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Internet of Things and Bayesian Networks

Originally posted on AnalyticBridge

As big data becomes more of cliche with every passing day, do you feel Internet of Things is the next marketing buzzword to grapple our lives.

So what exactly is Internet of Thing (IoT) and why are we going to hear more about it in the coming days.

Internet of thing (IoT) today denotes advanced connectivity of devices,systems and services that goes beyond machine to machine communications and covers a wide variety of domains and applications specifically in the manufacturing and power, oil and gas utilities.

An application in IoT can be an automobile that has built in sensors to alert the driver when the tyre pressure is low. Built-in sensors on equipment's present in the power plant which transmit real time data and thereby enable to better transmission planning,load balancing. In oil and gas industry, it can help in planning better drilling, track cracks in gas pipelines.

IoT will lead to better predictive maintenance in the manufacturing and utilities and this is will in turn lead to better control, track, monitor or back-up of the process. Even a small percentage improvement in machine performance can significantly benefit the company bottom line.

IoT in some ways is to going to make our machines more brilliant and reactive.

According to GE, 150 Billion dollars in waste across major industries can be eliminated by IoT.

There can be questions that how is IoT different from a SCADA (supervisory control and data acquistion) systems which gets extensively used in the manfucturing industries.

IoT can be considered to be an evolution on the data acquisition part of the SCADA systems.

SCADA has been basically considered to be systems in silos with the data accessible to few people and not leading to long term benefit.

IoT starts with embedding advanced sensors in machines and collecting the data for advanced analytics.

As we start receiving data from the sensors , one important aspect that needs all the focus is the data transmitted correct or erroneous.

How do we validate the data quality.

We are dealing with uncertainty out here.

One of the most commonly used methods for modelling uncertainty is Bayesian networks.

Bayesian network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph.

Bayesian networks can be used extensively in Internet of things projects to ascertain data transmitted by the sensors.