The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it. —Mark Weiser

The Internet of Things is a step in this very direction. And like all things new and mysterious, it has its fair share of utopian and dystopian soothsayers; with an almost certain probability that neither of their deterministic predictions will completely come to fruition in the future. However, what is interesting is the common basis on which both these viewpoints have been made: increasing reliance on generation of data by machines as opposed to humans. And this is where I feel, there is a dire need of policy measures even before the IoT infrastructure becomes ubiquitous.

In this regard, I believe that going forward, data needs to be divided into two categories, based on the source of generation. It needs to be noted however, the focus should not be on who is generating the data, but on how the data is being generated. The last definition is crucial because even if in an M2M communication, the root message (primarily, the original data) is created by the User.

The two types of data classification are as follows —

Active Data – Active Data is the kind of data that is generated with the active consent of the User in the sense that the User consciously generates the data. This can be thought as akin to the User Generated Content on Facebook, Twitter, LinkedIn, or any other social media. While the nitty-gritty of the Terms & Conditions of these sites can be argued (i.e. the “fine print”, the opt-in/out-out debate, etc. ), it is safe to assume that the Users generate most of the content consciously while actively consenting the to the T&C.

Passive Data – When it comes to the Internet of Things (or indeed, as some companies like to call it, The Internet of Everything), the increasing trend will be towards data generated by machines. However, this is not where the point of contention starts; it starts from how this data is generated. And the answer to this question is the subconscious behaviour of the Users. Allow me to explain. I am quite restless by nature and take breaks from sitting in a chair after every 10—15 minutes (Imagine sitting through an entire 1-hour lecture!). Now, this is something that I do subconsciously. In a normal non-IoT connected chair, this trait of mine might not be picked up. However, in a chair that is wired to the larger IoT infrastructure and my behavioural data shared with it can generate different insights to the third parties who are constantly monitoring my movements—Is he feeling uncomfortable? Are the ergonomics of the chair not optimal for this kind of User?—The insights can be varied and at times conflicting, thereby probably leading to less than optimal results. That might be a problem.

I am not saying that the generation of subconscious behavioural data is necessarily bad. What will set its usage apart from the good and bad will be the context in which it is used (Imagine having a heart attack in the middle of the street, one would agree that subconscious behavioural data collection would be extremely helpful in such a case!). Thus, what will be crucial from a policy perspective is the ex post or ex ante evidence and to understand the context in which one should consider the former over the latter and vice-versa.

The larger IoT infrastructure is a ‘Complex System’ in the sense that it is likely to exhibit ‘Strong Emergence’—the development of behaviour at the system level that cannot be understood or described in terms of the component subsystems (Cave, 2011). IoT is foreseen primarily as making this world a more efficient place with lesser reliance of human agency of unessential and mundane aspects of their day-to-day life, thereby allowing us to be more in control of the things that might really matter to them. But, whether such a vision will be implemented even close to its form will depend mainly on the policies that will allow us to take a step back and understand the nature of data and cross-link it with the context in which they are generated. In this regard, the ‘strong emergence’ feature of IoT might compel policy makers to contextualise policies in an ex post rather than an ex ante manner, with focus being more on principles than on rules.

Models

1. Internet of Things and Data Collection – Active and Passive Internet of Things
2. Internet of Things and Data Collection – Active and Passive Data under Conditions of Regulation

Model Assumptions

1. Device_C representhose devices (or groups of devices) to which we consciously feed in data. E.g. Mobile Phones, Laptops, etc.
2. Device_Sx (where, ‘x’ is a numeric suffix) represents those devices (or groups of devices)ich monitor our subconscious data. E.g. Any device that’s connected to the IoT infrastructure like a chair.
3. Device_S1 and Device_S2 assumed to be complementary to each other. This means that the User can either use Device_S1 OR Device_S2.
4. All behavral data has been taken for the average civilian population from the website of Bureau of Labor Statistics.
5. The numbers on the Y-Axis of the graphs do not mean anything in themselves since the numeric data taken is largely assumptional. Hver, what is important to be observed is the ratio between the amount of Active and Passive Data collected.
6. The data generated by the User and collected by the devices is in bits. For the purpose of this model, I introduce a new unit of inferred information. I call it ‘info.’. This is NOT equal to the amount of bits generated. It cbe thought as the unit of the amount of inferences or insights that can be generated from the bits of data.
7. This model is a microcosm of the entire IoT infrastructure representing a User and a finite collection of devices with which he might interact and which might interact amongst themselves.