Machine Learning Blazes Path for Sustainable Environmental Tech

A new field of Machine Learning called tinyML makes it possible to run Machine Learning models on tiny, battery-powered Internet of Things (IoT) devices. These devices will play a central role in helping organizations meet Environmental, Social, and Governance (ESG) goals.

Introduction

Just a few years ago, Machine Learning (ML) was a 'cloud only' capability. This was due chiefly to applying technologies like Deep Neural Networks (DNN) to inputs like audio, images, and video. For ML to achieve ‘human grade’ performance, a lot of training iterations and labeled data are needed. This requires lots of resources such as GPUs and storage that are available at the click of a button for every cloud provider. As a result, ML evolved mainly on the cloud, but that doesn’t mean it needs to stay there. Nowadays, a new field of Machine Learning called tinyML makes it possible to run Machine Learning models on tiny, battery-powered Internet of Things (IoT) devices.

The Internet of Things (IoT)

The IoT is a network of physical objects— “things”—that are embedded with sensors, software, and other technologies for the purpose of digitalization and automation. These devices range from ordinary household objects to sophisticated industrial equipment. A common trait for all IoT devices is that they are connected to a platform, and sometimes to each other, to perform their function. Connectivity is typically achieved through a cellular network for outdoor IoT devices. When indoors or in dense urban areas, WiFi, LPWAN or Bluetooth can be used. With more than 7 billion devices, this huge market is constantly growing. Some industry experts predict this number to grow to 22 billion by 2025. It’s no surprise that there is a “gold rush” to cash in on expected riches.

Machine Learning for IoT Devices

The vast majority (billions!) of IoT devices do not possess a fraction of the computing power that is available on the cloud. However, recent breakthroughs in Machine Learning (ML) allow even the tiniest IoT device to perform certain Machine Learning tasks. This emerging field of Machine Learning is known as tinyML. This combination of ML on tiny IoT devices is a part of larger concept of moving intelligence from the cloud to ‘Edge devices’ and is often referred to as ‘Edge Artificial Intelligence’ or ‘Edge AI’ in short.

Some pioneering applications for tinyML on IoT devices include:

• Voice commands like Alexa and Siri

• Smart cameras with object and facial recognition

• Real-time health and activity monitoring

• Smart city parking with automatic billing

Applications for Smart Cities

Take smart city parking, for example. One way to optimize parking is to put a video camera (similar to surveillance cameras!) at every street corner and monitor who parks where and when. This allows the municipality to automatically start the billing process for parking slots, as well as letting people know where the empty spots are located. Traditionally, this would require sending a live video feed to the cloud for processing. This creates a huge privacy issue: the municipality only needs to know the license number of the car to start the billing process. However, a live video feed contains much more information, like who is riding in the car with whom, creating a huge privacy issue. This is exactly where tinyML comes into play: tinyML lets you process the live video feed on resource-constrained devices in the field, without having to send it to the cloud. In this case, the only thing that would be sent to the cloud is the license plate number, and with that, the privacy issue is gone. The raw video would never leave the camera.

Environmental Impact

The combination of IoT and tinyML will also play a central role in helping organizations meet Environmental, Social, and Governance (ESG) goals. Environmental monitoring projects leverage IoT technologies for collecting field data such as air quality, water quality, noise levels, among other parameters. Traditionally, applying Machine Learning methods for Anomaly Detection would have happen on the cloud. With tinyML, these battery-powered IoT devices can now perform Anomaly Detection and other Machine Learning tasks right there at the field. This eliminates the latency introduced by transmitting data to the cloud for analysis and extends the battery life of the devices as well.

Conclusions

In the past, machine learning and tiny IoT devices were like chalk and cheese. It was nearly impossible to apply machine learning to these tiny devices. Machine learning was therefore done only in the cloud. As tinyML matures from a niche research field to a production-grade technology, billions of tiny IoT devices can now leverage machine learning. The fusion of IoT and Machine Learning opens the door for many new applications in a wide variety of industries. This in turn will increase the adoption of IoT technologies and boost the digitalization of cities and other legacy industries such as critical infrastructure, environmental monitoring, and transportation.