One of the challenges in the field of human activity recognition in smart homes based on IoT sensors is the variability in the recorded data. This variability arises from differences in home configurations, sensor network setups, and the number and habits of inhabitants, resulting in a lack of data that accurately represent the application environment. Although simulators have been proposed in the literature to generate data, they fail to bridge the gap between training and field data or produce diverse datasets. In this article, we propose a solution to address this issue by leveraging the concept of digital twins to reduce the disparity between training and real-world data and generate more varied datasets. We introduce the Virtual Smart Home, a simulator specifically designed for modeling daily life activities in smart homes, which is adapted from the Virtual Home simulator. To assess its realism, we compare a set of activity data recorded in a real-life smart apartment with its replication in the VirtualSmartHome simulator. Additionally, we demonstrate that an activity recognition algorithm trained on the data generated by the VirtualSmartHome simulator can be successfully validated using real-life field data.

This paper presents a multi-agent simulation implemented in Python, using fuzzy logic to explore collective battery recharge management for autonomous industrial vehicles (AIVs) in an airport environment. This approach offers adaptability and resilience through a distributed system, taking into account variations in AIV battery capacity. Simulation scenarios were based on a proposed charging/discharging model for an AIV battery. The results highlight the effectiveness of adaptive fuzzy multi-agent models in optimizing charging strategies, improving operational efficiency, and reducing energy consumption. Dynamic factors such as workload variations and AIV-infrastructure communication are taken into account in the form of heuristics, underlining the importance of flexible and collaborative approaches in autonomous systems. In particular, an infrastructure capable of optimizing charging according to energy tariffs can significantly reduce consumption during peak hours, highlighting the importance of such strategies in dynamic environments. An optimal control model is established to improve the energy consumption of each AIV during its mission. The energy consumption depends on the speed, as demonstrated via numerical simulations using realistic data. The speed profile of each AIV is adjusted according to the various constraints within an airport. Overall, the study highlights the potential of incorporating adaptive fuzzy multi-agent models for AIV energy management to boost efficiency and sustainability in industrial operations.

Stratégies d’intelligence collective pour des véhicules industriels autonomes efficaces: Les défis de l’industrie 4.0 comprennent l’optimisation des flux de données et la décentralisation de la prise de décision, là où les systèmes centralisés deviennent souvent inadéquats. Les véhicules industriels autonomes (VIAs) doivent devenir plus intelligents et coopératifs, en échangeant des données pertinentes sur le trafic entre eux et avec l’infrastructure. Cette thèse vise à renforcer l’autonomie des VIAs à travers des stratégies d’intelligence collective, améliorant leur adaptabilité, prise de décision et efficacité grâce à une communication et un partage d’informations facilités. Notre méthodologie, basée sur la modélisation, la simulation et des tests scénarisés, vise à proposer des stratégies collectives spécifiques pour renforcer les différentes fonctionnalités clés des VIAs. Tout d’abord, nous avons amélioré un algorithme d’évitement d’obstacles et développé une stratégie globale basée sur la perception partagée. Nous avons adapté et proposé des messages standardisés au contexte de l’industrie4.0 et introduit un système de (ré)allocation dynamique des tâches en environnements décentralisés. Ces contributions se basant sur l’hypothèse forte de la perception coopérative, nous avons ensuite proposé une architecture pour générer des données V2X. Enfin,nous avons développé des stratégies collectives de gestion de l’énergie pour chaque VIA basé sur un modèle de décision flou, afin qu’ils puissent déterminer de manière autonome les moments optimaux de recharge et ainsi réduire leurs temps d’inactivité au sein de la flotte.