This is a sidebar to some thinking on an architecture for the Internet of Things here… – Rob
I was recently prompted to think about a Big Data problem that is in the US papers… the issues around processing US Veterans through the Veteran’s Administration (VA) bureaucracy. I imagine that there are really two problems… I will outline them… but the point will be to try to get you to think about how to use the IoT creatively to mitigate seemingly intractable problems.
One requires some obvious, if not easy-to-implement, information technology… there has to be some fool-proof way to ensure that VA staff cannot game the system and hide problems. This could be solved with an audit system that looks for anomalies in processing patterns in much the same way that other fraud detection software operates.
But fraud is not the problem… it was meant to mask the problem. The fact is that there are more Veterans requiring medical assistance than there are funds and doctors to provide that assistance. All the fraud detection would have accomplished is to show how many Veterans are going without care.
So… the IT folks at the VA are tasked with the impossible task of servicing too many Veterans with too few doctors… But military doctors are used to this issue and the word “triage” comes from the vocabulary of war… not the vocabulary of medicine.
This leads me towards the point… there are new things, in the IoT sense, coming out that can make triage of Veterans who are not hospitalized possible. Rumors are that the new iWatch contains several sensors that can monitor the pulse, temperature, and maybe more, of wearers. What the VA needs to do is immediately put an iWatch on every Veteran who applies for medical help. They then need to monitor the vitals of all of the veterans and schedule in those with the weakest signs. They need to notify individuals to come in immediately when vitals turn bad… Further, they need to track these vitals against medical records and outcomes to make the triage ever more efficient over time.
By using internet Things to triage the VA could use their limited medical resource much more efficiently… and mitigate some or all of the problem.
The IoT affords us all opportunities to change the game for our employers. There will be opportunities to disrupt some existing markets. But to take full advantage will require us to be creative and smart. I, of one, am anxious to start… Now back to considering an architecture for things.
There are so many things in the Internet of Things (IoT) that might record data into your data fabric that a new approach may be required. Let’s think about this… define some terms, and see how these terms fit into current data fabric thinking, let’s consider how they fit into a more modern logical data warehouse architecture, and let’s think about whether the IoT might push us to a different approach.
I’m not going to go overboard on terms here… But we do need to distinguish between a sensor and a processor.
To my way of thinking a sensor is a thing. It creates, but does not necessarily process, data. A sensor has some means to communicate with a processor… but if there is no significant processing on the sensor other than communications then we will suggest that there is no “processor” in a meaningful sense. Let me give you four examples:
- The first is courtesy of Ray Carnes, a chief architect at Boeing. Imagine a brake-pad in your car with 100,000 dust-sized RFID sensors randomly scattered as part of the pad. These sensors do nothing but signal on an interval that they are present. This allows a processor elsewhere to record the signals and determine how much of the brake pad has worn. If only 80,000 sensors report we can assume that 20% of the pad has worn away.
- A Nest thermostat senses movement and temperature. It uses a network-connect to send the results of this sensing to the Nest mother-ship and performs little-or-no processing on site.
- Sensors in my Audi detect rotation of the wheels. There is a network that sends the results to a small embedded anti-lock braking processor that monitors all four wheels as well as the pressure on the brake-pedal and sends signals to all five components to allow the car to brake evenly.
- There is a sensor in the screen on the ATM I used yesterday that detects that I want to request service. This user interface communicates with a powerful general processor which then communicates with the Bank mother-ship to create and process banking transactions.
This last bullet is important… any device that takes user input is a sensor with an embedded processor. It is a “thing” just like the Nest thing. Today we tend to blur the line between sensor and processor as every thing has a powerful processor onboard. The IoT will change this assumption.
A processor then, is a computer that performs some analysis on the data generated by one or more sensors. A processor may also store data… a sensor will not.
Now let’s think about how we might combine sensors and processors in an architecture. To start lets consider the context of the data the processor can use for analysis:
- If the processor has only the last data sensed we would say that the context is immediate and local to one sensor. The processor can see streamed data but can only operate on the last event. We would say that this sensor-processor configuration can provide a simple reflexive response. When you press the lock button in your car a sensor detects this event and signals to all four doors and the boot to lock it up.
- