A Battery-Free Long-Range Wireless Smart Camera for Face Detection
Abstract
This paper presents a battery-free smart camera that combines tiny machine learning, long-range communication, power management, and energy harvesting. The smart sensor node has been implemented and evaluated in the field, showing both battery-less capabilities with a small-size photovoltaic panel and the energy efficiency of the proposed solution. We evaluated two different ARM Cortex-M4F microcontrollers, the Ambiq Apollo 3 that is an energy-efficient microcontroller, and a Microchip SAMD51 able to work in high radiation environments but with a higher power in active mode. Finally, a low power LoRa module provides the long-range wireless transmission capability. The tiny machine learning algorithm for face recognition has been optimized in terms of accuracy versus energy, achieving up to 97% accuracy recognizing five different faces. Experimental results demonstrated the capability of the developed sensor node to start from the cold start after 1 minute at a very low luminosity of 350 lux using a cm size flexible photovoltaic panels and work perpetually after the cold start.
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Giordano et al. (2020) integrate an ARM Cortex-M4F MCU with a low-power camera module (HM01B0) and a time-of-flight sensor (VL53L1X), with communication via a LoRa transceiver module (SX1262) and power managed by the BQ25504 chip.
“In (Giordano et al., 2020), an ARM Cortex-M4F MCU is integrated with a low-power camera module (e.g., HM01B0) and a time-of-flight sensor (e.g., VL53L1X).”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as Giordano et al., 2020
The Giordano et al. (2020) camera produces images of 320x320 pixels and consumes as low as 1.1 mW of power at 30 frames/s.
“The camera produces images of 320x320 pixels.”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as Giordano et al., 2020
Giordano et al. (2020) achieve 97% accuracy in the wireless camera sensing design mode.
“It achieves 97% accuracy in the wireless camera sensing design mode.”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as Giordano et al., 2020
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Self-citation
- Authors
- Giordano, M., Mayer, P., & Magno, M.
- Affiliations
- ETH Zurich (CH)
- Venue
- Proceedings of the 8th International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems
- Publisher
- ACM
- Published
- 2020-11-16
- Pages
- 29-35
- OpenAlex
- W3097976071
- Cited by
- 38
- References
- 22
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 9 · 2023: 10 · 2022: 6 · 2021: 3
- FWCI
- 11.73
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Workshop on Energy Harvesting & Energy-Neutral Sensing Systems (ENSsys) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Innovative Energy Harvesting Technologies
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Harvesting in Wireless Networks
- Physical Sciences › Computer Science › Computer Networks and Communications › Energy Efficient Wireless Sensor Networks
- Keywords
- face detection, face recognition, TinyML, energy harvesting, photovoltaic modules, LoRa, long-range wireless communication, ARM Cortex-M4F, low-power microcontroller, cold start, energy efficiency
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