12 Major Applications of IoT You Should Know | Jigsaw Academy (2022)

Introduction of IoT

This article lets us understand the applications of IoT. The Internet of Things is not a mysterious hype anymore. It’s a technology that has slowly gained momentum and is now silently shaping our future. IoT is the result of humankind’s curiosity and intention to lead a convenient and connected lifestyle, reducing labour and eliminating the chances of human errors. That’s why we decided to make devices smart and take care of things that will draw out efficiency. We’ve figured out that data is the new currency, and tons of crucial concerns can be addressed and resolved through data, and this is what drives the concept of the Internet of Things.

By making the devices connected to each other and the internet, we’ve let them collect and communicate data and make precise and informed decisions through Machine Learning and Neural Networks (complex mechanisms). This step has achieved outstanding outcomes.

Right now, as you read, there are billions of connected devices across the globe, collecting billions of petabytes of data every single day. These massive chunks of data are home to crucial pieces of information that can take care of home security, and entertainment needs and goes on to the extent of saving water and controlling fuel emissions. We have all had our experiences with IoT through smartphones, direct-to-home television services, smart televisions, and more. In case you haven’t paid attention, here are some of the top ten uses of IoT that you could’ve missed. Check them out.

Contents at a Glance

  1. Smart Homes
  2. Smart City
  3. Self-driven Cars
  4. IoT Retail Shops
  5. Farming
  6. Wearables
  7. Smart Grids
  8. Industrial Internet
  9. Telehealth
  10. Smart Supply-chain Management
  11. Traffic management
  12. Water and Waste management

List of Top 10 Applications of IoT

1. Smart Homes

One of the best and the most practical applications of IoT, smart homes really take both, convenience and home security, to the next level. Though there are different levels at which IoT is applied for smart homes, the best is the one that blends intelligent utility systems and entertainment together. For instance, your electricity meter with an IoT device giving you insights into your everyday water usage, your set-top box that allows you to record shows from remote, Automatic Illumination Systems, Advanced Locking Systems, and Connected Surveillance Systems all fit into this concept of smart homes. As IoT evolves, we can be sure that most of the devices will become smarter, enabling enhanced home security.

2. Smart City

Not just internet access to people in a city but to the devices in it as well – that’s what smart cities are supposed to be made of. And we can proudly say that we’re going towards realizing this dream. Efforts are being made to incorporate connected technology into infrastructural requirements and some vital concerns like Traffic Management, Waste Management, Water Distribution, Electricity Management, and more. All these work towards eliminating some day-to-day challenges faced by people and bring in added convenience.

3. Self-driven Cars

We’ve seen a lot about self-driven cars. Google tried it out, Tesla tested it, and even Uber came up with a version of self-driven cars that it later shelved. Since it’s human lives on the roads that we’re dealing with, we need to ensure the technology has all that it takes to ensure better safety for the passenger and those on the roads.

The cars use several sensors and embedded systems connected to the Cloud and the internet to keep generating data and sending them to the Cloud for informed decision-making through Machine Learning. Though it will take a few more years for the technology to evolve completely and for countries to amend laws and policies, what we’re witnessing right now is one of the best applications of IoT.

4. IoT Retail Shops

If you haven’t already seen the video of Amazon Go – the concept store from the eCommerce giant, you should check it out right away. Perhaps this is the best use of the technology in bridging the gap between an online store and a retail store. The retail store allows you to go cashless by deducting money from your Amazon wallet. It also adds items to your cart in real-time when you pick products from the shelves.

If you change your mind and pick up another article, the previous one gets deleted and replaces in your cart with the new item. The best part of the concept store is that there is no cashier to bill your products. You don’t have to stand in line but just step out after you pick up your products from shelves. If this technology is effective enough to fetch more patronage, this is sure to become a norm in the coming years.

5. Farming

Farming is one sector that will benefit the most from the Internet of Things. With so many developments happening on tools farmers can use for agriculture, the future is sure promising. Tools are being developed for Drip Irrigation, understanding crop patterns, Water Distribution, drones for Farm Surveillance, and more. These will allow farmers to come up with a more productive yield and take care of the concerns better.

