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<p class="0abstract">Distance learning experiments have been launched since 2010 in several Moroccan universities as part of an experimental approach. It, therefore, seems to us that a strategy must be put in place to give this choice of education its place in training and grant it the means necessary to achieve these objectives. The objective of this paper has presented an overview, of free and proprietary e-learning platforms, their functional architectures, as well as the types of e-learning devices that can create from platforms e-learning. To deduce the state of the art of the e-learning provisions of Moroccan universities and compare them with the e-learning provisions of European and American universities.</p>

In this paper, we present a new scheme to intelligently control the cycles and phases of traffic lights by exploiting the road traffic data collected by a wireless sensor network installed on the road. The traffic light controller determines the next phase of traffic lights by applying the Ant Colony Optimazation metaheuristics to the information collected by WSN. The objective of this system is to find an optimal solution that gives the best possible results in terms of reducing the waiting time of vehicles and maximizing the flow crossing the intersection during the green light. The results of simulations by the SUMO traffic simulator confirm the preference of the developed algorithm over the predefined time controller and other dynamic controllers.

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Traffic optimization at an intersection, using real-time traffic information, presents an important focus of research into intelligent transportation systems. Several studies have proposed adaptive traffic lights control, which concentrates on determining green light length and sequence of the phases for each cycle in accordance with the real-time traffic detected. In order to minimize the waiting time at the intersection, the authors propose an intelligent traffic light using the information collected by a wireless sensors network installed in the road. The proposed algorithm is essentially based on two parameters: the waiting time in each lane and the length of its queue. The simulations show that the algorithm applied at a network of intersections improves significantly the average waiting time, queue length, fuel consumption, and CO2 emissions.

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