{"ID":2857757,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.09842","arxiv_id":"2510.09842","title":"Towards Automated and Predictive Network-Level Energy Profiling in Reconfigurable IoT Systems","abstract":"Energy efficiency has emerged as a defining constraint in the evolution of sustainable Internet of Things (IoT) networks. This work moves beyond simulation-based or device-centric studies to deliver measurement-driven, network-level smart energy analysis. The proposed system enables end-to-end visibility of energy flows across distributed IoT infrastructures, uniting Bluetooth Low Energy (BLE) and Visible Light Communication (VLC) modes with environmental sensing and E-ink display subsystems under a unified profiling and prediction platform. Through automated, time-synchronized instrumentation, the framework captures fine-grained energy dynamics across both node and gateway layers. We developed a suite of tools that generate energy datasets for IoT ecosystems, addressing the scarcity of such data and enabling AI-based predictive and adaptive energy optimization. Validated within a network-level IoT testbed, the approach demonstrates robust performance under real operating conditions.","short_abstract":"Energy efficiency has emerged as a defining constraint in the evolution of sustainable Internet of Things (IoT) networks. This work moves beyond simulation-based or device-centric studies to deliver measurement-driven, network-level smart energy analysis. The proposed system enables end-to-end visibility of energy flow...","url_abs":"https://arxiv.org/abs/2510.09842","url_pdf":"https://arxiv.org/pdf/2510.09842v1","authors":"[\"Mohammud J. Bocus\",\"Senhui Qiu\",\"Robert J. Piechocki\",\"Kerstin Eder\"]","published":"2025-10-10T20:16:00Z","proceeding":"cs.NI","tasks":"[\"cs.NI\",\"cs.AR\",\"cs.PF\"]","methods":"[]","has_code":false}