USE OF BIG DATA IN IOT
The “Internet of Things (IoT)” is a wide-ranging concept that has reconstructed the way people make interaction with each other globally. IoT refers to the interconnection between many things such as vehicles, physical devices, electrical appliances, daily appliances, and other objects too. They implant them with various network connectivity and software. The true potential comes when it is unlocked with Big Data with its potential for analyzing and processing vast databases or data. In this article, there will be a discussion based on the use of Big Data in the IoT. In the end, there will be a conclusion based on this topic.
Collection of Data and Storage in the IoT
Data Flow in the World of IoT
Data Sources: There are various sources such as environmental sensors, intelligent vehicles, smart devices, and different kinds of sensors that help to collect data for IoT. The data can be sent via protocols like CoAP, HTTP, and MQTT to the cloud edge gateway.
Data Storage: The storage layers help to store data from devices and the sensors at the cloud edge for short-term and long-term as well (Hussein, 2019). Cloud storage can manage and store data to an extent.
Data Analytics and Data Applications: Maximum institutions can use the run application of the cloud. The layers analyze the data via Machine Learning, AI, and some computer techniques. Analytics helps to make data-driven decisions based on the collected data and gives valuable insights.
IoT Critical Storage Technology
The environment: This is the very 1st challenge that comes from storing data. The modern automotive world is an example of the IoT concept with cloud servers for Data Acquisition, AI, Mining, and Storage such as IPSE, and HDFS (Deepa et al., 2022). Automotive cars are the future of the technological world.
Reliability: This is considered for solving storage problems in different applications in IoT. Validation and testing are the 2 important things for high-reliability products.
Data Integrity: This is important for time-series data to be analyzed. The time-series data gives the forecasting of the data and it is also helpful for the IoT data storage and the future.
Security: Data security and process security are crucial for IoT technology. The security should be strongly encrypted and should be stored safely with the required protocols.
Maintenance with predictive analysis and “Real-time data processing”
For magnifying the data-driven decision with maintenance, the implementation of predictive modeling is important. Real-time data is essential for applications in IoT. It enhances the cost-effectiveness and accuracy of the model.
Resource Optimization and Growth
IoT device is great for business applications and fields like agriculture, automation, smart cities, blockchain, and sustainable resource allocation.
The use of big data in IoT in this scenario would be applicable in the following.
● Data Collection
● Demand Response
● Real-time Monitoring
● Insights of Energy Consumption
● Predictive Maintenance
● Sustainability
● Integration of Renewable Energy and Resources
● Grid Optimization
Real-time data from IoT devices are used to improve energy efficiency, reduce waste, encourage sustainability, and accelerate the transition to a more sustainable and resilient energy future (Tanwar et al., 2020).
The Cases of Use of Big Data in IoT
- Big Data and IoT allow for “real-time fraud detection”, which improves security and consumer happiness. It improves the customer’s encounter through comprehension of patterns of behavior and removing roadblocks in encounters.
- Data processing velocity promotes algorithmic trading, which improves trading choices and success rates. Algorithms are used in risk management to anticipate credit ratings, assuring appropriate borrowing choices while minimizing risk.
- Big Data in Electronic medical records enhance patient care by supplying full records and allowing institutions to share data.
- E-books and apps for education that are powered by Big Data tailor learning experiences using deep learning (Amanullah et al., 2020). IoT sensors improve safety by identifying and warning of possible hazards.
- Retailers use IoT as well as big data to personalize customer visits to stores, resulting in timely discounts and incentives in the use of big data in the IoT.
- Forecasting data tailors marketing to individual client preferences, increasing sales income. Big Data forecasts audience interest, allowing for more tailored user experiences.
- Through data-driven insights, consumer acquisition expenses are reduced, and attrition is reduced.
Advantages and Disadvantages
Advantages
● To enhance the data-driven decision and helps to improve the business outcome.
● New opportunities and innovation are always welcomed in this field. This leads to product development also.
● With the help of real-time data processing, the situation changes can be quicker in the context of business.
● The use of big data in the IoT makes efficiency improve.
● Customer experiences are also enhanced by this usage.
Disadvantages
● Data privacy and security are very important concerns for the use of big data in the IoT.
● Overloading of data can create a lack of data management and it sometimes can be challenging too.
● The reliability and the quality of the data should be accurate; otherwise, the prediction of the output will go wrong.
Challenges and limitations
● Big data sources and the poor quality of data are challenging for creating proper data-driven decisions.
● Lack of efficiency in the processing of big data is very challenging and there are possibilities for the wrong predictions in the use of big data in the IoT (Alleema et al., 2022).
● Storing the massive data and database properly is also very demanding.
● Understanding the use of big data in the IoT is very important.
Future recommendations
Strong privacy and information security protections will become more important as the Internet of Things (IoT) expands. To protect sensitive information and earn consumers’ confidence, businesses should spend money on state-of-the-art authentication, encryption, and access restrictions.
By combining Big Data analytics with edge computing, we can lessen the load on the cloud and improve response times to data requests. Real-time analytics and reaction capabilities may be greatly enhanced by collecting information closer to the source.
The forecasting and decision-making capacity of the IoT will be greatly improved by the incorporation of algorithms that combine AI and machine learning with Big Data (Kumar et al., 2023). These will allow for self-driving systems, instantaneous identification of anomalies, and optimized use of available resources.
Managing the massive volumes of data produced by the IoT will need the development of strong data management structures and adherence to ethical data standards. In order to guarantee appropriate and ethical data processing, there must be unambiguous norms on data ownership, permission, and use.
Developing energy-saving technologies will be essential to lessen the IoT’s as well as Big Data’s negative effects on the planet as well as cut down on operating expenses.
It is critical to train a workforce that can tackle difficult the use of big data in the IoT problems in the Internet of Things (IoT) space. Professionals will be able to use these innovations to their full extent if they get ongoing training and education.
Conclusion
It can be concluded that IoT connects automobiles, physical devices, electrically operated everyday appliances, and other items. They install networks and software. Big Data unlocks its entire power by analyzing and processing massive datasets. Most universities can utilize a remotely operated application. ML, AI, and related computer methods examine the data. The analysis provides insights and data-driven choices. IoT technology needs data and operational security. Effectively encrypt as well as store security utilizing protocols. If the data is unreliable, the output forecast will be inaccurate. Organizations should invest in advanced authentication, encrypting, and access controls to secure sensitive data and build customer trust. The IoT’s huge data volumes need solid data management systems and ethical data guidelines.
