Semantic Brokering of Multimedia Contents for Smart Delivery of Ubiquitous Services in Pervasive Environments

With the proliferation of modern mobile devices having the capability to interact each other and with the environment in a transparent manner, there is an increase in the development of those applications that are specifically designed for pervasive and ubiquitous environments. Those applications are able to provide a service of interest for the user that depends on context information, such as the user's position, his preferences, the capability of the device and its available resources. Services have to respond in a rational way in many different situations choosing the actions with the best expected result by the user, so making environment not only more connected and efficient, but smarter. Here we present a semantic framework that provides the technology for the development of intelligent, context aware services and their delivery in pervasive and ubiquitous environments

Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

Economy and Divorces: Their Impact Over Time on the Self-Employment Rates in Spain

The paper used time-series data and examined the effect of economic and social variables on the male and female self-employment rates in Spain. We also employed cointegration analysis (with and without) structural breaks. We thus find strong evidence that long run relationships exist among the variables. More precisely, we find that the unemployment rates and the ratio of self-employment to employees’ earnings have a positive effect on self-employment, whereas, economic development and divorce rates have a negative effect. Importantly, we find that the economic variables have equal or stronger long run impact on females than males, with both groups reacting to changes in family circumstances. Finally, we show that the short run family circumstances are better predictors of self-employment choices rather than economic factors, with self-employment being a means of adjustment to new personal circumstances and economic needs

Multiobjective optimization for brokering of multicloud service composition

The choice of cloud providers whose offers best fit the requirements of a particular application is a complex issue due to the heterogeneity of the services in terms of resources, costs, technology, and service levels that providers ensure. This article investigates the effectiveness of multiobjective genetic algorithms to resolve a multicloud brokering problem. Experimental results provide clear evidence about how such a solution improves the choice made manually by users returning in real time optimal alternatives. It also investigates how the optimality depends on different genetic algorithms and parameters, problem type, and time constraints

A methodology for deployment of IoT application in fog

The foreseen increase of IoT connected to the Internet is worrying the ICT community because of its impact on network Infrastructure when the number of requesters become larger and larger. Moreover also reliability of network connection and real-time constraints can affect the effectiveness of the Cloud Computing paradigm for developing IoT solutions. The necessity of an intermediate layer in the whole IoT architecture that works as a middle ground between the local physical memories and Cloud is proposed by the Fog paradigm. In this paper we define and use a methodology that supports the developer to address the Fog Service Placement Problem, which consists of finding the optimal mapping between IoT applications and computational resources. We exploited and extended a Fog Application model from the related work to apply the proposed methodology in order to investigate the optimal deployment of IoT application. The case study is an IoT application in the Smart Energy domain. In particular, we extended a software platform, which was developed, and released open source by the CoSSMic European project, with advanced functionalities. The new functionalities provide capabilities for automatic learning of energy profiles and lighten the platform utilization by users, but they introduce new requirements, also in terms of computational resources. Experimental results are presented to demonstrate the usage and the effectiveness of the proposed methodology at deployment stage

Multi-objective Decision Support for Brokering of Cloud SLA

The selection of Cloud providers, whose offers best fit the requirements of a particular application, is a complex issue due to the heterogeneity of the Cloud services, resources, technology and service levels offered by the several providers. In this paper, we illustrate a model for the resolution of a problem of choice between alternatives, when numerous and often contradictory points of view must be taken simultaneously in consideration. We describe its implementation inside Cloud Agency to broker the proposals that best fit user’s needs

A distributed agent-based decision support for cloud brokering

In goal oriented problems, decision-making is a crucial aspect aiming at enhancing the ability to make decisions by full autonomous, or supervised software agents. Agents usually resolve a huge number of evaluations and decisions without the user intervention. Just the remaining uncertainty should be left to the user's attention. In this paper we present a complete constrained multi-objectives optimization problem, modeled as an agent based decision support systems, that helps to choose the Cloud proposals that best satisfy the needs of the user, among the ones offered by known vendors. In particular we focus on a distributed solution that exploits the multi-agents programming model and the Cloud elasticity to comply with computational requirements of delivering such brokering at Cloud service level