Data Science for Internet of Things (IoT) Applications

As we all know today’s digital world revolves around data. To deal with huge amounts of dynamic data, we adopt data science techniques with IoT devices to make lives easier and to handle scenarios taking immediate action.

Data Science for Internet of Things (IoT) Applications

In this article, we will discuss the different techniques of data science that can be used with IoT and the key applications of Data Science for IoT. Finally, we discuss the Challenges that are faced while applying Data Science to IoT applications. Let us start with “What is IoT”?

What is IoT?

With the advancement of technology, IoT is shaping the way we live our lives. IoT is a system of interrelated devices connected to the internet to transform or receive data from one to the other. Some of the examples of IoT include smartphones, smart tablets, smart watches, smart TVs, etc. The best example would be a smart home in which an AC, doorbell, Geaser, smoke detectors, and security alarms can be interconnected to each other and share data with the user over a mobile application. The user can now get detailed insights into the workings of the devices around them.

What is Data Science?

Data Science is the field of study that involves extracting knowledge and insights from noisy data and turning those insights into meaningful actions that a business/organization can take. Data Science plays a crucial role in Internet of Things by extracting knowledge and meaningful insights from huge data collected from various interconnected devices.

Data Science, Machine Learning and IoT are redefining our way of life, enabling machines to do what people once thought only humans could do. It is also revolutionizing the way we do business. Every industry and business is using data science and machine learning in some form or the other.

Difference between Traditional Data Science and IoT

The basic difference between the two types of data is that the traditional data is created by human whereas the IoT data is created by the machine. Collecting and organized data is a straightforward process in traditional data science and the content is consumed based upon the request whereas in IoT the data is continuosly pushed and seems never-ending. The continuos arriving data must be quickly analyzed and instant decisions must be taken. If the data processing is slowed down in IoT, the overall value of the data might be essentially reduced.

Data Science Techniques used in IoT applications:

1. Cognitive Computing: Advanced techniques such as Natural Language Processing, Pattern Matching and Machine learning can be used by the organizations/businesses to gain meaningful insights from huge amounts of data which cannot be done using conventional methods.
2. Real-Time Processing: The data coming from IoT devices is continous and non stop. The actions must be taken immediately based on the incoming data and a delay can cause huge loss and also a second or a minute is a huge thing.
3. Deep Learning: Various techniques of Deep Learning such as Neural Networks can be used to process unstructured data either in the form of text,image,audio or a video. Image recognition, Speech recognition, Natural Language Processing and Autonomous Decison-Making techniques can be utilized in IoT applications.
4. Big Data Analysis: Big Data alludes to a huge arrangement of data that no traditional management tool of data can deal with and permits admittance to huge data sets in real time. They can be used to gather insights from IoT data that would be very difficult to deal with traditional methods.
5. Edge Computing: Edge computing refers to processing or utilizing data at the edge of the network or closer to where its being generated which adds value to real-time processing in IoT applications. This also improves the response time and minimizes bandwith.

IoT Applications Empowered by Data Science

1. Predictive Maintenance: The algorithms of Data Science can help in analyzing the sensor data of various devices connected to IoT to predict failures and maintenance needs. This can help organizations to take precautionary measures in advance further reducing the downtime and increasing the lifespan of resources.
2. Retail Analytics: Data science can help retailers to analyze customer feedback and experiences thereby increase sales and reduce costs. Various algorithms can be used to analyze customer behavior and develop pricing strategies that generates more sales.
3. Healthcare: Smart devices such as wearable fitness trackers and healthcare monitoring systems can collect patients health related data. This data can be anaylzed using data science models and can be used by healthcare professionals to montior vital signs and predict diseases and early intervention.
4. Traffic Management: Data Science can be applied to data obtained from sensors embedded in infrastructure to manage traffic flow, improve energy consumption and also improve city services.
5. Automation: Data science can be used in smart homes to automate tasks such as electricity supplies and security based on the users preferences and patterns

Challenges of IoT Applications in Data Science

Apart from all the above applications of IoT , there are also a few challenges that has to be overcome before it becomes a perfect choice. Some of the challenges faced are listed below:

1. Data Storage and Analysis: Iot devices carry with huge amounts of data which is very expensive and challenging to store and process large volumes of data. Hence, effective solutions are needed to store and process lots of data.
2. knowledge discovery and computational complexities: As the data is massive, figuring out and understanding useful information is very hard because of its size and complexity. An action that could be taken to solve this would be putting away the data that is acquired from the working frameworks.
3. Data Analysis and Visualization: It is often challenging to apply data science methods in a secure way and also it is difficult to present the data in a meaningful way.
4. Too Much Data: Too much of data becomes overwhelming and difficult to filter out. Errors in data entry operations could lead to poor data quality. However, much categorized industry principles needs to be utilized to overcome this.
5. Balancing Scale and Speed: Data from IoT devices can become challenging to analyze in large scale environments like cloud. The cloud may not be suitable for real time processing scenarios.

Conclusion

The article covers in depth how Data Science plays an import role in IoT applications. We have discussed different Data science techniques that can be used to analyze and process large amounts of data generated dynamically by IoT devices. We then discussed how Data Science plays a crucial role in developing IoT applications in various domains such as Predictive Maintenance, Retail Analysis, Healthcare, traffic management and many more.However, there are a few challenges faced while adapting Data Science techniques in IoT applications and we have discussed those in detail.

In summary, Data Science can offer a significant impact in Constructing IoT applications. Companies can utilize these techniques in IoT to reduce labor costs, automate processes, improve operational efficiency and thereby intelligently gather insights from huge data and take meaningful actions immediately.