If the experts’ estimates regarding IoT are correct, it means that in 5-10 years there will be more than 50 billion interconnected devices in the world. And they all will generate zettabytes of data, which can be and should be collected, organized and used for various purposes. Hence the tight correlation between IoT and Big Data is hard to ignore, because IoT and Big Data are like Romeo and Juliet – they are created for each other. The unprecedented amount of data produced by IoT would be useless without the analytic power of Big Data. Contrariwise, without the IoT, Big Data would not have the raw materials from which to model solutions that are expected of it.
What are the impacts of IoT on Big Data?
The IoT revolution means that almost every device or facility will have its own IP address and will be interconnected. They are going to generate a huge amount of data, spewing at us from different sides – household appliances, power stations, automobiles, train tracks and shipping containers etc. That’s why the companies will have to update technologies, instruments and business processes in order to be able to cope with such great amount of data, benefit from its analysis and finally gain profit. The influence of Big Data on IoT is obvious and it is conducted by various means. Let’s take a closer look at the Big Data areas impacted by IoT.
Methods and facilities of Data Storage
IoT produces a great and stable flow of data, which hits companies’ data storage. In response to this issue, many companies are shifting from their own storage framework towards the Platform as a Service (PaaS) model. It’s a cloud-based solution, which supports scalability, flexibility, compliance, and an advanced architecture, creating a possibility to store useful IoT data.
There are few options of models in the modern cloud storage: public, private and hybrid. Depending on the specific data nature, the companies should be very accurate while choosing a particular model. For instance, a private model is suitable for the companies who work with extremely sensitive data or with the information which is controlled by the government legislation. In other cases, a public or hybrid option will be a perfect fit.
Changes in Big Data technologies
While collecting the relevant data, companies need to filter out the excessive information and further protect it from getting attacked. It presupposes using highly productive mechanism that comprises particular software and custom protocols. Message Queue Telemetry Transport (MQTT) and Data Distribution Service (DDS) are two of the most widely used protocols. Both of them are able to help thousands of devices with sensors to connect with real-time machine-to-machine networks. MQTT gathers data from numerous devices and puts the data through the IT infrastructure. Otherwise, DDS scatters data across devices.
After receiving the data, the next step is to process and store it. The majority of the companies tend to install Hadoop and Hivi for Big Data storage. However there are some companies which prefer to use NoSQL document databases, as Apache CouchDB and others. Apache CouchDB is even more suitable, because it provides high throughput and very low latency.
Filtering out redundant data
One of the main challenges with Internet of Things is data management. Not all IoT data is relevant. If you don’t identify what data should be transmitted promptly, for how long it should be stored and what should be eliminated, then you could end up with a bulky pile of data which should be analyzed. Executive director of Product Marketing Management at AT&T, Mobeen Khan, says: “Some data just needs to be read and thrown away”.
The survey carried out by ParStream (an analytical platform for IoT) shows that almost 96 % of companies are striving to filter out the excessive data from their devices. Nevertheless only few of them are able to do it efficiently. Why is it happening? Below you can see the statistics, depicting the main problems which most of the companies are facing with the data analysis procedure. The percentage figure points out the percentage of the respondents to the ParStream survey confronting the challenge.
• Data collection difficulties – 36%
• Data is not captured accurately – 25%
• Slowness of data capture – 19%
• Too much data to analyze in a right way – 44%
• Data analyzing and processing means are not developed enough – 50%
• Existing business processes are not adjustable to allow efficient collection – 24%
To perform the action of filtering out the data effectively, organizations will need to update their analysis capabilities and make their IoT data collection process more productive. Cleaning data is a procedure that will become more significant to companies than ever.
Data security challenges
The IoT has made an impact on a security field and caused challenges which can’t be resolved by traditional security systems. Protecting Big Data generated from IoT arouses complications as this data comes from various devices, producing different types of data as well as different protocols.
The equally important issue is that many security specialist lack experience in providing data security for IoT. Particularly, any attack can not only threaten the data but also harm the connected device itself. And here is the dilemma when a huge amount of sensitive information is produced without the pertinent security to protect it.
There are two things that can help to prevent attacks: a multilayered security system and a thorough segmentation of the network. The companies should use software-defined networking (SDN) technologies combined with network identity and access policies for creating a dynamic network fragmentation. SDN-based network segmentation also should be used for point-to-point and point-to-multipoint coding based on the merger of some software-defined networking and public key infrastructure (SDN/PKI). In this case data security mechanisms will be keeping pace with the growth of Big Data in IoT.
IoT requires Big Data
With the emerging of IoT step by step many questions arises: Where is the data coming from IoT going to be stored? How is it going to be sorted out? Where will the analysis be conducted? Obviously, the companies which will be able to cope with these issues the next few years are going to be in prime position for both profits and influence over the evolution of our connected world. The vehicles will become smarter, more able to maintain larger amounts of data and probably able to carry out limited analytics. However as IoT grows and companies grow with IoT, they will have many more challenges to resolve.
What do you think about the evolving of Big Data in IoT? Have you already experienced the challenges of Big Data in IoT? And do you have any ideas about the progressive solutions to these challenges? I’ll be happy to hear your opinion in the comments below. Please, feel free to share your thoughts.