6. Wearables

Wearables remain a hot topic in the market, even today. These devices serve a wide range of purposes ranging from medical, and wellness to fitness. Of all the IoT startups, Jawbone, a wearables maker, is second to none in terms of funding.

7. Smart Grids

One of the many useful IoT examples, a smart grid, is a holistic solution that applies an extensive range of Information Technology resources that enable existing and new gridlines to reduce electricity waste and cost. A future smart grid improves the efficiency, reliability, and economics of electricity.

8. Industrial Internet

The Industrial Internet of Things consists of interconnected sensors, instruments, and other devices connected with computers’ industrial applications like manufacturing, energy management, etc. While still being unpopular in comparison to IoT wearables and other uses, market researchers like Gartner, Cisco, etc., believe the industrial internet to have the highest overall potential.

9. Telehealth

Telehealth, or Telemedicine, hasn’t completely flourished yet. Nonetheless, it has great future potential. IoT Examples of Telemedicine include the digital communication of Medical Imaging, Remote Medical Diagnosis & Evaluations, Video Consultations with Specialists, etc.

10. Smart Supply-chain Management

Supply chains have stuck around in the market for a while now. A common example can be Solutions for tracking goods while they are on the road. Backed with IoT technology, they are sure to stay in the market for the long run.

11. Traffic Management

Car traffic management in large cities can be greatly improved with the help of the Internet of Things (IoT).The Internet of Things helps us stay informed and improves traffic monitoring by allowing us to use our mobile phones as sensors to collect and share data from our vehicles through apps like Waze or Google Maps. This feeds and improves the data on the various routes to the same destination, distance, and estimated arrival time.

Analysis of traffic patterns over a long period is anotherIoT application. It provides an idea of what might happen during peak hours. Commuters will be better prepared to avoid traffic and delays by being made aware of possible alternatives

12. Water/ Waste Management

Many cities are adopting water recycling using water treatment units. Using anIoT application, you can see how much wastewater is being produced, how much is being consumed in a specific area, and how waste production is changing over time.

We can effectively deal with this problem usingInternet of Things applicationsand smart sensor technology. With a smart waste management system, authorities will be able to predict how much waste will be generated in a specific location, how to properly process it, when to clear it, and how to analyze data for future planning, among other things.

An overview of waste generated in every neighbourhood and how much waste is generated over time can be easily assessed using analytics solutions. This data will be used to plan the city’s expansion and upgrade projects. Smart analytics solutions can be used to manage waste collection and treatment fleets, as well as to predict future trends.

Scope of IoT

Introduction

Data Science is a vast stream and involves handling data in various ways. As a data scientist or IT professional, you should be aware of the best Data Science tools available in the market to complete your work efficiently. Do you know that the global Data Science market is expected to

Knowing how to use Data Science tools can help you build a bright and promising career in Data Science. Read on to know some of the top Data Science tools available in the market!

List of Data Science Tools

  1. SAS
  2. Apache Hadoop
  3. Tableau
  4. TensorFlow
  5. BigML
  6. Knime
  7. RapidMiner
  8. Excel
  9. Apache Flink
  10. PowerBI
  11. DataRobot
  12. Apache Spark
  13. Sap Hana
  14. MongoDB
  15. Python
  16. Trifacta
  17. Minitab
  18. R
  19. Apache Kafka
  20. QlikView
  21. MicroStrategy
  22. Google Analytics
  23. Julia
  24. SPSS
  25. MATLAB

1. SAS

SAS (Statistical Analysis System) is one of the oldest Data Science tools in the market. One can perform granular analysis of textual data and can generate insightful reports via SAS. Many data scientists prefer the visually appealing reports generated by SAS.

Besides data analysis, SAS is also used to access/retrieve data from various sources. It is widely used for multiple Data Science activities like data mining, time series analysis, econometrics, business intelligence, etc. SAS is platform-independent and is also used for remote computing. One can’t ignore the role of SAS in quality improvement and application development.

2. Apache Hadoop

Apache Hadoop is an open-source software widely used for the parallel processing of data. Any large file is distributed/split into chunks and then handed over to various nodes. The clusters of nodes are then used for parallel processing by Hadoop. Hadoop consists of a distributed file system responsible for dividing the data into chunks and distributing it to various nodes.

