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Mobile collaborative learning is considered the next step of on-line collaborative learning by incorporating mobility as a key and breakthrough requirement. Indeed, the current wide spread of mobile devices and wireless technologies brings an enormous potential to e-learning, in terms of ubiquity, pervasiveness, personalization, flexibility, and so on. For this reason, Mobile Computer-Supported Collaborative Learning has recently grown from a minor research field to significant research projects covering a fairly variety of formal and specially informal learning settings, from schools and universities to workplaces, museums, cities and rural areas. Much of this research has shown how mobile technology can offer new opportunities for groups of learners to collaborate inside and beyond the traditional instructor-oriented educational paradigm. However, mobile technologies, when specifically applied to collaborative learning activities, are still in its infancy and many challenges arise. In addition, current research in this domain points to highly specialized study cases, uses, and experiences in specific educational settings and thus the issues addressed in the literature are found dispersed and disconnected from each other. To this end, this paper attempts to bridge relevant aspects of mobile technologies in support for collaborative learning and provides a tighter view by means of a multidimensional approach.

In Wireless Mesh Networks (WMNs) the meshing architecture, consisting of a grid of mesh routers, provides connectivity services to different mesh client nodes. The good performance and operability of WMNs largely depends on placement of mesh routers nodes in the geographical area to achieve network connectivity and stability. Thus, finding optimal or nearoptimal mesh router nodes placement is crucial to such networks. In this work we propose and evaluate Genetic Algorithms (GAs) for near-optimally solving the problem. In our approach we seek a two-fold optimization, namely, the maximization of the size of the giant component in the network and that of user coverage. The size of the giant component is considered here as a criteria for measuring network connectivity. GAs explore the solution space by means of a population of individuals, which are evaluated, selected, crossed and mutated to reproduce new individuals of better quality. The fitness of individuals is measured with respect to network connectivity and user coverage being the former a primary objective and the later a secondary one. Several genetic operators have been considered in implementing GAs in order to find the configuration that works best for the problem. We have experimentally evaluated the proposed GAs using a benchmark of generated instances varying from small to large size. In order to evaluate the quality of achieved solutions for different possible client distributions, instances have been generated using different distributions of mesh clients (Uniform, Normal, Exponential and Weibull). The experimental results showed the efficiency of the GAs for computing high quality solutions of mesh router nodes placement in WMNs.

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The Mobile Ad Hoc Networks (MANETs) are useful in many applications environments and do not need any infrastructure support. Much work has been done on routing in MANETs. However, the proposed routing solutions only deal with the best effort data traffic. Connections with Quality of Service (QoS) requirements, such as voice channels with delay and bandwidth constraints, are not supported. The QoS routing has been receiving increasingly intensive attention in the wireline network domain. However, these QoS routing algorithms can not be applied directly to MANETs, because of the bandwidth constraints and dynamic network topology of MANETs. Searching for the shortest path with minimal cost and finding delay constrained least-cost paths are NP-complete problems. For this reason, approximated solutions and heuristic algorithms should be developed for multi-path constraints QoS routing. Also, to cope with changing of MANET topology, routing methods should be adaptive, flexible, and intelligent. In this paper, we propose a Genetic Algorithm (GA) based routing method for Mobile Ad-hoc Networks (GAMAN). Robustness rather than optimality is the primary concern of GAM AN. The GAM AN uses two QoS parameters for routing. The performance evaluation via simulations shows that GAM AN is a promising QoS routing algorithm for MANETs.

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With the emerging paradigm of grid computing and the development of grid infrastructures, grid-based applications are becoming a common approach for solving many complex, large-scale problems in science and engineering. In order to benefit from the large computing power of grid systems, efficient allocation of jobs to resources is necessary. In this work, we consider the allocation problem in immediate mode, in which jobs are allocated as soon as they arrive in the system. We implemented several methods and measured four parameters of the system: makespan, flowtime, resource utilization and matching proximity. The immediate methods are especially interesting when good quality allocations are necessary in very short time. The considered methods have been tested using the most difficult benchmark in the literature for the problem. The computational results allowed us to identify which of considered methods perform better for makespan, flowtime, resource utilization and matching proximity. Also, we were able to evaluate the usefulness of such methods if we knew in advance certain grid characteristics such as degree of consistency of computing, heterogeneity of jobs and resources.