This paper presents a comprehensive mathematical model to characterize the energy dynamics of batteryless IoT sensor nodes powered entirely by ambient energy harvesting. The model captures both the energy harvesting and consumption phases, explicitly incorporating power management tasks to enable precise estimation of device behavior across diverse environmental conditions. The proposed model is applicable to a wide range of IoT devices and supports intelligent power management units designed to maximize harvested energy under fluctuating environmental conditions. We validated our model against a prototype batteryless IoT node, conducting experiments under three distinct illumination scenarios. Results show a strong correlation between analytical and measured supercapacitor voltage profiles, confirming the proposed model's accuracy.

Analytical Modeling of Batteryless IoT Sensors Powered by Ambient Energy Harvesting

Zanella, Andrea;
2025

Abstract

This paper presents a comprehensive mathematical model to characterize the energy dynamics of batteryless IoT sensor nodes powered entirely by ambient energy harvesting. The model captures both the energy harvesting and consumption phases, explicitly incorporating power management tasks to enable precise estimation of device behavior across diverse environmental conditions. The proposed model is applicable to a wide range of IoT devices and supports intelligent power management units designed to maximize harvested energy under fluctuating environmental conditions. We validated our model against a prototype batteryless IoT node, conducting experiments under three distinct illumination scenarios. Results show a strong correlation between analytical and measured supercapacitor voltage profiles, confirming the proposed model's accuracy.
2025
IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
36th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2025
   Taming the environmental impact of mobile networks through GREEN EDGE computing platforms
   GREENEDGE
   European Commission
   Horizon 2020 Framework Programme - European Training Networks
   953775
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3593859
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 0
  • OpenAlex ND
social impact