Besides the Hadoop File Distribution System, many other Hadoop components are used to parallelly process data, such as Hadoop YARN, Hadoop MapReduce, and Hadoop Common.

3. Tableau

Tableau is a data visualization tool that assists in decision-making and data analysis. You can represent data visually in less time by Tableau so that everyone can understand it. Advanced data analytics problems can be solved in less time using Tableau. You don’t have to worry about setting up the data while using Tableau and can stay focused on rich insights.

Founded in 2003, Tableau has transformed the way data scientists used to approach Data Science problems. One can make the most of their dataset using Tableau and can generate insightful reports.

4. TensorFlow

TensorFlow is widely used with various new-age technologies like Data Science, Machine Learning, Artificial Intelligence, etc. TensorFlow is a Python library that you can use for building and training Data Science models. You can take data visualization to the next level with the aid of TensorFlow.

TensorFlow is easy to use as it is written in Python and is widely used for differential programming. One can deploy Data Science models across various devices using TensorFlow. TensorFlow uses an N-dimensional array as its data type, which is also called a tensor.

5. BigML

BigML is used for building datasets and then sharing them easily with other systems. Initially developed for Machine Learning (ML), BigML is widely used for creating practical Data Science algorithms. You can easily classify data and find the anomalies/outliers in the data set using BigML.

The interactive data visualization process of BigML makes it easy for data scientists to make decisions. The Scalable BigML platform is also used for time series forecasting, topic modelling, association discovery tasks, and much more. You can operate on large sets of data using BigML.

6. Knime

Knime is one of the widely used Data Science tools for data reporting, mining, and analysis. Its ability to perform data extraction and transformation makes it one of the essential tools used in Data Science. The Knime platform is open-source and free to use in various parts of the world.

It uses ‘Lego of Analytics,’ a data pipelining concept for integrating various components of Data Science. The easy-to-use GUI (Graphical User Interface) of Knime helps perform data science tasks with minimum programming expertise. The visual data pipelines of Knime are used to create interactive views for the given dataset.

7. RapidMiner

RapidMiner is a widely used Data Science software tool due to its capacity to provide a suitable environment for data preparation. Any Data Science/ML model can be prepared from scratch using RapidMiner. Data scientists can track data in real-time using RapidMiner and can perform high-end analytics.

RapidMiner can perform various other Data Science chores like text mining, predictive analysis, model validation, comprehensive data reporting, etc. The high scalability and security features offered by RapidMiner are also remarkable. Commercial Data Science applications can be developed from scratch using RapidMiner.

8. Excel

Part of Microsoft’s Office tools, Excel is one of the best tools for Data Science freshers. It also helps in understanding the basics of Data Science before moving into high-end analytics. It is one of the essential tools used by data scientists for data visualization. Excel represents the data in a simple way using rows and columns to be understood even by non-technical users.

Excel also offers various formulas for Data Science calculations like concatenation, find average data, summation, etc. Its ability to process large data sets makes it one of the critical tools used for Data Science.

9. Apache Flink

It is one of the best Data Science tools 2020/2021 offered by the Apache Software Foundation. Apache Flink can quickly carry out real-time data analysis. Apache Flink is an open-source distributed framework that can perform scalable Data Science computations. Flink offers both pipeline and parallel execution of dataflow diagrams at low latency.

An unbounded data stream that does not have a fixed start and endpoint can also be processed using Apache Flink. Apache has a reputation for providing Data Science tools and techniques that can speed up the analysis process. Flink helps data scientists in reducing complexity while real-time data processing.

10. PowerBI

PowerBI is also one of the essential tools of Data Science integrated with business intelligence. You can combine it with other Microsoft Data Science tools for performing data visualization. You can generate rich and insightful reports from a given dataset using PowerBI. Users can also create their data analytics dashboard using PowerBI.

The incoherent sets of data can be turned into coherent sets using PowerBI. You can develop a logically consistent dataset that will generate rich insights using PowerBI. One can generate eye-catching visual reports using PowerBI that can be understood by non-technical professionals too.

