Image by Gerd Altmann from Pixabay | For Representational Purpose Only
Tanveer Singh
As the planet struggles under the weight of 40+ billion metric tons of CO₂ emissions in 2024 alone, and an ever-rising energy demand, the search for smarter, leaner solutions has never been more urgent. There enters the TinyML, where the power of AI meets ultra-low energy computing to drive sustainability at scale.
It may be shocking, but as you are reading this, billions of sensors are tracking the planet’s health – from the air we breathe to the energy we consume. Already, more than 14 billion IoT devices are being used to monitor climate change and are projected to reach a whopping 30 billion by the end of 2030. But the concerning part is that the energy consumed by these devices is around 200 terawatt-hours of electricity annually, which is roughly equivalent to the entire energy consumption of countries like Thailand. To meet this demand, energy is produced through the traditional method of burning fuel, which further emits millions of Carbon footprints annually, that is even more than the lifetime emissions of 4 cars, just to monitor climate change. And therein lies the irony.
Furthermore, the constant transmission of data through these sensors requires millions of dollars for their deployment and maintenance. Like a large-scale smart city as big as New York, IoT networks can cost over $10–15 million per year to operate. This is exactly where TinyML comes as the solution, offering a path that enables IoT devices to process data locally, reducing energy consumption by up to 90% and significantly lowering costs.
Tiny ML bridges the gap between artificial intelligence and embedded systems, allowing machine learning activities even in sensors as small as a grain of sand. It is based on the idea of machine learning that is focused on building machine learning models on low-power devices like microcontrollers, enabling the device to process data instantly and anywhere, without depending on external internet storage to compute it. One clear example is Alexa, which uses TinyML models to send instant responses to the device for processing instead of sending through the cloud (external storage ), which will take a longer time.
Additionally, TinyML improves privacy and data security by running locally and reduces overall operational cost by 50-60% as compared to large ML models working on external storage. Take the example of Google's TinyML image classification that runs directly on devices, keeping images private while cutting storage and cloud costs by over 50%. TinyML can be best understood as having a mini robot in your pocket that can solve problems instantly, instead of always asking a big computer far away for help. It is faster, saves energy, and keeps your information private. When this field is applied to the climate, its efficiency becomes a distinguished factor.
Besides being cost-effective and having higher efficiency, it also helps in tracking air quality to predict natural disasters and, hence, supports the fight against climate change. Tiny ML sensors enable the quick detection of forest fires through heat or smoke detection, and aid in local air and water quality checks, eliminating the need for cloud computing dependency. For instance, Arduino-based air quality sensors are used to measure air quality and provide data on the temperature and humidity of an area. These models can also be used in solar or wind farms to check the performance of the solar cells and windmills through the consumption of energy, which can further help in increasing the efficiency of the farms. For example, Google’s DeepMind AI was successfully used with wind farms in the U.S. to predict wind power output 36 hours in advance, boosting the value of wind energy by around 20%. Interestingly, these sensors can also aid in monitoring birds' and whales' calls or other animals to track migration patterns and population health, as well as because of their small size and working on low power, and hence, they can help researchers to get valuable data on ecosystems without disturbing the wildlife. Moreover, TinyML sensors used in smart grids help in improving energy utilization by constantly monitoring and managing the transport of electricity so that energy is not wasted. Besides this, these devices can help in measuring the water pressure, tidal patterns, and ground movement of an area, and the data from this can be used to detect disasters earlier. For instance, in Japan, Tiny ML sensors placed along coastlines measure tidal waves and ground vibration in real time, which helps authorities to issue faster tsunami and earthquake warnings.
However, while these applications highlight the transformative impact of Tiny ML in tackling climate related problems, the integration also brings forth several challenges that need to be addressed to ensure reliability and scalability. First and foremost is the limitation of hardware, which is that there is limited storage, approximately in kilobytes or 1 to 5 megabytes, to store data compared to traditional models that have memory in gigabytes and terabytes. As a result, small models in TinyML will be less precise than the traditional models, which can be a huge challenge in models that work on reliability, for example, disaster management models. Furthermore, the harsh conditions like weather or wildlife can damage these devices, leading to malfunctioning and increasing the cost of maintenance.
Additionally, even though these devices are cost-effective, deploying billions of devices will still require huge funding, which can limit their production and scalability.
Despite these challenges, the future of TinyML is being shaped by the integration of emerging technologies, large-scale adoption, and the expanding market of AI. The combination of TinyML with the 5 G network, which provides 100 times faster speed than 4 G and the ability to connect over one million devices per square kilometer, can enable the creation of massive, interconnected sensors all over the cities that can provide faster and reliable data. Additionally, integrating it with federated learning- an ML technique that enables multiple devices to train a model together without sharing the raw data - can help in ensuring data privacy and increasing the accuracy of the models. Furthermore, Government and Research institutes are likely to adopt TinyML models in various tasks as they provide a scalable and cost-effective solution, especially in environments with limited resources. For instance, the U.S. National Aeronautics and Space Administration (NASA) has explored TinyML to process sensor data directly on satellites, reducing the need for constant communication with Earth.
It won’t be an exaggeration to say that the Tiny ML models have the potential to shape the future of the world. By offering scalable as well as energy-efficient solutions, Tiny ML stands out as the best alternative to tackle the climate change problems. From reducing the CO2 emissions to providing faster processing of data and strengthening the privacy and accuracy of the data, the Tiny ML model can be a changemaker catalyst not only in the world of climate change but in other fields, too. Undoubtedly, Tiny ML paves the way for a future where artificial intelligence works in harmony with the planet.
Tanveer Singh, a first-year student at Plaksha University, has been passionate about writing articles and poems since high school. From raising public awareness of new technologies to highlighting environmental and societal issues, he has explored a wide range of themes through his work and aspires to continue making an impact in this space for the long run.
