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What is the Role of Machine Learning in IoT?

What is IoT? And its future!

The “Internet of Things” – IoT, technically described as an electronic device that is equipped with sensors, that sends data and receives instructions thanks to the internet connection. To describe in non-technical terms, billions of physical devices (with sensor) connected to internet all around the world. IoT have diverse application around all the sectors, to empower and enrich human’s life on this planet.

For example, let’s take a smartphone, you are listening songs using earphones connected to smartphone while you are busy with other things (driving), there comes IoT powered by (Artificial Intelligence) AI. Imagine IoT sensors in the earphones, that could take your heart rate data and with the help of AI it could predict your emotion. Based on that emotion your smartphone could pick the best song stored somewhere in the world. There are several million songs around the world and your smartphone doesn’t need to have super storage to store all the songs or either super computing power for the applied AI model for emotion sensing. All it needs to ensure is, it is being connected to internet.

According to Business Insider, there will be more than 41 billion IoT devices by 2027, up from about 8 billion in 2019. This survey was constructed by 400 responses from top executives around the world. Those companies include Alibaba, Alphabet, Amazon, Apple, VMWare, Verizon, etc. It further states that by 2027 all the devices that remain behind reaches to get the internet access, and the IoT market grow to over $2.4 trillion annually.

IoT combined with the most dynamic technology of Artificial Intelligence (AI) could possibly make the IoT system itself to become more smarter and can easily mimic the human’s activity.

Role of Artificial Intelligence in IoT

“AI + IoT = AIoT”

AI is defined as the process of making the machines intelligent enough to do the tasks without any human intervention. All the IoT devices together collects huge data and on the other hand to build a state-of-the art AI model it needs huge data. Thus, the combination of these two dynamic techniques makes the monotonous IoT into an intelligent IoT (smart tasks without human intrusion). The powerful combination of IoT with the AI can be a huge breakthrough in humans’ life.

So, when we talk about the Artificial Intelligence (AI), Machine learning (ML) and Deep Learning (DL) plays the more vital role since DL and ML are the subsets of AI.

Machine Learning (ML): Machine learning has a ML algorithms or techniques in the form of computer program that learn insights from data iteratively, on its own or using the set of rules we mention. There are three major types of machine learning algorithms they are: Supervised learning and Unsupervised learning. Let’s see some of the machine learning algorithms or models used in IoT.

Regression: Regression is the fundamental concept in the machine learning. It falls under the category of supervised learning where the model is trained using the input data (independent feature) and the output labels (dependent feature). Regression is applied to the continuous nature of data. There are two types of regression that is linear regression and non-linear regression.

Linear regression is applied when there is a linearity in the input data. For example, when the input x is changed there should be a change possibly in an output y. The equation that the linear regression model uses to train is given by Y = θ1 + θ2 X1. For example, take co2 emission in vehicles based on the engine size and number of cylinders. The emission rate has a linear relation with the engine size and number of cylinders.

Low level TensorFlow for regression problems (house pricing).
Linear regerssion

Non-linear regression, for example consider the data of a china gross domestic income (GDI) per year. Here the independent feature in the data is years and the dependent feature or predicted variable is GDI. From this data we could see the non-linear relation between the variables. The equation for the Non-linear regression is given by Y = θ1 + θ2 (X1)2.

First steps with Non-Linear Regression in R | R-bloggers
Non-linear regression

Classification: Classification is a supervised learning technique. It is used in categorizing the unknown set of items into discreate set of classes. Classification algorithm learn the relationship between input feature variable and target variable of interest. The target variable is categorical with discreate values. The famous classification algorithms widely used are K-Nearest Neighbors, Decision tree, Logistic Regression and Support Vector Machine.

Clustering: Clustering means finding the clusters in a dataset, in an unsupervised technique. Cluster is defined as group of data points or objects in a dataset that are similar to other objects in a group, and dissimilar to data points in another cluster. Widely used clustering algorithms are K-means clustering, Hierarchical clustering and Density-based clustering.

