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Mobile Learning (also known as m-learning) and game based learning (GBL) are two important elements in Technology-Enhanced Learning. By using mobile technology and benefiting from their features we can provide a pervasive learning without being restricted by time and space (Learning anywhere and anytime). GBL over the last decade has played an important role in increasing the motivation of the learner player through the integration of gamification into the learner’s learning process. The combination of the two elements gave birth to a new concept of educational system called Ubiquitous Learning Game (ULG). Mobile technologies are very diverse and market demands push the continued development of new technologies and features that present a big challenge in time and development costs. On the other hand creating a nice game for different player profiles requires the addition of the learner’s model in the design phase of the game. In this sense the main aim of this paper is to present the new architecture of the <e-Adventure > educational adventure games authoring tool and its implementation by addressing the different challenges already cited in order to generate an adaptive ULG for multiples mobile platforms.

Today, Vehicular Ad-hoc Networks (VANET) have become an interesting research topic for developing Intelligent Transport Systems. In urban environments, vehicles move continuously and at different speeds, which leads to frequent changes in the network topology. The main issue faced in an urban scenario is the performance of routing protocols when delivering data from one vehicle to another. This paper introduces ECRDP, an Efficient Clustering Routing approach using a new clustering algorithm based on Density Peaks Clustering (DPC) and Particle Swarm Optimization (PSO). First, the PSO algorithm is applied to determine the cluster heads, or a new fitness function for finding the best solutions is formulated using the DPC algorithm. Next, clustering is performed based on the reliability of links parameter between vehicles. Then, a maintenance phase is proposed to update the cluster heads and redistribute the vehicles in the clusters. Finally, the effectiveness of the suggested scheme is evaluated by a simulation operated by MATLAB on a real urban scenario. The results achieved show an overall increase in stability, proven by a reduction in change rate by 74%, and an improvement in performance indicated by an increase in intracluster throughput by 34% and inter-cluster by 47%, as well as an overall reduction of average delay by 16%.

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