11. DataRobot

DataRobot is one of the valuable tools required for Data Science operations integrated with ML and Artificial Intelligence. You can drag and drop a dataset quickly on the DataRobot user interface. Its easy-to-use GUI makes data analytics possible for freshers as well as expert data scientists.

You can build and deploy more than 100 Data Science models at once via DataRobot and can get rich insights. Enterprises also use it to provide high-end automation to their users/customers. The efficient predictive analysis offered by DataRobot can help you in making intelligent data-based decisions.

12. Apache Spark

Apache Spark is designed for performing Data Science calculations with low latency. Based on the Hadoop MapReduce, Apache Spark can handle interactive queries and stream processing. It has become one of the best Data Science tools in the market due to its in-memory cluster computing. Its in-memory computing can increase the processing speed significantly.

Apache Spark supports SQL queries so that you can derive various relationships among your dataset. Spark also provides various APIs in Java, Scala, and Python for developing Data Science applications.

13. Sap Hana

Sap Hana is a relational database management system that makes data storage and retrieval easy. It is a handy tool in Data Science due to its in-memory and column-based data management system. If a database has objects stored in a geometrical space (spatial data), it can be processed using Sap Hana.

Various other Data Science activities can be performed using Sap Hana like text search and analytics, graph data processing, predictive analysis, etc. Its in-memory data storage stores data in the main memory besides keeping it in any disk, which offers enhanced querying and data processing.

14. MongoDB

MongoDB is a high-performance database and is one of the top Data Science tools in the market. One can store large volumes of data in a collection (MongoDB documents) offered by MongoDB. It provides all the capabilities of SQL and supports dynamic queries.

MongoDB stores data in the form of JSON-style documents and offers high data replications capability. Managing Big data is much easier with MongoDB as it provides high data availability. Besides basic database queries, MongoDB can also perform advanced analytics. The high scalability of MongoDB also makes it one of the most widely used Data Science tools.

15. Python

The Data Science tools and technologies are not limited to databases and frameworks. Choosing the right programming language for Data Science is of utmost importance. Python is used by a lot of data scientists for web scraping. Python offers various libraries designed explicitly for Data Science operations.

You can efficiently perform various mathematical, statistical, and scientific calculations with Python. Some of the widely used Python libraries for Data Science are NumPy, SciPy, Matplotlib, Pandas, Keras, etc.

16. Trifacta

Trifacta is one of the widely used Data Science tools for data cleaning and preparation. A cloud data lake that includes a mix of structured and unstructured data can be cleaned using Trifacta. The data preparation process is significantly paced via Trifacta as compared to other platforms. One can easily identify the errors, outliers, etc., in the dataset using Trifacta.

You can prepare data in less time across a multi-cloud environment, too, using Trifacta. You can automate the data visualization process and data pipeline management using Trifacta.

17. Minitab

Minitab is a software package and is widely used for data manipulation and analysis. Minitab will help you in identifying trends and patterns in an unstructured dataset. The dataset which is going to be the input for data analysis can be simplified using Minitab. Minitab also helps data scientists to automate Data Science calculations and graph generation.

While using Minitab, descriptive statistics are displayed based on the entered dataset highlighting various key points in data like mean, median, standard deviation, etc. Besides creating multiple types of graphs with Minitab, you can also perform regression analysis.

18. R

R provides a scalable software environment for statistical analysis and is one of the many popular programming languages used in the Data Science sector. Data clustering and classification can be performed in less time using R. Various statistical models can be created using R, supporting both linear and nonlinear modelling.

You can perform data cleaning and visualization efficiently via R. R represents the data visually in simple ways so that everyone can understand it. R offers various add-ons for Data Science like DBI, RMySQL, dplyr, ggmap, xtable, etc.

19. Apache Kafka

Apache Kafka is a distributed messaging system used to transfer large volumes of data from one application to another. Real-time data pipelines can be constructed in less time using Apache Kafka. Known for its fault tolerance and scalability, Kafka will provide you with zero data loss while transferring data over applications.

Apache Kafka works on the publish-subscribe messaging system where publishers convey messages within topics to subscribers. The subscribers can consume all the messages in a topic via the publish-subscribe messaging system.

