How Do We Use IoT to Collect Data for Big Data Analysis and Machine Learning?
The Internet of Things is a device that can transmit data over a network with minimal human intervention. IoT devices can be divided into three parts.
1. Things that collect information and send it
Some devices have built-in sensors and are used as temperature sensors, motion sensors, air quality sensors, soil moisture sensors, etc. These sensors along with connectivity help us automatically collect data from the environment in which they are located.
2. Things that receive data and act on it
You must have seen machines and devices that acquire data and then act on it.
3. Things that can do both of these tasks
After looking at these two categories of devices, we move on to another category of devices that can receive data, process it, and send it over a network.
What is big data?
Big Data is a term that refers to a massive collection of structured and unstructured data that is very difficult to process with traditional techniques. However, it is important to analyze business data to gain actionable insights to help you take strategic business actions.
There are many tools used by data analysts to generate useful information from unorganized data.
Why use machine learning for IoT?
IoT and machine learning provide insights otherwise hidden in data for fast, automated responses and better decision-making. Machine learning for IoT can be used to project future trends, detect anomalies, and augment intelligence by ingesting images, videos, and audio.
Machine learning can help demystify hidden patterns in IoT data by analyzing huge volumes of data using sophisticated algorithms. Machine learning inference can supplement or replace manual processes with automated systems using statistically derived actions in critical processes.
Machine learning for IoT can be used to:
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Process and transform data into a consistent format
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Build a machine learning model
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Deploy this machine learning model to the cloud, edge, and device
For example, using machine learning, a company can automate quality control and defect tracking on its assembly line, track asset activity in the field, and predict consumption and demand patterns.
What is Big Data Analytics?
Big data analytics is often the complex process of examining big data to uncover informationsuch as hidden patterns, correlations, market trends, and customer preferencesthat can help organizations make informed business decisions.
On a broad scale, data analytics technologies and techniques provide organizations with a way to analyze data sets and gather new information. Business Intelligence (BI) queries answer fundamental questions about business operations and performance.
Big data analytics is a form of advanced analytics that includes complex applications with elements such as predictive models, statistical algorithms, and what-if analyses used by analytics systems.
What is the relationship between big data analytics and IoT?
Various techniques are used to collect and store data. One of the main sources of data collection is IoT devices. These devices have built-in sensors that collect data from the environment in which they are located. Collected valuable data is transferred to the cloud via the Internet.
These piles of data are referred to as big data, where artificial intelligence and machine learning are used to generate useful information.
How do IoT and Big Data interact?
IoT and big data have a mutual relationship and influence each other significantly. As IoT grows, it creates a demand for big data capabilities. The increase in the amount of data every day requires more advanced and innovative storage solutions, resulting in an update of the organization’s big data storage infrastructure.
Big data and IoT have a closely linked future. These two areas will bring new solutions and opportunities that will have a long-term impact.
What is the role of Big Data Analytics in IoT?
We have seen that smart devices are important components of IoT, these devices generate a huge amount of data that needs to be explored and researched in real-time. This is where predictive and Big Data Analytics come into play. In addition, big data analytics tools use IoT for easy control, but they also show some problems. Big data is evident in IoT due to the huge deployment of sensors and things that can be used on the Internet.
Also, data processing in big data faces challenges due to short computing, network, and storage resources at the end of IoT devices.
The processing of big IoT data takes place in four consecutive steps. A group of unstructured data is generated by IoT devices and stored in a big data system. A big data system is a shared distributed database where a huge amount of data is stored. The stored data is analyzed using analytical tools such as Hadoop MapReduce or Spark
Then generating reports on the analyzed data.
When the entire IoT system acts as a source of generated data, the role of big data analytics in IoT becomes essential, big data analytics is an emerging tool to analyze the data generated by the connected device in IoT to help take the lead in improving decision making. creation.
Large amounts of data are collected in real-time and stored using various storage techniques such as Microsoft Azure and can be processed as part of a big data process. Below are the steps that are considered for data processing:
IoT-connected devices generate a huge amount of heterogeneous data, which is stored in a big data system on a large scale. This big data produced by IoT strongly depends on the 3V factors or characteristics of big data which are volume, velocity, and variety.
A big data system is a shared and distributed database, so a large amount of data is stored in big data files in a storage system. Interpret and explore collected IoT Big Data using advanced analytics tools like Hadoop, Spark, etc.
View and generate descriptions of researched data for accurate and timely decision-making.
Challenges in IoT with Big Data Analytics
The rapid growth of various applications in IoT also leads to various challenges such as data storage and management: The data produced by Internet-enabled devices is constantly expanding, and the storage capacity of the big data system is limited, so the storage and management of such a large amount of data become the most important challenge. It is necessary to design some mechanisms and frameworks for collecting, storing, and processing this data.
Data visualization:
We already know that generated data is heterogeneous. i.e. structured, unstructured and semi-structured in different formats, making it difficult to directly visualize this data. It is necessary to prepare data for better visualization and understanding for accurate industrial time decision making and improving industrial efficiency. You can also learn about the types of data visualization in Business Analytics.
Confidentiality and privacy:
Every smart object in a globally connected network represents an IoT system that is mainly used by humans or machines, which increases the focus on privacy and information leakage. Thus, this key data should be confidential and provide privacy as the generated data contains users’ personal information.
Integrity: Connected devices are adept at sensing, communicating, sharing information, and performing analytics for various applications. These devices ensure that users do not share their data indefinitely, data collection methods must successfully deploy scope and integrity conditions with certain standard practices and rules.
Power Capture:
Internet-connected devices should be connected to an endless power source for smooth and uninterrupted functioning of IoT operations. These devices are limited in terms of memory, computing power, and performance, so the devices must be deployed with lightweight mechanisms.
Apart from these big challenges, big data analytics also faced other huge challenges like securing devices and backup against attacks as these are the most obvious tools for attacks and provide a gateway for nefarious activities.
Easy availability of these devices is another challenge, the devices must be reliably available due to their critical application nature such as smart homes, smart cities, smart industries, etc.
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