{"ID":2871552,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.10867","arxiv_id":"2509.10867","title":"Agent-based Simulation for Drone Charging in an Internet of Things Environment System","abstract":"This paper presents an agent-based simulation model for coordinating battery recharging in drone swarms, focusing on applications in Internet of Things (IoT) and Industry 4.0 environments. The proposed model includes a detailed description of the simulation methodology, system architecture, and implementation. One practical use case is explored: Smart Farming, highlighting how autonomous coordination strategies can optimize battery usage and mission efficiency in large-scale drone deployments. This work uses a machine learning technique to analyze the agent-based simulation sensitivity analysis output results.","short_abstract":"This paper presents an agent-based simulation model for coordinating battery recharging in drone swarms, focusing on applications in Internet of Things (IoT) and Industry 4.0 environments. The proposed model includes a detailed description of the simulation methodology, system architecture, and implementation. One prac...","url_abs":"https://arxiv.org/abs/2509.10867","url_pdf":"https://arxiv.org/pdf/2509.10867v1","authors":"[\"Leonardo Grando\",\"José Roberto Emiliano Leite\",\"Edson Luiz Ursini\"]","published":"2025-09-13T15:47:08Z","proceeding":"cs.MA","tasks":"[\"cs.MA\",\"cs.NI\",\"cs.RO\"]","methods":"[]","has_code":false}