20. QlikView

QlikView is one of the widely used Data Science tools and is also concerned with business intelligence. QlikView helps data scientists to derive relationships between unstructured data and perform data analysis. You can also demonstrate a visual representation of data relationships via QlikView. One can perform data aggregation and compression via QlikView in less time.

You do not have to spend time determining the relationships between data entities as QlikView does it for you automatically. Its in-memory data processing provides faster results as compared to other Data Science tools in the market.

21. MicroStrategy

MicroStrategy is used by data scientists who are also into business intelligence. Besides enhanced data visualizations and discovery, MicroStrategy offers a wide range of data analytics capabilities. You can connect MicroStrategy to various data warehouses and relational systems to access data, thus adding to its data accessibility/discovery capabilities.

You can break unstructured and complex data into smaller chunks of data for better analysis via MicroStrategy. Better data analytics reports can be generated with the help of MicroStrategy, along with monitoring data in real time.

22. Google Analytics

Data scientists are spread across various industries/streams, and one of them is digital marketing. It is one of the top Data Science tools used in the digital marketing industry. A web admin can access, visualize, and analyze the website data via Google Analytics to understand the way users interact with the website.

The data trail that users leave behind while using a website can be recognized and used to generate better marketing decisions via Google Analytics. Non-technical professionals can also use it to perform data analytics with its high-end analytics and easy-to-use interface.

23. Julia

Julia is another programming language specifically designed for Data Science and is termed the successor of Python by many Data Science experts. With its JIT (Just-in-Time) compilation, Julia can match the speed of popular programming languages like C, and C++ during Data Science operations.

Julia allows you to perform complex statistical calculations related to Data Science in less time. You can manually control the garbage collection process via Julia, and you don’t even have to worry about memory management. Its math-friendly syntax and automatic memory management have made it one of the most preferred programming languages for Data Science.

24. SPSS

Researchers widely use SPSS (Statistical Package for the Social Sciences) to perform statistical data analysis. SPSS can also be used to process and analyze survey data in less time. One can build predictive models via the Modeler program offered by SPSS.

Surveys contain text data, and SPSS can determine the insights from text data in a survey. You can also create various types of data visualizations via SPSS like density charts, radial boxplots, etc.

25. MATLAB

MATLAB is one of the popular Data Science tools used by organizations/enterprises. It is a programming platform designed for data scientists and helps them access data from flat files, databases, cloud platforms, etc. You can perform feature engineering on a given dataset efficiently with MATLAB. The data types of MATLAB are designed explicitly for Data Science and reduce a lot of time in data pre-processing.

Conclusion

Data scientists use a lot of tools to reduce latency and errors while analyzing big data. The above Data Science tools list includes some of the most widely used tools in the industry.

If you aspire to become a successful data scientist, signing up for a reliable course that will equip you with top Data Science tools is a great idea!

Check out Jigsaw Academy’s Data Science program to learn more about the top Data Science tools through its seamless industry-oriented learning approach. Start learning the basics of Data Science today!

As you know, IoT is evolving and is being experimented with and used in tons of different ways than you can ever imagine. Some IoT examples can be Smart Breweries, Smart Coffee Machines, Smart Parking Facilities, Smart Supply-chain Mechanisms, and more.

Lastly, let us have a quick glance at what the statistics say about the scope of the Internet of Things. As per Fortune Business Insights, the global Internet of Things market was valued at US$250.72 billion in 2019 and is anticipated to reach US$1,463.19 billion by 2027, expanding at a Compound Annual Growth Rate (CAGR) of 24.9 per cent in the forecast period. Well, that pretty much speaks for itself. Beginners who have IoT as a career choice in mind might just be in luck. They can invest in a training program like Jigsaw Academy’s 12-week IoT Bootcamp program that offers in-person mentorship, hands-on project experience, personalized career support, and more!

Also, If you are interested in making a career in the Data Science domain, our 9-month-long (Live Online) Postgraduate Certificate Program in Data Science and Machine Learning course can help you immensely in becoming a successful Data Science professional.

Also, Read

  • How to Apply Machine Learning to IoT Data?
  • Uday Choudhary’s Experience in Building a Career in IoT Technology

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