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Assessing Ambiguity of Context Data in Intelligent Environments: Towards a More Reliable Context Managing System

Modeling and managing correctly the user context in Smart Environments is important to achieve robust and reliable systems. When modeling reality we must take into account its ambiguous nature. Considering the uncertainty and vagueness in context data information it is possible to attain a more precise picture of the environment, thus leading to a more accurate inference process. To achieve these goals we present an ontology that models the ambiguity in intelligent environments and a data fusion and inference process that takes advantage of that extra information to provide better results. Our system can assess the certainty of the captured measurements, discarding the unreliable ones and combining the rest into a unified vision of the current user context. It also models the vagueness of the system, combining it with the uncertainty to obtain a richer inference process.

Imhotep: an approach to user and device conscious mobile applications

As the dependence on mobile devices increases, the need for supporting a wider range of users and devices becomes crucial. Elders and people with disabilities adopt new technologies reluctantly, a tendency caused by the lack of adaptation of these technologies to their needs. To address this challenge, this paper describes a framework, Imhotep, whose aim is to aid developers in the accessible application creation process, making the creation of user-centered applications easier and faster. Our framework allows to easily adapt the applications to the constraints imposed by the user capabilities (sensorial, cognitive, and physical capabilities) and device capabilities by providing a repository that will manage the compilation and deployment of applications that include a set of preprocessor directives in the source code. These directives are enhanced with concepts that are automatically adjusted to the current trends of mobile devices by using a Fuzzy Knowledge-Eliciting Reasoner. Our final goal is to increase the number of applications targeted to elders and people with disabilities providing tools that facilitate their development. The paper also describes the evaluation of both the accuracy of the fuzzy terms generated for mobile devices and the usability of the proposed platform.

Flexeo: An Architecture for Integrating Wireless Sensor Networks into the Internet of Things

Wireless sensor networks are a hot topic in Ubiquitous Computing for implementing context-awareness scenarios. The connection of sensor nodes to the Internet leads to new ways for remote monitoring of human behavior in real-time. In this paper, we introduce Flexeo: a flexible architecture for implementing monitoring solutions based on wireless sensor networks, with distributed intelligence at different layers. In this way, sensor-populated scenarios may communicate with Internet-based facilities enabling the vision of an Internet of Things.

Learning Analytics on federated remote laboratories: Tips and techniques

A remote laboratory is a software and hardware tool which enables students to use real equipment -located in an educational institution- through the Internet. This way, students can experiment as if they were using the laboratories with their own hands. And, depending on the design, instructors can later see the results of these students. During the last decade, federation protocols to share remote laboratories have emerged. The focus of these protocols is to be make remote laboratories of one institution available in other in an automated manner, through institutional contracts. And these federation protocols usually rely on existing Remote Laboratory Management Systems (RLMS), which usually provide APIs for tracking student usage. At the same time, the interest on Learning Analytics is increasing. Learning Analytics focuses on the measurement and analysis of data about learners in their context. In the particular context of federated remote laboratories, new challenges arise: on the one hand, remote laboratories must be prepared to track insightful information from the student session so as to extract patterns, and on the other hand, the usage of a federated environment requires different degrees of anonymity. This contribution describes the new Learning Analytics dashboard of WebLab-Deusto, detailing what information can be extracted and how the usage of a RLMS simplifies the development of such tools in a federated environment.

Modeling Users, Context and Devices for Ambient Assisted Living Environments

The participation of users within AAL environments is increasing thanks to the capabilities of the current wearable devices. Furthermore, the significance of considering users preferences, context conditions and devices capabilities help smart environments to personalize services and resources for them. Being aware of different characteristics of the entities participating in these situations is vital for reaching the main goals of the corresponding systems efficiently. To collect different information from these entities, it is necessary to design several formal models which help designers to organize and give some meaning to the gathered data. In this paper, we analyze several literature solutions for modeling users, context and devices considering different approaches in the Ambient Assisted Living domain. Besides, we remark different ongoing standardization works in this area. We also discuss the used techniques, modeled characteristics and the advantages and drawbacks of each approach to finally draw several conclusions about the reviewed works.

