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.
@article{arxiv.2510.09842,
title = {Towards Automated and Predictive Network-Level Energy Profiling in Reconfigurable IoT Systems},
author = {Mohammud J. Bocus and Senhui Qiu and Robert J. Piechocki and Kerstin Eder},
journal= {arXiv preprint arXiv:2510.09842},
year = {2025}
}