This paper constitutes a state-of-the art of specification languages relevant to be used as front-ends towards the DEVS (Discrete EVent System Specification) formalism. Comparison criteria are defined to evaluate specification languages for the description of DEVS structures. Finally, the need for building an original front-end, accounting for the whole criteria, is discussed.
University of Corsica and CNRS are working on a scientific program called "Smart Paesi". This project focus on a sustainable rural territories development using advanced artificial intelligence concepts in order to adapt smart city concept (including sustainable development, ICT with by example wireless sensors network, education, e-citizenship, governance) to rural territories and their specificities. In this paper, we introduce a new approach combining discrete event modelling concepts and machine learning methods. This work is a first step towards the conception of a generic and scalable framework allowing model generation from large amount of data.
Wireless sensor network (WSN) deployment is an intensive field of research. In this paper, we propose a novel approach based on machine learning (ML) and metaheuristics (MH) for supporting decision-makers during the deployment process. We suggest optimizing node positions by introducing a new hybridized version of the “Hitchcock bird-inspired algorithm” (HBIA) metaheuristic algorithm that we named “Intensified-Hitchcock bird-inspired algorithm” (I-HBIA). During the optimization process, our fitness function focuses on received signal maximization between nodes and antennas. Signal estimations are provided by the machine learning “K Nearest Neighbors” (KNN) algorithm working with real measured data. To highlight our contribution, we compare the performances of the canonical HBIA algorithm and our I-HBIA algorithm on classical optimization benchmarks. We then evaluate the accuracy of signal predictions by the KNN algorithm on different maps. Finally, we couple KNN and I-HBIA to provide efficient deployment propositions according to actual measured signal on areas of interest.
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