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The synergy between Artificial Intelligence and the Edge Computing paradigm promises to transfer decision-making processes to the periphery of sensor networks without the involvement of central data servers. For this reason, we recently witnessed an impetuous development of devices that integrate sensors and computing resources in a single board to process data directly on the collection place. Due to the particular context where they are used, the main feature of these boards is the reduced energy consumption, even if they do not exhibit absolute computing powers comparable to modern high-end CPUs. Among the most popular Artificial Intelligence techniques, clustering algorithms are practical tools for discovering correlations or affinities within data collected in large datasets, but a parallel implementation is an essential requirement because of their high computational cost. Therefore, in the present work, we investigate how to implement clustering algorithms on parallel and low-energy devices for edge computing environments. In particular, we present the experiments related to two devices with different features: the quad-core UDOO X86 Advanced+ board and the GPU-based NVIDIA Jetson Nano board, evaluating them from the performance and the energy consumption points of view. The experiments show that they realize a more favorable trade-off between these two requirements than other high-end computing devices.

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Most recent e-learning platforms include testing modules and Computer Based Assessment (CBA) is currently one of the most pressing issues in Learning Management Systems (LMS). Traditional testing techniques aren't fully adequate to examination administration in higher education contexts, as they generally require choosing the answer just in a narrow set of options. Adaptive testing permits an evaluation more suitable for the examinee but it is affected by limits related to the uniformity of the exams, to the range/width of the subjects and to the large amount of items to be inserted in the question databases. e-Xamina is a prototype of assessment platform aimed at combining the features of these two main kinds of CBA. e-Xamina model suggests a new performance based exam pattern and a score criterion based on standard examination practice. Finally, e-Xamina multi-user feature allows the teachers to cooperate in managing course database, with many different levels of competence and right