University Networking in Developing Countries based on C4D-Learning

In recent years, the establishment of cooperation networks between universities is one of the most important trends in higher education all over the world. Well recognized local and international university networks have been implemented in most educational institutions. It is common to find associations of various prestigious universities collaborating in a high-­‐technology research project including a very specialized teaching as well. This is the most common cooperation networks among higher education institutions in developed countries. An increasingly common type of networking between developed and developing universities is related to cooperation for development. This is the case of many universities in Africa that are needed for external help in order to improve its capabilities. Numerous memorandums of understanding regarding first world institutions that collaborate with universities in developing countries describe contributions of eventual visiting professors, teaching material and courses. But probably there exist another type of more important, but less explored association, such as networking among developing universities. The new goal, in this case, is not only the excellence but also the mutual development.

Cooperative off-policy prediction of markov decision processes in adaptive networks

We apply diffusion strategies to propose a cooperative reinforcement learning algorithm, in which agents in a network communicate with their neighbors to improve predictions about their environment. The algorithm is suitable to learn off-policy even in large state spaces. We provide a mean-square-error performance analysis under constant step-sizes. The gain of cooperation in the form of more stability and less bias and variance in the prediction error, is illustrated in the context of a classical model. We show that the improvement in performance is especially significant when the behavior policy of the agents is different from the target policy under evaluation.

Self-adaptive strategy based on fuzzy control systems for improving performance in wireless sensors networks

The solutions to cope with new challenges that societies have to face nowadays involve providing smarter daily systems. To achieve this, technology has to evolve and leverage physical systems automatic interactions, with less human intervention. Technological paradigms like Internet of Things (IoT) and Cyber-Physical Systems (CPS) are providing reference models, architectures, approaches and tools that are to support cross-domain solutions. Thus, CPS based solutions will be applied in different application domains like e-Health, Smart Grid, Smart Transportation and so on, to assure the expected response from a complex system that relies on the smooth interaction and cooperation of diverse networked physical systems. The Wireless Sensors Networks (WSN) are a well-known wireless technology that are part of large CPS. The WSN aims at monitoring a physical system, object, (e.g., the environmental condition of a cargo container), and relaying data to the targeted processing element. The WSN communication reliability, as well as a restrained energy consumption, are expected features in a WSN. This paper shows the results obtained in a real WSN deployment, based on SunSPOT nodes, which carries out a fuzzy based control strategy to improve energy consumption while keeping communication reliability and computational resources usage among boundaries.