The Internet of Things (IoT) is understood and perceived by many as the use of internet enabled devices that can be connected to the world wide web so that you can monitor and control “things” remotely through your smart phone or other mobile devices. Typical applications are house temperature monitoring and control, remote programming for recording of television programmes and managing personal comfort settings in motor vehicles.
The other view of the IoT is very different, it is about using connected devices to gather information. The information that is collected is analysed (by applying techniques such as predictive analytics) and used to understand the behaviour of the thing being monitored and can, as a result, be used to control other things . Companies such as Facebook, Amazon and Google are masters at this (ref Sterling, The Epic Struggle of the IoT).
What is happening now is that ideas described above are being combined. The information that is being generated from the “Things” that we use is being analysed and used to control and/or improve the functioning of the “Thing” or some related service. These concepts are now being applied within industrial domains and across local/national governments as well as the consumer. Organisations have realised that the information that now own has real value that can be monetised in ways that they never considered before.
Developers are creating IoT based solutions without thought as to how they can be used in the bigger picture, in effect creating solutions for problems that do not exist yet and without considering the true business value that they can bring. Suddenly the IoT has become a much larger wilder space and it is filled with danger (hence the Badlands of the title).
A good example of the Industrialised IoT (IIoT) is the real time capturing of data from vehicles and then using this data to improve the development process and response to potential customer issues. The data being captured is not just vehicle speed or braking, but devices are monitoring many different aspects of the vehicles behaviour. In this instance, capturing engine noise, doing some inline spectral frequency analysis and then sending it into the cloud to be further analysed on a server to identify engine faults. If an engine fault is identified a number of things could then happen, depending on the fault.
- A communication is sent by text message or displayed in the vehicle itself to notify the user to present the vehicle at a garage.
- The analysis identifies that the vehicle could be repaired by changing a software parameter so it updates the vehicle when it is parked.
- The analysis becomes part of a wider picture, identifying a trending issue in the vehicle and other vehicles. This could trigger a recall and a also raise an defect request that needs to be addressed with the next iteration of the vehicle.
This is just one of many examples of the industrialisation of the IoT and shows the power of what is now possible and also highlights some of the dangers. Issues such as Security of the vehicle and information, timely and lossless transmission of data (both directions), ensuring that the analysis is correct, managing the amount of data (many gigabytes per hour) etc.
For this to work successfully it needs an approach that goes beyond the way that the early IoT applications were developed. The current approach is that devices are developed that tap into existing systems and we have been controlling them ourselves via mobile devices. This is a very unregulated world.
An example that starts to show the true complexity and scope of IoT development is a washing machine that turns itself on at night when there is a low electricity tariff. On the face of it this sounds like a simple application. The reality starts to come when you scale this upto a city of say 10 million people, this could lead to 2-3 Million washing machines that could all come on at 3 AM in the morning causing a huge draw on the electricity resources. If the power company could capture the location of the machines that were going to be turned on then it could potentially schedule them to run to even out and regulate the available supply. We have gone from a simple washing machine to something that is affecting the management and control of power distribution for an entire city.
To be able to create an IoT based system to implement the above example requires a much more discipline approach than what is currently being done. This is going far beyond simple IoT applications. It needs frameworks and standards for how these systems are specified, designed and developed. Because of the size of the infrastructure and complexity required to sustain a system such as that described above it really needs to be developed using a Enterprise Architecture and Systems Engineering approaches based on Services.
The organisation that is currently doing a lot of work in this area is the Industrial Internet Consortium (IIC), a group of companies who are coming together (under a year old, already 100+ members) under the leadership of IBM, Intel, GE, Cisco and AT&T to define the way forward for the IIoT.
Part of the work of the IIC is to define a reference architecture and a framework to support the design and development of IIoT architectures and application. This should help to tame the “Bad Lands” of the IIoT as it provides a means to:-
- Capture common business models;
- Usage scenarios of where and how IoT technology is expected to be used
- Common functional behaviour
- Identify potential technical implementations for the functional behaviour
The framework consists of 4 views,
- A business view, to capture the motivation behind the vision and enable business leaders to rationalise decisions behind the need for an IoT based solution and identify the capabilities needed to support the vision.
- A Usage view, used to capture typical roles, activities and scenarios to help understand how people and legacy systems would expect to interact or use the IoT based solution.
- A functional view, used to capture the functional or logical architecture that could be used to specify the systems.
- An implementation view that provides guidance to the sort of technology that could implement the system.
The nature of these large IoT systems is that they need to be highly adaptable and tolerant of changes in technology, so when it comes to architecting such a system the one best ways to consider specifying it is by using a service based approach.
So how can this framework be implemented ? Currently no tool maps directly onto the framework as it has not been formalised as a concrete metamodel. But the principles behind it are rooted in MODAF and DoDAF. It is a layered approach that maps very well onto the MODAF/DoDAF viewpoints and the traceability between the viewpoints. Also many of the elements identified in the IIC framework resonate closely with elements in MODAF/DoDAF, from Vision, Goals, Capabilities, through to Roles, Activities, Services etc thus giving us a means to understand these systems using current technology and thinking. We do not need to reinvent the wheel yet again.
To find out more come to the Integrated EA 2015 Conference.