A method for automatic generation of fuzzy membership functions for mobile device's characteristics based on Google Trends

While creating a framework for adaptive mobile interfaces for m-learning applications we found that in order to ease the use of our framework we needed to present the mobile device characteristics to non-expert users in a easy to understand manner. Using fuzzy sets to represent the characteristics of mobile devices, non-expert developers such as teachers or instructional designers can actively participate in the development or adaptation of the educational tools. To be able to automatically generate the fuzzy membership functions of the sets we needed the data of the mobile device market, regrettably this information is not publicly available. To tackle this problem we have developed a method to estimate the market share of each mobile device based on the popularity metrics recovered from Google Trends and then we use that estimated value as the input to generate the fuzzy set of each characteristic. The proposed method allows us to not only model the state of the market in different periods of time, but also to localize the results to adapt them to the mobile market of specific countries. In this paper we will describe the proposed algorithm and we will discuss the obtained results.

A middleware for the deployment of ambient intelligent spaces

The latest mobile devices are offering more multimedia features, better communication capabilities (Bluetooth, Wi-Fi, GPRS/UMTS) and are more easily programmable than ever before. So far, those devices have been used mainly for communication, entertainment, and as electronic assistants. On the other hand, Ambient Intelligence (AmI) is emerging as a new research discipline merging the fields of Ubiquitous Computing and Communications, Context Awareness and Intelligent User Interfaces. The ultimate goal of AmI is to surround our working and living environments with context-aware, cooperative and invisible devices that will assist and help us in our everyday activities. Current mobile devices, which accompany us anywhere and at anytime, are the most convenient tools to help us benefit from AmI-enhanced environments. In other words, mobile devices are the best candidates to intermediate between us and our surroundings. In consequence, this paper proposes a middleware which aims to make this vision reality following a two-fold objective: (1) to simplify the creation and deployment of physical spaces hosting smart objects and (2) to transform mobile devices into universal remote controllers of those objects.

A Distributed Reasoning Engine Ecosystem for Semantic Context-Management in Smart Environments

To be able to react adequately a smart environment must be aware of the context and its changes. Modeling the context allows applications to better understand it and to adapt to its changes. In order to do this an appropriate formal representation method is needed. Ontologies have proven themselves to be one of the best tools to do it. Semantic inference provides a powerful framework to reason over the context data. But there are some problems with this approach. The inference over semantic context information can be cumbersome when working with a large amount of data. This situation has become more common in modern smart environments where there are a lot sensors and devices available. In order to tackle this problem we have developed a mechanism to distribute the context reasoning problem into smaller parts in order to reduce the inference time. In this paper we describe a distributed peer-to-peer agent architecture of context consumers and context providers. We explain how this inference sharing process works, partitioning the context information according to the interests of the agents, location and a certainty factor. We also discuss the system architecture, analyzing the negotiation process between the agents. Finally we compare the distributed reasoning with the centralized one, analyzing in which situations is more suitable each approach.

Combining Users’ Activity Survey and Simulators to Evaluate Human Activity Recognition Systems

Evaluating human activity recognition systems usually implies following expensive and time-consuming methodologies, where experiments with humans are run with the consequent ethical and legal issues. We propose a novel evaluation methodology to overcome the enumerated problems, which is based on surveys for users and a synthetic dataset generator tool. Surveys allow capturing how different users perform activities of daily living, while the synthetic dataset generator is used to create properly labelled activity datasets modelled with the information extracted from surveys. Important aspects, such as sensor noise, varying time lapses and user erratic behaviour, can also be simulated using the tool. The proposed methodology is shown to have very important advantages that allow researchers to carry out their work more efficiently. To evaluate the approach, a synthetic dataset generated following the proposed methodology is compared to a real dataset computing the similarity between sensor occurrence frequencies. It is concluded that the similarity between both datasets is more than significant.

Modelling users, context and devices for adaptive user interface systems

Purpose – The purpose of this paper is to review the state-of-the-art in adaptive user interface systems by studying their historical development over the past 20 years. Moreover, this paper contributes with a specific model combining three main entities (users, context and devices) that have been demonstrated to be always represented in these environments. Novel concepts that should be taken into account in these systems are also presented. Design/methodology/approach – The authors first provide a review and a comparison of current user interface adaptive systems. Next, the authors detail the most significant models and the set of techniques used to, finally, propose a novel model based on the studied literature. Findings – Literature solutions for adaptive user interface systems tend to be very domain dependant. This situation restricts the possibility of sharing and exporting the information between such systems. Furthermore, the studied approaches barely highlight the dynamism of these models. Origina...