{"id":85550,"date":"2023-09-26T14:09:50","date_gmt":"2023-09-26T06:09:50","guid":{"rendered":"https:\/\/www.seeedstudio.com\/blog\/?p=85550"},"modified":"2023-10-31T17:12:29","modified_gmt":"2023-10-31T09:12:29","slug":"xiao-nrf52840-sense-powered-self-sustaining-iot-harnessing-indoor-solar-cells-and-edgeml","status":"publish","type":"post","link":"https:\/\/www.seeedstudio.com\/blog\/2023\/09\/26\/xiao-nrf52840-sense-powered-self-sustaining-iot-harnessing-indoor-solar-cells-and-edgeml\/","title":{"rendered":"XIAO nRF52840 Sense-Powered Self-Sustaining IoT: Harnessing Indoor Solar Cells and EdgeML"},"content":{"rendered":"\n

Traditional battery-powered IoT devices are often unreliable and costly due to frequent recharging or replacement. Indoor solar cells offer a sustainable alternative by harnessing indoor light for perpetual energy, eliminating the need for constant battery changes. In this project, discover how to use a Seeed Studio XIAO nRF52840 Sense to run a machine learning algorithm trained with Edge Impulse, sending inference values via BLE to a gateway.<\/p>\n\n\n\n

Seeed Hardware:<\/strong> Seeed Studio XIAO nRF52840 Sense<\/a><\/p>\n\n\n\n

Software:<\/strong> Arduino, Edge Impulse<\/p>\n\n\n\n

Industry:<\/strong> Smart Buildings, Smart Agriculture, Home Automation, Wildlife Conservation<\/p>\n\n\n\n

The Background<\/h2>\n\n\n\n

The advent of the Internet of Things (IoT) has transformed our interaction with technology, albeit at a price \u2013 increased power consumption. Conventional battery-operated IoT devices often demand frequent recharging or replacement, leading to unreliability and long-term expenses. Fortunately, an alternative solution exists: indoor solar cells.<\/p>\n\n\n\n

By harnessing indoor light sources, these devices can furnish dependable, energy-efficient power for edge-based machine learning models. This breakthrough eliminates the necessity for constant battery maintenance and introduces a new era of self-sustaining IoT applications. In this project, we will delve into how indoor solar cells empower ultra-low-power machine learning models on edge devices.<\/p>\n\n\n

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The Challenge <\/h2>\n\n\n\n

The challenge in deploying machine learning models on IoT devices presents a dual dilemma. Firstly, there’s the imperative for dependable power sources capable of meeting the computational demands of the algorithms. Secondly, these power solutions must be economically viable and user-friendly, facilitating widespread acceptance of these technologies.<\/p>\n\n\n\n

Indoor solar cells offer a promising solution to address these hurdles by delivering a dependable, energy-efficient power source for fueling machine learning models on IoT devices. By capturing indoor light, these cells can provide uninterrupted energy, obviating the necessity for frequent battery replacements and ushering in a transformative era of self-sufficient edge computing applications.<\/p>\n\n\n\n

The Solution<\/h2>\n\n\n\n

In order to showcase the seamless integration of low-power IoT devices capable of running machine learning models on the edge with Indoor Solar Cells, Mihnea Stoica<\/a>, Alexandra Covor<\/a>, Constantin Craciun<\/a>, Zalmotek<\/a> and Andrei Nicula<\/a> have developed a cutting-edge application. This innovative system utilizes the XIAO nRF52840 Sense, a powerful IoT development board, to execute a machine learning algorithm trained via Edge Impulse. The inference results are then transmitted via Bluetooth Low Energy (BLE) to a designated gateway. By leveraging firmware optimized for minimal power consumption, the XIAO board can operate efficiently for extended durations using solar energy.<\/p>\n\n\n\n

The Hardware Pick<\/h4>\n\n\n\n