Special Session: Physically Flexible Devices for Health and Activity Monitoring: Challenges from Design to Test

Tuncel, Yiğit; Bhat, Ganapati; Ogras, Ümit Y.

doi:10.1109/vts48691.2020.9107559

Cited by 4 publications

(2 citation statements)

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“…The energy generated by a PV-cell quickly reduces for indoor lighting conditions where the irradiance is lower [34]. Furthermore, previous studies showed that indoor lighting conditions at the home, office, and store environments significantly differ from each other.…”

Section: B Light Energy Harvesting Modelmentioning

confidence: 99%

“…FlexSolarCells SP3-37, a lightweight, flexible, and small form-factor PV-cell, is used as the transducer [33]. We identified the I-V characteristics of SP3-37 in a previous study through a set of controlled experiments with a halogen lamp [1], [34]. This information is used to compute the harvested light energy when the irradiance is known.…”

Section: B Light Energy Harvesting Modelmentioning

confidence: 99%

See 1 more Smart Citation

ECO: Enabling Energy-Neutral IoT Devices through Runtime Allocation of Harvested Energy

Tuncel¹,

Bhat²,

Park³

et al. 2021

Preprint

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Energy harvesting offers an attractive and promising mechanism to power low-energy devices. However, it alone is insufficient to enable an energy-neutral operation, which can eliminate tedious battery charging and replacement requirements. Achieving an energy-neutral operation is challenging since the uncertainties in harvested energy undermine the quality of service requirements. To address this challenge, we present a rollout-based runtime energy-allocation framework that optimizes the utility of the target device under energy constraints. The proposed framework uses an efficient iterative algorithm to compute initial energy allocations at the beginning of a day. The initial allocations are then corrected at every interval to compensate for the deviations from the expected energy harvesting pattern. We evaluate this framework using solar and motion energy harvesting modalities and American Time Use Survey data from 4772 different users. Compared to state-of-theart techniques, the proposed framework achieves 34.6% higher utility even under energy-limited scenarios. Moreover, measurements on a wearable device prototype show that the proposed framework has less than 0.1% energy overhead compared to iterative approaches with a negligible loss in utility.

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Section: B Light Energy Harvesting Modelmentioning

confidence: 99%

Section: B Light Energy Harvesting Modelmentioning

confidence: 99%