The Reality Of IoT vs. The World of Connected Things: The Same Or Different?


This week I had the pleasure to speak at the MIT Enterprise Forum annual conference on Connected Things. The MIT Media Center was buzzing with vibrant conversations about Connected Things, IoT, RFID, AR/VR, and more. Great debates everywhere! So here are some thoughts:

First off, let’s establish some ground rules:

  • 1.‘Connected World’ implies ‘things’ that are connected to ‘something’ via the Internet. There may or may not be any visible outward change (e.g., action, reaction, monitoring, outcome, or behavior) at the end point. Simply connected. Connected does not automatically imply connected with a purpose. Almost everything today is Internet-enabled, but that does NOT imply meaningful or intentional connectivity. What we are looking for is ‘mindfully connected things!”.
  • 2. The Internet of Things (IoT), on the other hand, generally implies intentional and meaningful connectivity with a purpose – goal-oriented connectivity with outcomes of consequence. For instance, sensors that provide accurate information which prompt specific decisions. There is (or ought to be) mindful intent in IoT. Over the next few months, let’s explore the nuances of IoT to gain a deeper understanding.

For starters, the top level of the landscape may look as though we have five different types of IoT:

A.Industrial IoT:
This is today’s large scale use case. Billions of end points connected to a gateway (or multiple gateways). Input is sent, data is analyzed and action takes place. This involves the larger world of sensors across any and all industries providing simple to complicated input. Algorithms can run on gateways nearby a cluster of devices or, depending on the complexity of a device, can at times run on the device itself. This process applies to the industrial economy, hence Industrial IoT. As an example, sensors in farms allow us to have an accurate read of water levels, humidity, and weather patterns. This information, in turn, enables us to allocate water dispersion in the most economical and effective manner, supporting the crop to generate the best results. The impact goes beyond profit – this chain of events from data to consequences enables farms to consume water efficiently and avoid wasting this often-scarce resource.

B.The large scale, intentional connected world:
This is when things connect to each other and often implies complexity. My GPS connects to my car – which connects to my mobile – which connects to many other devices and services. Imagine how wonderful and complex the world of driverless cars and delivery drones will be. Imagine the complexity of the connections and how mindful we are going to need to be to avail ourselves of this connection wisely. In other words, tons of pre-planning is called for, and this is what I call multi-dimensional (or complex) IoT. This involves complex and integrated interconnectivity leading to the truly connected world.

C. Predictive & Cognitive IoT:
Now things get exciting. Enough with monitoring. Let’s start predicting and optimizing based on the data we have obtained. For decades, we have massaged data to obtain information (often passive information using analytics) and we have not used that data to predict and improve future results. Repeatedly looking at the same data and results (e.g., graphs, analytics) only means that I see it over and over again! Now, instead of simply getting readings from farm sensors, what if we start predicting water utilization in the future based on many sources of data which we can obtain, aggregate, and analyze. What if we start optimizing to use even less water?! This gets us into the world of Cognitive or Predictive IoT. It involves looking at many complex sources of data at the same time, as well as knowing what to look at and how to predict what simple analytics cannot show us. Imagine the consequences in power, water and energy utilization! Think of the consequences in healthcare!

D. Machine learning (self learning systems):
We hear a lot about the multi-zettabytes of data that IoT is going to generate. Simply put, we are looking at billions of bytes and an exponentially growing amount of data that humans will not be able to look through and comprehend. Frankly, unless we start predicting and learning (in other words, changing how we look at data), one thing is for certain: we will be buried under the mountains of data! We need to design systems (algorithms) to start ‘trimming’ what we look at and when we look at- based on the importance and consequence of data and hand over a ton to machine learning. Merely collecting every piece of IoT data is not the solution. Knowing what, when and which data to collect is the essential element. When you are dealing with such huge volumes of data, this is much harder and critical. So let’s start noodling on this one! (Note: the term ‘machine learning’ elicits a lot of reaction about security hacks and other concerns, so more on this soon.)

E. Social IoT or Personalization of Data:
I often use IoT interchangeably with IoD (Internet of Data). Data is the final outcome of IoT. Data is not always going to emerge from a device; rather, sometimes it is going to be from you, the human. To take a simple example: I may pick up your heart rate from your Fitbit, but I also need to know your mood, your level of energy, what you ate, your general preferences, your DNA, and much more in order to use the heart rate in a meaningful way to make a consequential decision as a doctor. Another example (retail) has to do with the personalization of data, which refers to understanding you and all your preferences. Then I can be delighted by being presented with choices I love! Hence, I shop more! Hotels, banks, insurance companies, airlines, and retailers need to understand us better and know our preferences. This is essential in order to provide meaningful impact and be able to delight us, and hence increase sales. Think about why you may like AirBnB and Uber more so than your traditional hotel or taxi? All of this requires understanding you and your data. The ‘social you’! Some data come from devices, some from social data, and some which you will need to enter from many other sources.

All of the afore-mentioned elements are interconnected. We are still in the very early stages of IoT and enabling the industrial IoT and the Connected World. In a way, I think of IT to-date as the necessary infrastructure (scaffolding and plumbing) that prepared us for this massive explosion. Think of how critical cloud computing, mobile apps, and similar technologies are to enabling the connected world. In a sense, we spent the last few decades getting ready. Now we are ready. Now the key is for us do it right!

My parting thought: IoT is highly complex. It gives rise to our connected world, one in which I hope we are meaningfully connecting Things to the Internet with great purpose, outcome, and intent. A world in which we can always improve things – and improve everything!

There is much for us to unravel and I am delighted that finally we are ready for this amazing journey…

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