The Three Amigos - Internet of Things, Big Data & Cloud

The trio of IoT, Big Data & Cloud can definitely be called the three musketeers as they all well serve with the famous quote: All for one & one for all.

Big data describes large volume of data (both structured & unstructured), arrives from multiple sources at an alarming velocity, volume and variety. Meanwhile IoT – Internet of Things, is the internetwork connectivity of things like vehicles, devices, buildings and appliances etc. which are embedded with electronics and enables the things to accumulate and exchange data. The last amigo – Cloud computing is storing and accessing data & services over the internet instead of from one’s own infrastructure.

Big Data IoT’s impact

The IoT and big data are clearly intimately connected: billions of internet-connected 'things' will, by definition, generate massive amounts of data. IoT Big Data is going to redefine human lives. According to Gartner, revenue generated from IoT products and services will exceed $300 billion in 2020, and that probably is just the tip of the iceberg. Companies will be forced to upgrade their environment to accommodate the additional data volume in the big data world, which will generate the massive amount of revenue by the IoT.

To keep things simple, let us consider only a few examples of just three major areas of our lives: Health, Automobile and work.

IoT will help to handle medical emergencies efficiently and quickly. If someone at home is seriously ill, a smartwatch or even a pacemaker could identify an emergency situation by analyzing the data such as heartbeat and blood pressure real time and send an emergency notification to the server of the nearest clinic.

Each Driverless cars are anticipated to create 1Gb/sec which would equal more than petabytes per year. Big data will play a significant role in analyzing this vast amount of data which will manipulate the steering, accelerator & brakes for a safe drive. And the music system in the car can automatically play the favorite songs based on the data of preferences collected over a period of time and analyzed.                            

At work, automatic switch off of lights through sensors is very much effective nowadays and eventually the light sensors will be used to track the tons of activities we perform in the workplace. People enter the office buildings, hold meetings, have coffee & casually chat in the break room and finally leave at the end of the day. Tracking activities of the workers are not limited to the light sensors and even the keyboard or mouse could also be fitted with sensors to find out how much time you spend at your desk. The employer will want these data to be analyzed to find out the productivity of the workers.

The IoT is getting expanded in all other domains & industries as well, which leads to the exponential growth of the data for analytics.

Data collection – IoT

Companies need to collect all the data that is relevant to their business and that is a seriously challenging task because they need to filter out redundant data and also protect the data from getting attacked. 

The most common data collection tool is the sensor-fitted devices. Message Queue Telemetry Transport (MQTT) and Data Distribution Service (DDS) are two of the most comprehensive protocols. Both protocols can help thousands of sensor-fitted devices connect with real-time machine-to-machine networks. MQTT collects data from multiple devices and puts the data through the IT infrastructure. On the other hand, DDS distributes data across devices.

Big Data of IoT on Cloud

When we talk about IoT and the Data collection for the analytics, one of the first things we need to worry about is the huge, continuous stream of data hitting companies’ data storage which will be in Zettabytes in the near future. Data centers must be equipped to handle this additional load of heterogeneous data.

With regards to the direct impact on big data storage infrastructure, many organizations are shifting toward the Platform as a Service (PaaS) model instead of keeping their own storage infrastructure, which would require continuous expansion to handle the load of big data. PaaS is one of the cloud-computing solution that provides scalability, flexibility, elasticity, compliance, and a sophisticated architecture to store valuable IoT data.

 Cloud storage options include private, public, and hybrid models. If firms have sensitive data or data that is subject to regulatory compliance requirements that require heightened security, a private cloud model would be the best fit. For non-critical data, a public or hybrid model can be chosen as the IoT data storage.

Summary

The growth of the IoT with Big Data signs a new era of technology, and organizations that wish to be part of this era have to accommodate the data types and sources from multiple facets. In the near future, Brontobyte will be the measurement to describe the type of sensor data that will be generated from the IoT. Cloud computing will be playing a vital role in the efficient deployment of Big Data platforms in the cloud for the IoT applications, with its scalability & elasticity features.

And as the IoT establishing itself across almost all the domains & industries, the changes are just the beginning and there will be lot of challenges to solve like the security of this humongous amount of data.

Reference:

http://data-informed.com/the-impact-of-internet-of-things-on-big-data/

http://www.kdnuggets.com/2015/07/impact-iot-big-data-landscape.html

http://dataconomy.com/how-data-science-is-driving-the-driverless-car/