Abstract
In several monitoring applications, such as those that can be found in industrial factory floor, it may be necessary to obtain an uniform sensing coverage, providing as much as possible the same coverage degree for the entire network area. The IEEE 802.15.4 has become an important standard in wireless sensor networks. However, the use of cluster-tree topology in these networks hampers a fair and uniform coverage in the sensing area. Clusters more distant from the base station are eventually adversely affected, with messages from its sensors delayed and discarded. In this paper, we propose a framework, entitled GLHOVE, whose goal is to make a trade-off between a minimum and uniform coverage area and the energy consumption of the network. The simulation results show an increase up to 20% of the fairness metric (w.r.t. messages received by base station). © 2013 IEEE.

Abstract
Emergence of the social web has brought new powerful web applications that connect people in a global scale and allow them to reap the benefits of social life from online virtual environments. Taking into account the strengths and weaknesses of online social networks and tourism information systems, we propose an evolved version of the Toursplan information system, backed up by an online social network. This way, Toursplan can exploit social data to generate collective intelligence in the tourism and travel domains, and improve the quality of its recommendation and planning services. © 2013 ACM.

Abstract
Environmental concerns and the shortage in the fossil fuel reserves have been potentiating the growth and globalization of distributed generation. Another resource that has been increasing its importance is the demand response, which is used to change consumers' consumption profile, helping to reduce peak demand. Aiming to support small players' participation in demand response events, the Curtailment Service Provider emerged. This player works as an aggregator for demand response events. The control of small and medium players which act in smart grid and micro grid environments is enhanced with a multi-agent system with artificial intelligence techniques - the MASGriP (Multi-Agent Smart Grid Platform). Using strategic behaviours in each player, this system simulates the profile of real players by using software agents. This paper shows the importance of modeling these behaviours for studying this type of scenarios. A case study with three examples shows the differences between each player and the best behaviour in order to achieve the higher profit in each situation.

Abstract
Interactive visualization of virtual environments is an active research topic. There is a multiplicity of applications such as simulation systems, augmented and mixed reality environments, computer games, amongst others, which endlessly demand for greater levels of realism and interaction. At every stage of the process, including modeling, image synthesis, transmission and navigation, there are identifiable circumstances which may compromise the achievement of high quality solutions for the posed problems. For many of these problems, an effective use of optimization tools can play a major role in order to achieve solutions with better quality. Within this context, an innovative optimization architecture is presented regarding to two major principles. The first principle comprises the possibility to integrate, with reduced effort, the optimization tools with existent applications and systems. Thus, we propose an agent-based framework where the optimization application may operate as an independent process in respect to the visualization application where communication is achieved by means of a specifically developed high-level message based protocol. The second principle establishes on the utilization of a class of intelligent optimization methods, known as metaheuristics, which major distinguishing quality is their great level of problem-independence, thus, enabling a wider application. The paper describes conducted experiments and presents results that demonstrate the utility and efficacy of the proposed framework.

Abstract
Logic Programming languages, such as Prolog, provide an excellent framework for the parallel execution of logic programs. In particular, the inherent non-determinism in the way logic programs are structured makes Prolog very attractive for the exploitation of implicit parallelism. One of the most noticeable sources of implicit parallelism in Prolog programs is or-parallelism. Or-parallelism arises from the simultaneous evaluation of a subgoal call against the clauses that match that call. Arguably, the most successful model for or-parallelism is environment copying, that has been efficiently used in the implementation of or-parallel Prolog systems both on shared memory and distributed memory architectures. Nowadays, multicores and clusters of multicores are becoming the norm and, although, many parallel Prolog systems have been developed in the past, to the best of our knowledge, none of them was specially designed to explore the combination of shared with distributed memory architectures. Motivated by our past experience, in designing and developing parallel Prolog systems based on environment copying, we propose a novel computational model to efficiently exploit implicit parallelism from large scale real-world applications specialized for the novel architectures based on clusters of multicores. © João Santos and Ricardo Rocha.

Abstract
This paper addresses the bidding problem faced by an electric vehicles (EV) aggregation agent when participating in the day-ahead electrical energy market. Two alternative optimization approaches, global and divided, with the same goal (i.e. solve the same problem) are described. The difference is on how information about EV is modeled. The global approach uses aggregated values of the EV variables and the optimization model determines the bids exclusively based on total values. The divided approach uses individual information from each EV. In both approaches, statistical forecasting methods are formulated for the EV variables. After the day-ahead bidding, a second phase (named operational management) is required for mitigating the deviation between accepted bids and consumed electrical energy for EV charging. A sequential linear optimization problem is formulated for minimizing the deviation costs. This chain of algorithms provides to the EV aggregation agent a pathway to move to the smart-grid paradigm where load dispatch is a possibility.