IoT-Based Systems Construction with Python and TinyML

Internet of things (IoT) General Indication

Internet of things (IoT) empowers uninterrupted control of environments, mechanisms, and machines by using tiny sensors. With advanced developments in sensor technologies and microcontrollers, assembling IoT platforms has become highly efficient.

Not only this, at possible affordable prices, IoT systems are configured with numerous connectivity options. IoT devices such as sensors and hardware are being employed in large places, i.e., public areas, and residencies. The increase in demand for these IoT sensors lies in their effectiveness and low-cost availability.

The IoT sensors generate an elephantine amount of data. Also, these sensors control and monitor the deployed environments, physical properties, and 24/7 ensured maintenance. There are countless examples of IoT devices that support people and industries.

Air quality sensors constantly inspect the gaseous contaminants in the air, indoor, and outdoor. The baby monitor has microphones that are always listening. Smartwatches always pay attention to crucial health parameters with the help of sensors. Similarly, diverse sensors are available for measuring the physical conditions, i.e., pressure, humidity, temperature, and magnetometer. 

Machine Learning

Discovering the insights, patterns, information, and knowledge present in the data is the core function of machine learning (ML) algorithms. The manual inspection of data is not a stress-free and informal activity as it demands vast and immense comprehension.

The junction of IoT and machine learning algorithms make the development highly efficient with smart applications, smart environments, and a better user experience. Tiny machine learning is also indicated as tinyML enables the low-power, low-latency, high performance, and frivolous ML inference.

Python and Tiny Machine learning for Effective IoT-Based Systems

Python for TinyML 

Usually, embedded systems built based upon the idea of not making it intelligent. Instead, the idea of creating devices smart is very popular these days. Therefore, enhance TinyML is introducing that can execute on-device inquiries with sensor inputs with tremendously low power.

With hardware enhancements and the expansions in TinyML tools such as TensorFlow Lite, it becomes easy to built-up such devices that are smart.

Python library TensorFlow Lite supports in assembling machine learning models. This library of Python supports the C, C++, and JAVA for model building. However, there are other alternatives of TensorFlow Lite available yet they are not so common for model building.

The use-cases offered through TensorFlow Lite are as follows:

      • Object Detection: Python TensorFlow Lite supports multiple object detection in an image with 80 unique and different classes of objects.  
      • Smart Responses: Smart responses when a developer uses it to build algorithms.

Pros of IoT-Based Devices Constructed with TinyML

There are multiple advantages of automated machine learning listed down below:

      • Deployment Environment for IoT

In IoT devices, specifically utilize tiny microcontrollers short for (MCUs). Machine learning algorithms also build with the employment of the MCUs. Hence, the deployment procedure of the systems becomes super easy.

      • Data Privacy of IoT based Systems

Local embedded tiny microcontrollers make the machine learning inference happen. With the support of MCUs, the ML engineers get relax not to sending data streams. Hence, data will become private and secure as it remains on-device.

      • High Performance

Due to the less or sometimes no transmission of data, tinyML also consumes less power and permits it to function with high performance.

      • Availability

In milliseconds, all the transactions carry out without any single interruption. Meanwhile, all inferences are local which makes it 24/7 available across the network.