable, intelligent, highly adaptive and able to fulfill users’ needs. When distinct users interact with such a system, their intentions are likely to be different, often resulting in conflicting situations, which the system ought to recognize and, if possible, resolve automatically. However, conflict detection and resolution in HBAS are not yet fully understood. This work aims at investigating conflict in Ambient Intelligence systems, namely those supported by HBAS. Our main contribution is a systematization and review of existing literature concerning conflict detection and resolution in these systems.

The current feasibility of gesture recognition for a smartphone using J2ME

The need to improve communication between humans and computers has been instrumental in defining new communication models, and accordingly, new ways of interacting with machines. The use of gestures as a means of communication has been a challenging task. The latest generation of smartphones boasts powerful processors and built-in video cameras, making them capable of executing complex and computationally demanding applications. Thus, the integration of gesture recognition systems in smartphone applications might be a close reality. In this paper, we present studies of a gesture recognition prototype system for smartphones. We use a number of tasks typically employed in gesture recognition systems which permit to assess the current feasibility of smartphones to implement this kind of systems. Based on both the execution time and classification performance, we conclude that the latest smartphone generation is capable of executing complex image processing applications, with the most penalizing factor being camera performance regarding capture rates with the current J2ME support.

An Analysis of Navigation Algorithms for Smartphones Using J2ME

Embedded systems are considered one of the most potential areas for future innovations. Two embedded fields that will most certainly take a primary role in future innovations are mobile robotics and mobile computing. Mobile robots and smartphones are growing in number and functionalities, becoming a presence in our daily life. In this paper, we study the current feasibility of a smartphone to execute navigation algorithms. As a test case, we use a smartphone to control an autonomous mobile robot. We tested three navigation problems: Mapping, Localization and Path Planning. For each of these problems, an algorithm has been chosen, developed in J2ME, and tested on the field. Results show the current mobile Java capacity for executing computationally demanding algorithms and reveal the real possibility of using smartphones for autonomous navigation.

The Feasibility of Navigation Algorithms on Smartphones using J2ME

Embedded systems are considered one of the areas with more potential for future innovations. Two embedded fields that will most certainly take a primary role in future innovations are mobile robotics and mobile computing. Mobile robots and smartphones are growing in number and functionalities, becoming a presence in our daily life. In this paper, we study the current feasibility of a smartphone to execute navigation algorithms and provide autonomous control, e.g., for a mobile robot. We tested four navigation problems: Mapping, Localization, Simultaneous Localization and Mapping, and Path Planning. We selected representative algorithms for the navigation problems, developed them in J2ME, and performed tests on the field. Results show the current mobile Java capacity for executing computationally demanding algorithms and reveal the real possibility of using smartphones for autonomous navigation.

Specifying Adaptations through a DSL with an Application to Mobile Robot Navigation

Developing applications for resource-constrained embedded systems is a challenging task specially when applications must adapt to changes in their operating conditions or environment. To ensure an appropriate response at all times, it is highly desirable to develop applications that can dynamically adapt their behavior at run-time. In this paper we introduce an architecture that allows the specification of adaptable behavior through an external, high-level and platformindependent domain-specific language (DSL). The DSL is used here to define adaptation rules that change the run-time behavior of the application depending on various operational factors, such as time constraints. We illustrate the use of the DSL in an application to mobile robot navigation using smartphones, where experimental results highlight the benefits of specifying the adaptable behavior in a flexible and external way to the main application logic.

A Domain-Specific Language for the Specification of Adaptable Context Inference

Context-aware mobile applications can benefit from context inference adaptation based on run-time operating conditions, such as battery life or sensor availability. Developing applications with such adaptable behavior, however, is notoriously cumbersome, as developers need to deal with low-level system interfacing and programming issues. In this paper we describe a domain-specific language (DSL) and a middleware infrastructure to support the specification, deployment and maintenance of run-time adaptable context inference processes. We illustrate the benefits of our approach via a case study, highlighting the new abstractions that facilitate the specification of adaptable behavior using different algorithms and the corresponding varying parameter settings, with a specific goal of minimizing the energy while maintaing acceptable end-application performance and accuracy.

Resource Description Language: A Unified Description Language for Network Embedded Resources

As machine-to-machine networks become larger and more pervasive, manual configuration and discovery of resources will become intractable. It is in this context that we propose the RDL, a Resource Description Language that represents a uniform way of describing embedded resources, allowing them to be shared and enabling a new class of resource-aware applications. The RDL can describe a wide range of resources, characterizing individual nodes or entire networks. It can contribute to overcome performance issues in dense networks or mobility-driven problems in highly dynamic machine-to-machine topologies by providing the means for self-adaptability and manageability, as well as opportunistic resource sharing in context-aware embedded applications. The main goal for the RDL is to define a reusable and extensible resource description specification, which can only be reached if the resources are described in a standardized format. To illustrate the feasibility of our approach, we have also developed a Java implementation of the RDL framework, as well as a TinyOS implementation targeting resource constrained platforms. Furthermore, we have developed Modulus, a modular middleware for the development of resource-aware distributed applications.