Understanding the nature of IoT data

This post is in a series Twelve unique characteristics of IoT based Predictive analytics/machine learning .

I will be exploring these ideas in the Data Science for IoT course /certification program when it’s launched.

Here, we discuss IoT devices and the nature of IoT data

Definitions and terminology

Business insider makes some bold predictions for IoT devices

The Internet of Things will be the largest device market in the world.

By 2019 it will be more than double the size of the smartphone, PC, tablet, connected car, and the wearable market combined.

The IoT will result in $1.7 trillion in value added to the global economy in 2019.

Device shipments will reach 6.7 billion in 2019 for a five-year CAGR of 61%.

The enterprise sector will lead the IoT, accounting for 46% of device shipments this year, but that share will decline as the government and home sectors gain momentum.

The main benefit of growth in the IoT will be increased efficiency and lower costs.

The IoT promises increased efficiency within the home, city, and workplace by giving control to the user.

And others say internet things investment will run 140bn next five years

 

Also, the term IoT has many definitions – but it’s important to remember that IoT is not the same as M2M (machine to machine). M2M is a telecoms term which implies that there is a radio (cellular) at one or both ends of the communication. On the other hand, IOT means simply connecting to the Internet. When we are speaking of IoT(billions of devices) – we are really referring to Smart objects. So, what makes an Object Smart?

What makes an object smart?

Back in 2010, the then Chinese Premier Wen Jiabo once said “Internet + Internet of things = Wisdom of the earth”. Indeed the Internet of Things revolution promises to transform many domains .. As the term Internet of Things implies (IOT) – IOT is about Smart objects

 

For an object (say a chair) to be ‘smart’ it must have three things

-       An Identity (to be uniquely identifiable – via iPv6)

-       A communication mechanism(i.e. a radio) and

-       A set of sensors / actuators

 

For example – the chair may have a pressure sensor indicating that it is occupied

Now, if it is able to know who is sitting – it could co-relate more data by connecting to the person’s profile

If it is in a cafe, whole new data sets can be co-related (about the venue, about who else is there etc)

Thus, IOT is all about Data ..

How will Smart objects communicate?

How will billions of devices communicate? Primarily through the ISM band and Bluetooth 4.0 / Bluetooth low energy. Certainly not through the cellular network (Hence the above distinction between M2M and IoT is important). Cellular will play a role in connectivity and there will be many successful applications / connectivity models (ex Jasper wireless). A more likely scenario is IoT specific networks like Sigfox (which could be deployed by anyone including Telecom Operators).  Sigfox currently uses the most popular European ISM band on 868MHz (as defined by ETSI and CEPT), along with 902MHz in the USA (as defined by the FCC), depending on specific regional regulations.

Smart objects will generate a lot of Data ..

Understanding the nature of IoT data

In the ultimate vision of IoT, Things are identifiable, autonomous, and self-configurable. Objects  communicate among themselves and interact with the environment. Objects can sense, actuate and predictively react to events

Billions of devices will create massive volume of streaming and geographically-dispersed data. This data will often need real-time responses. There are primarily two modes of IoT data: periodic observations/monitoring or abnormal event reporting. Periodic observations present demands due to their high volumes and storage overheads. Events on the other hand are one-off but need a rapid reponse. If we consider video data(ex from survillance cameras) as IoT Data, we have some additional characteristics.

Thus, our goal is to understand the implications of predictive analytics to IoT data. This ultimately entails using IoT data to make better decisions.

I will be exploring these ideas in the Data Science for IoT course /certification program when it’s launched. Comments welcome. In the next part of this series, I will explore Time Series data