Current Trends: Machine Learning and AI in IoT

Introduction

The Internet of Things (IoT) is the network of various physical devices in-order to exchange data and take appropriate action. In recent years the growth in technology enhances the communication between different devices are made much easier. It is estimated that there will be 30 billion devices by 2020 [Nordrum et al ., 2016 ]. Some of the IoT applications include automated vehicles, home automation, remote health monitoring etc. In-order to make these devices work in a smarter way or to make IoT applications more intelligent there is need for analyzing the huge amount of data using machine learning algorithm [Mahdavinejad et al., 2016]. Machine learning refers to the set of techniques meant to deal with huge data in the most intelligent way in order to derive actionable insights. Figure 1 refers to the confluence of different fields such as IoT, artificial intelligence and big data

Internet of Things

The Internet of Things (IoT), refers to the collection of inter connected everyday objects over the Internet and also to one another. It provides users with smarter and smoother experiences. Internet of Things is mainly being driven by various sensors that would possibly sense the real world data, some of the widely used sensors are temperature, pressure, gas, smoke, IR, image sensors etc. IoT platform could deliver plenty of functionalities with the intelligence by combining a set of sensors and a communication networks. Thus it could able to improve and achieve effectiveness in their autonomous functionality.

The data that is flowing across the network and devices are being stored and the same is being processed, to derive the required insights. The various stakeholders who are in need of those insights will be served on time. The data sharing is done in a secured way, only the authorized users permitted to use the same.

Let us take a worlds well know Tesla vehicles as an example. The sensors mounted in and around the car senses variety of data and derive many fact values based on the perception from the environment. Then it uploads data into a huge database. The data is further processed and send necessary signals to other vehicles or other parts.

Figure 1.

Confluence of IoT, artificial intelligence and big data

Foremost Uses Of Machine Learning In Iot

• •

  Clustering of Data: Clustering deals with grouping objects which are similar to each other based on an objective measure. Binary Classification (positive or negative), Logistic Regression (discrete outcome), K-Means for clustering the data are some prevalent algorithms that’s being useful in clustering in Machine Learning. From any real time data to identify certain behavioral analysis is possible. Example: Medical data, data from the devices such as gyroscope, accelerometer etc.

• •

  Anomaly Detection: Anomaly detection is a technique used to identify uncommon patterns that do not conform to anticipated behavior, called outliers. It finds many applications in business, from intrusion detection (identifying abnormal patterns in network traffic that could sign a hack) to system health monitoring (spotting a malignant tumor in a scan report), and from fraud detection in credit card transactions.

• •

  Prediction of Data Trends: Predictive analytics utilizes historical data to predict future happenings. Usually, historical data is used to build a mathematical model that captures significant trends. The predictive model is then used on present data to predict what will happen next, or to suggest activities to be taken to achieve the optimal outcomes.