IoT and TinyML from buzzwords to reality
Every certain period of time buzzwords appears in the technology world, a revolutionary technology, business, or idea that will change the world. We have heard words such as blockchain, machine learning, virtual reality, nft among others, but the first one I heard when I was studying was the Internet of Things.
Internet of things describes the net of physical objects that integrate sensors, software, and other technologies to connect and exchange data with other devices through the cloud. Since then I have had little to no interaction with IoT during my university, but suddenly everything exploded. IoT was an ever-growing concept in my life, from consumer products cheaper and easier to find Amazon smart connectors to endless ESP32 Wi-Fi projects while scrolling in Hackster.io Iot was everywhere with a global market expected to reach a value of USD 1,386.06 billion by 2026 from USD761.4 billion in 2020. Moreover, the Covid-19 pandemic has increased the desire for emerging technology-enabled solutions that will provide connected continuous monitoring that reduces the risks of caregivers being exposed to the virus.
Now, another buzzword is more real than ever and is making its way to the embedded world. Tiny machine learning is a field of machine learning technologies and applications including hardware, algorithms, and software capable of performing on-device sensor data analytics at extremely low power, typically in the mW range and below. The merging of both technologies will have a great impact, with 2.5 billion tinyML enabled devices in the world by 2030 will provide a framework for having an integrated system that receives sensor data, make calculations, take decisions and send data with the following benefits:
Data Security: Privacy of data is more reliable since only the really necessary data is sent to the cloud.
Low power: Transferring data to the cloud and the server infrastructure requires a lot of energy.
No dependence on connection: Decisions can be made on-site since the model is on the device, not in the cloud.
Latency: Data transfer takes time which generates a delay.
TinyML and IoT are the way to go in several industries, from agriculture to healthcare, their impact is much more noted and will be far more in the next years when their journey from buzzwords to reality will end.