Ambient Computing
What is Ambient Computing or Ambient Intelligence?
IoT/ Ambient Computing/ Intelligence were terms that were referred to with recent discussions within my peer network. Which triggered various ideas and concepts of how all of these relate to each other. Their application/ intention of use and how theory can be put to practical use were some of the questions which I started to research on. The first point of call was to get a definition around the question at hand, the one which was the most apt for this can be summarized as:
“Ambient intelligence(computing) and ubiquitous computing characterize intelligent, pervasive and unobtrusive computer systems embedded into human environments, tailored to the individual’s context-aware needs.”[1]
If we were to contemplate on that definition for an adequate amount of time, it effectively means having a set of automated system(s) responding to an event(s) and re-establish the system to normal operational levels or to raise an alarm if normality cannot be restored. For example
And many more examples in the middle that we can think of. The next thing to build on this will be a framework that can be the right balance between theory, architecture, and business.
Proposed Framework
Taking it back to basics the four distinct levels of processes within this space are
1. Monitor (M) – Keep sensing for steady state of the system.
2. Action (A) – Perform a task when steady state not maintained.
3. Response (R) – A loop between action, response and maintain.
4. Stabilize (S) – Final resting state of system.
Thus, the ambient intelligence (AMi) reference framework consists of these four proposed states of Monitor, Action, Response and Stabilize (MARS). Once the MARS actions are completed either the system will be back to functioning within defined parameters or an escalation for manual intervention is initiated. The manual intervention can further be automated to provide further levels of AMi to the systems in place.
Recommended by LinkedIn
This framework and its way of working is good from a theoretical point of view, however, to bring it to life and practically how it will operate is something which I will briefly touch upon
Theory to Practice
A business model[3][4] (as per Gassmann et al., 2014) consists of several essential element surrounding the “Who, What, How and Why”. Wherein “Who” refers to the target customer, “What” is the value proposition, “How” is the delivery of the value proposition and the “Why” defines the underlying economic model.
If we were to refer the AMi framework proposed it tries to answer the key questions around the business model and how one can approach to solving various challenges and interesting, use cases across industries.
Where does IoT, AI, ML and other fit?
In theory having a sensor generating data is a pointless thing to do unless this data can be action-ed upon or there is a need to perform some analytics on this data in future. Therefore, IoT will be a subset of the wider AMi pool of use cases. To highlight this, it will look something along the lines of the Venn diagram shown (consulting cannot be completed without one of them!). Although this isn't detailed out, we can add additional bits like AR/VR and other enablers to build the AMi eco-system for specific use cases
Thus, to summarize Ambient Intelligence/ computing is IoT in action across a single sensor or a vast network of devices to achieve the goals of a tailored, context-aware individual needs.
[1] Bick, M., Kummer, T.F. (2008). Ambient Intelligence and Ubiquitous Computing. In: Adelsberger, H.H., Kinshuk, Pawlowski, J.M., Sampson, D.G. (eds) Handbook on Information Technologies for Education and Training. International Handbooks on Information Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74155-8_5