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Modeling the Carbon Footprint of Battery-Powered IoT Sensor Nodes for Environmental-Monitoring Applications

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Conference Paper

Maistriaux, Pol, Thibault Pirson, Maxime Schramme, Jérôme Louveaux, and David Bol. 2023. “Modeling the Carbon Footprint of Battery-Powered IoT Sensor Nodes for Environmental-Monitoring Applications.” In Proceedings of the 12th International Conference on the Internet of Things, 9–16. IoT ’22. New York, NY, USA: Association for Computing Machinery.

The Internet-of-Things (IoT) is frequently presented as an effective tool to monitor our environment and subsequently reduce the environmental footprint of human activities. However, the environmental footprint of IoT nodes themselves is often overlooked. The standardized life-cycle assessment (LCA) methodology can help in this respect. While production impacts can be estimated using LCA databases, use phase impacts are complex to model for battery-powered IoT nodes, commonly used in environmental monitoring. Indeed, battery maintenance operations involve component replacement, transportation and depend on the service lifetime which is strongly influenced by the use phase scenario. We therefore propose a comprehensive open-source parametric model of battery-powered IoT nodes use phase in environmental monitoring applications. The model assesses the overall environmental footprint, including deployment and maintenance, with an enhanced service lifetime evaluation. Using a custom node prototype, additionally validating the underlying power consumption modeling, we then analyze a case study. The use phase model fosters eco-design by allowing the optimal battery capacity identification and highlighting the impact of various parameters on the carbon footprint, e.g., use phase scenario, operating conditions, node positioning, transport scheme, and replacement strategy. Finally, the model can easily be transposed to evaluate economic aspects, motivating the environmental and economic co-optimization.

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