Resultados de búsqueda para mobile computing

During the last decade, the mobile application market has grown steadily thanksto the massive use of smartphones and the emergence of cloud computing foroffloading computation tasks and improving the quality of experience. Nevertheless, the increasing amount of information exchanged, the stringent requirements of many IoT applications, and the need for these applications to adapt their behaviour in real time to the user’s context set these architectural assumptions a challenge. Paradigms such as mobile, mist, and edge computing have recently been proposed to exploit the computational and storage capabilities of current smartphones and IoT devices in order to onload some tasks onto them, reducing the overhead on both the cloud and the network. Currently, the application of these paradigms requires skilled developers as there lack standards and tools facilitating their development.This communication introduces Human Microservices as a framework facilitating the deployment of APIs on companion devices in order to provide personal and updated information that can be consumed by other entities. The framework improves the integration of humans in the IoT loop and facilitates the deployment of computation units in devices closer to end users. The proposed framework is based on existing standards in order to improve software quality and shorten the learning curve.

El Instituto Internacional de Investigacion e Innovacion del Envejecimiento es un proyecto transfronterizo y multidisciplinar centrado en la mejora de la calidad de vida de los ancianos mediante el uso de la tecnologia. En este proyecto colaboran la Universidad de Evora, el Instituto Politecnico de Porto Alegre, el Instituto Politecnico de Beja, la Administracion Regional de Salud de Alentejo y la Universidad de Extremadura. Los objetivos del proyecto se centran en comprender los aspectos biomedicos, funcionales y psicologicos del envejecimiento; generar nuevos modelos y procesos de cuidado a ancianos y desarrollar soluciones tecnologicas que contribuyan a la salud y calidad de vida de los ancianos y a la sostenibilidad de los servicios.

Traditional recommendation systems offer relevant items (e.g., books, movies, music, etc.) to users, but they are not designed for mobile environments. In those environments, the context (e.g., the location, the time, the weather, the presence of other people, etc.) and the movements of the users may be important factors to obtain relevant and helpful recommendations. The emergence of context-aware recommendation systems has prompted the growth of recommendation algorithms that incorporate context information. However, most existing research in this field considers only static context information, despite the fact that exploiting dynamic context information would be very helpful in mobile computing scenarios. Moreover, the design and implementation of generic frameworks to support an easy development of context-aware recommendation systems has been relatively unexplored. In this paper, we present our ongoing work to develop a context-aware recommendation framework for distributed and mobile environments, which will allow suggesting relevant items to mobile users.

In the next years, vehicular ad hoc networks (VANETs) are expected to become a reality and a great number of interesting applications for drivers and passengers will be developed (related to safety, comfort, and entertainment). In all these applications, the acquisition, management and an efficient and effective exchange of data will be key issues. Therefore, significant data management challenges arise in this context. We argue that mobile agent technology could play an important role as a middleware for the development of applications for vehicular networks, as they naturally support disconnected operations and distributed data management. Overall, a development of solutions based on agents that autonomously take decisions may be promising. A significant problem arises when we need to evaluate a data management approach for vehicular networks. Deploying it in the real-world in order to evaluate the proposal with real cars is impractical and very expensive, and so field tests are limited to very small-scale and controlled scenarios, mainly as a proof of concept. Instead, simulators are usually used for experimental evaluation. However, existing simulators for vehicular networks do not directly support testing data management techniques based on the use of mobile agents. In this paper, we present a simulator that offers interesting functionalities for that context. The simulator has been developed in a quite generic and extensible way, in order to facilitate its use in a variety of scenarios to test different data management approaches.

The massive involvement of human in Cyber-Physical Systems is to a large extend managed through their smart devices. So far, these devices have been used as simple set of sensors capable of capturing the users context and uploading it to a central server. However, this architecture leads to a high consumption of the device’s resources. Consumption that is dramatically increased when similar data are used in several CPS. Nevertheless, smart devices even increasing storage and computing capacities allow them to take a more active role in these systems. This paper presents an architecture where smart devices are treated as the bridge between the physical world and the cyber space. In this architecture, smart devices store and infer the user contextual and sociological information, reacting to the state of the user or collaborating with other computational infrastructures. This architecture enables the development of human-centric CPS with clear social orientation.

In recent years smartphone users have increased the number of cloud services and platforms used from them. These platforms and services are usually used, by users, to interact with others people and, by
the mobile telephony firms, to create a sociological profile of the people and, thus, achieving a more adapted advertising. However, the information uploaded to these platforms is usually very similar. Uploading it to every platform entails an irrational consumption of the device resources.
But, if it is not the same, the sociological profiles created could be inconsistent. The capabilities of current smartphones enable them to keep all the owner’s information and to provide services for accessing it. To achieve such paradigm shift new tools and platforms are needed. This paper reports a proof of concept of a mobile application that creates and stores the sociological profiles of their users, allowing them to send messages based on those profiles. The use of this new paradigm reduces the consumption of the smartphone resources and facilitates the creation of comprehensive sociological profiles.

Due to the high availability of data, users are frequently overloaded with a huge amount of alternatives when they need to choose a particular item. This has motivated an increased interest in research on recommendation systems, which filter the options and provide users with suggestions about specific elements (e.g., movies, restaurants, hotels, news, etc.) that are estimated to be potentially relevant for the user. Recommendation systems are still an active area of research, and particularly in the last years the concept of context-aware recommendation systems has started to be popular, due to the interest of considering the context of the user in the recommendation process. In this paper, we describe our work-in-progress concerning pull-based recommendations (i.e., recommendations about certain types of items that are explicitly requested by the user). In particular, we focus on the problem of detecting the type of item the user is interested in. Due to its popularity, we consider a keyword-based user interface: the user types a few keywords and the system must determine what the user is searching for. Whereas there is extensive work in the field of keyword-based search, which is still a very active research area, keyword searching has not been applied so far in most recommendation contexts.