By Bill McCabe

Big Data and Internet of Things (IoT): Challenges and Applications

All imaginable things & sectors are becoming more innovative due to the evolution and growth of IoT, including connected cars, Smart Manufacturing equipment, smart cities and homes, and related health. The Internet of things (IoT) is an extensive network of physical devices that can collect data from the cloud, transmit data, and complete users’ tasks. It is formed by countless things equipped to collect and share data.

Big data and IoT are well on their way to their moment of glory. However, there are several quirks and pitfalls to avoid if you want to use this invention.

How to apply Big Data in IoT

Firstly, there are many ways you can use IoT big data for your benefit. Quick analysis is sometimes enough to get by. However, some beneficial results are only available after more thorough data processing.

Real-time monitoring: 

Big Data gathered by connected devices can be applied in real-time operations to track physical activities (count steps, monitor movements, and more) and measure the temperature at home or work. In healthcare, real-time monitoring is widely used for measuring heart rate, blood pressure & sugar. Real-time tracking is also used in other industries, including manufacturing (to manage production equipment), agriculture (for monitoring cattle and plants), and other industries.

Data analysis:

IoT-generated big data processing provides the opportunity to move beyond monitoring and gain critical insight from this data: recognize trends & tendencies, reveal hidden patterns, and uncover obscure facts and correlations.

Optimization & Process control:

Sensor data provides additional context to uncover complex performance problems influencing performance and streamlining procedures. 

  • Traffic Management: Tracking traffic volume on multiple dates and times to develop recommendations for traffic optimization (such as increasing the number of buses and trains during certain times, determining whether it is profitable, suggesting the introduction of new traffic light schemes, and recommending the construction of new roads to relieve congestion).
  • Retail: Personnel at the supermarket are told when certain items are almost gone so they can replenish the shelves with goods.
  • Agriculture: Sensor data determines when to water plants.

Predict ongoing maintenance: 

Data from connected devices can serve as a trustworthy source for risk prediction and early detection of potentially dangerous situations, such as:

  • Healthcare: Monitoring patient conditions and detecting risks (such as those patients who are most vulnerable to diabetes and heart attacks) will allow for prompt action.
  • Manufacturing sector: Predict machinery breakdowns.

Not all Internet of Things (IoT) solutions require big data:

Additionally, only some Internet of Things (IoT) solutions needs big data. It’s crucial to consider decreasing processing efforts for dynamic data and avoiding massive data storage that won’t require in the future.

Big data challenges in the IoT

Big data is meaningless unless it is processed to provide anything of value. Additionally, there are several difficulties related to data collection, processing, and storage.

Data accuracy: 

Even though big data is never wholly correct, verifying that the sensors are working correctly and that the information is valid and uncontaminated by various causes before beginning analysis is crucial.

Which information to store: 

Terabytes of data are produced by connected devices, making it difficult to decide which data should be kept and which should be deleted. Additionally, specific data may not seem valuable at first glance, but you might need it in the future. The problem is to do it at the lowest possible cost if you choose to store the data for the future.

Deep Analysis: 

When realizing that only some big data is significant, a new problem arises: determining when the quick analysis is sufficient and when a more thorough study can be beneficial.


There is no denying that linked devices in various industries can improve our lives, but there are also grave concerns around data security. Cybercriminals can link to industries, power plants, and data centers and steal personal information from telecom companies. IoT big data is a comparatively recent phenomenon for security professionals, and the absence of relevant experience raises security vulnerabilities.

Processing big data in an IoT solution

Data processing components in IoT systems vary depending on the characteristics of incoming data, anticipated results, and more.

Sensors that are connected to objects provide data. Any entity can be a “thing,” including a structure, an automobile, a plane, an industrial machine, or medical equipment. Data arrives either regularly or in real-time. The latter is necessary for handling situations quickly and processing data in real time.

To reduce the amount of data transported to the next IoT system, block things send their data via gateways, which ensure original data filtering and preparation.

Edge analytics: 

It is sensible to carry out data filtering & preprocessing before engaging in deep data analysis to choose the most pertinent data required for particular activities. Additionally, this step guarantees real-time analytics to identify helpful patterns discovered earlier by in-depth cloud investigation immediately.

Cloud gateway:

For fundamental protocol translation & communication between several data protocols, a cloud gateway is required. It also permits secure data transmission and compression between a remote gateway and centralized IoT servers.

In a data lake, data generated by linked devices is in its original format. “Streams” are how unprocessed data enters a data lake. In a data lake, the data is stored until it is used for business needs. A data warehouse houses cleaned and organized data.

Machine learning: 

The models are produced by a machine learning module using previously gathered historical data. These models are updated frequently (for instance, once every month) using fresh data streams. Incoming information is collected and used for model training and development. These models can be employed by process control that issue commands and alerts in reaction to new sensor data once they have been examined and approved by experts.


IoT produces big data for preventative maintenance, process optimization, real-time tracking, and analytics. Still, extracting valuable insights from vast amounts of data in different forms is challenging; you must ensure that sensors function well, data communication, and the information is processed correctly. Additionally, the question of which data is valuable for processing and storing is perpetual.

Despite the potential issues mentioned above, it is essential to remember that IoT development is gaining momentum and assisting companies from all industries in exploring new digital opportunities.

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