Deep Learning (DL): Deep learning is a sub-field of machine learning, that was designed with the inspiration from human brain and called as an Artificial Neural Network (ANN). Thus, the advancement in deep neural networks makes it more sophisticated to react in real-complex environment faster than humans.

Perceptrons - the most basic form of a neural network · Applied Go

Artificial Neural Network: Artificial neural network was constructed mainly with three layers, they are input layer, hidden layer, output layer. The inputs from the first layer (input layer) gets multiplied by the weight and added bias. The bias and the weights are random at first. Then these values pass through some activation function (ReLU, Sigmoid, Tanh, etc) and then pass on to the next layer until the output layer. This iteration of process can be repeated until we get the optimum performance/accuracy.

Applied Deep Learning - Part 1: Artificial Neural Networks
Artificial neural network

Applications of Machine Learning to IoT

Today there are several ML algorithms applied in IoT. These ML applications highly depends on the applied field. There are several reasons why machine learning influences the IoT. But first what happens if IoT is implemented without ML? IoT has to face the following consequences when it is solely implemented without ML. That includes integration of data from multiple sources, device managements, handling huge volume of data and version controlling of applications.

IoT deals with the interconnection of devices with the main aim of sharing the information (data). These data were the standard reason that makes the ML more powerful, increasing the efficiency of IoT. The key factors that ML contribute to IoT are: to analyze the data and predict the future events, converting raw data into human understandable format, real-time recommendation system, maintenance of the devices (IoT), etc.

The process of making IoT intelligent and analyzing the big data produces by billions of such devices find an application in several fields. Such fields are self-driving vehicles, wearables, industrial automation, agriculture, health-care and retail shopping.

Industrial Automation: When it comes to the production lines in industries you need help of the automated robots. Robots that work alongside of humans called as the collaborative robots or cobots. The main disadvantage of them is that they function without the knowledge of any obstacles (Human’s) present in their environment. This situation could potentially cause lethal injury or death in case. In order to mitigate the physical damage to humans or to make the robots intelligent enough to aware of their working environment certain safety systems are in need. There comes the application of ML/DL algorithms with IoT, in developing the computer vision based intelligent safety system for collaborative robots.

Agriculture:  The world population is continuing to grow, In the next 80 years there will be addition of 3.6 billon peoples to the present population, So, there will be increased demand for the food. Thus, IoT and AI together improve the agricultural production with the following technologies,

  • Precision farming tools using the satellite data. This technique was used to reduce the use of fertilizer that contains nitrogen and to increase crop yields.
  • Crop monitoring, using the data from cameras and sensors the condition of crops can be monitored and analyzed. The machine learning algorithms with the use of those data gives timely update to the farmer about the crop’s condition.
  • AI – powered pest control, the IoT micro sensors along with AI control solutions make farmers be able to treat plants individually and protect them from any potential disease and pests.

Self-driving cars: Self-driving cars, is the future of automobiles. With the combination of IoT (sensors, cameras, LiDAR, RADAR) and Deep neural network it is possible to make the car drive by itself. There is an active research and development going on in this field, carried out by corporate companies like Tesla, Google, Uber, Volvo, etc.

Wearables and Health-care: Wearables could collect the raw heart rate, EEG, and human body motion data with the help of IoT-sensors embedded in it. The retrieval of these metrics can be translated into more accurate and tailored information, through the implementation of artificial intelligence, in order to increase the awareness of the health and fitness conditions, early disease detection and avoidance of the potential risk in the cardiovascular system.

Smart retailing: Make your shopping smarter! With the combination of IoT and AI, the consumer gets the smarter experience in online as well as in an offline shopping. With the help of AI, it could also help the retailer to understand the consumer buying pattern. The multinational clothing-retail company H&M has offered its customer a new shopping experience with the concept of Smart mirror.

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