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Smart Internet of Everything: Machine Learning in IoT and its Applications

IoT today is one of the most significant sources of new data. Considering that, data science will provide a considerable contribution to making IoT applications more intelligent and fast. Current applications of data science backed with Machine Learning has helped us deduce significant factors to help achieve optimum success in this field.

First, since data gets generated from different sources with specific data types, it is imperative to adopt or develop algorithms that has the capacity to handle the data characteristics.Next, the vast number of resources that generate data in real-time are not without the problem of scale and velocity. Conclusively, finding the best data model that fits the data is one of the most vital issues for pattern recognition and for better analysis of IoT data. 

These so called ‘issues’ have paved a path for a vast number of opportunities in expanding new developments. Big data can be laid down as high-volume, high-velocity, and high variety data that demands cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.

3 Major Concepts of Machine Learning in IoT

In order to better understand what algorithm best fits for processing and decision-making in the field of IoT, one needs to understand the most basic concepts of IoT.

  1. i) The overall application of IoT
  2. ii) The data-driven vision of ML algorithms

iii) Characteristics of IoT data

The overall application of IoT

As we know, the purpose of IoT is to develop a smarter environment and a simplified life-style by saving time, energy, and money. It also reduces significant amount of costs for major industries. Four major components of IoT include: 1) sensors, 2)processing networks, 3) data analysis data, and 4) system monitoring. Since IoT is integrated with a  number of technologies, and connectivity is a mandatory and sufficient condition for it to function, there are certain communication protocols which can are some of the most basic ingredients of this technology. Cumulatively, we need to enhance these components:

(1)Device to Device (D2D): is a type of communication which enables communication between nearby mobile phones; representing the next generation of cellular networks.

(2) Device to Server (D2S): is a type of communication device where all of the data is sent to the servers; can be either close or far from the devices. Such communication is majorly applied to cloud processing.

(3) Server to Server (S2S): is a type of communication where servers transmit data between each other and is majorly applied for cellular networks.


Before transferring data to other devices, one needs to prepare the data in order to establish communication. For this, there are various analytical processes and computing methods that are used.

Fog Computing:- This method is applied in order to migrate information from the data center task to the edge of the servers.

Edge computing:- The processing is run at a distance from the core in this type of computing.

Cloud computing:- Cloud has high latency and high load balancing, indicating that this architecture is not sufficient for processing IoT data because most processing should run at high speeds.

Once we understand the detailed classification and the purpose for which we intend to use the IoT device, we can establish the correct type of algorithm to use under the hood. Majorly this part of allocating algorithms comes up during the process of IoT app development and a lot of brainstorming goes behind it.

Let us have a look on the surface of some of the most widely-used and sophisticated Machine Learning algorithms that can be inculcated with the IoT devices.

A) Classification:- This type of ML algorithm is used in smart cities, especially for managing smart traffic. It helps in traffic prediction and in increasing data abbreviation.

B) Clustering:- This algorithm is used for smart traffic and smart health. It again aids in traffic prediction and in increasing data abbreviation along with patient data monitoring.

C) Linear Regression:- This algorithm is mainly used in economics and helps in real-time prediction along with data abbreviation.

D) K-Nearest Neighbours:- This algorithm is applied for smart-citizens and helps in analysing passenger travel patterns.

E) Feed Forward Neural Network:- Used for smart health purposes and helps in reducing energy-consumption and forecast the state of elements.

F) Canonical Correlation Analysis:- Used for monitoring public places and helps majorly in fault detection.

Conclusion

IoT has excited every single individual connected with information technology today. It promises an all-connected, all-encompassing future. These connections and smart devices together will lay down the foundation of a world we have so fondly visualized with sci-fi books and movies.

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Technology consultant in leading web and mobile application development company committed to providing end-to-end IT services in Web, Mobile & Cloud.

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