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Sensors and Data Streams in IoT

Internet of Things (IoT) brings a whole new world of data, real-time streaming requirements, operational difficulties, security, and a large stream of massive data that needs to be made available for use at scale. IoT devices find application in various settings- factories, industries, power plants, vehicles, etc. to name a few. These devices output massive amounts of data from the sensors they use. This data is streamed non-stop and is used for making future predictions, assess the current conditions, optimize the working, etc.

The data from the onboard sensors is based on things like humidity, temperature, air conditions, luminance, etc. The data from these sensors is used by billions of other devices, people, organizations and places. While the management of such a network has its own problems, the opportunities are abundant too.

First, let’s talk about the sensors. Sensors first appeared decades ago, as a means to detect changes in quantity and give the output as an electrical or optical signal. They have been used for many purposes and in various fields over the years, from utilities and energy, to manufacturing and industries. Now with the rise of IoT, the uses of sensors – and the data streaming from them – has diversified manifold and continues to do so. From the largest of aircrafts to the smallest of pacemakers, the data from the sensors flows from the devices to the network and back and this has made the IoT a major contributor to Big Data.

Today, organizations are investing heavily in capturing and store the data from the sensors, but it is extraction and analysis of that data which is the daunting task. To take full advantage of data streams in the IoT, organizations must understand the exploding number of ways “big” IoT data needs to be filtered, mashed up, compared, contrasted, interpolated and extrapolated. The 4 ‘V’s which need to be considered by the organization are-

  1. Volume- whether the massive amount of data being received can be accessed, stored, processed and analyzed.
  2. Variety- whether the various types of data and their formats can be managed on the fly.
  3. Velocity- whether the data can be captured and analyzed as fast as the rate at which it is being generated.
  4. Veracity- whether the data has been filtered, validated or cleansed and made trustworthy enough for use as basis of data-driven decisions.

If these conditions are suitably met by the organization, they can easily distinguish themselves from their competitors and be at the forefront of the IoT Industrial Revolution aka Industry 4.0.

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