Towards Automated and Predictive Network-Level Energy Profiling in Reconfigurable IoT Systems
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917004563185664 |
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| author | Bocus, Mohammud J. Qiu, Senhui Piechocki, Robert J. Eder, Kerstin |
| author_facet | Bocus, Mohammud J. Qiu, Senhui Piechocki, Robert J. Eder, Kerstin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_09842 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Automated and Predictive Network-Level Energy Profiling in Reconfigurable IoT Systems Bocus, Mohammud J. Qiu, Senhui Piechocki, Robert J. Eder, Kerstin Networking and Internet Architecture Hardware Architecture Performance 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. |
| title | Towards Automated and Predictive Network-Level Energy Profiling in Reconfigurable IoT Systems |
| topic | Networking and Internet Architecture Hardware Architecture Performance |
| url | https://arxiv.org/abs/2510.09842 |