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arXiv · 2510.09842

Towards Automated and Predictive Network-Level Energy Profiling in Reconfigurable IoT Systems

Mohammud J. Bocus·Senhui Qiu·Robert J. Piechocki·Kerstin Eder
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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.

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Mohammud J. Bocus, Senhui Qiu, Robert J. Piechocki, Kerstin Eder. 2025-10-10. Towards Automated and Predictive Network-Level Energy Profiling in Reconfigurable IoT Systems. https://arxiv.org/abs/2510.09842

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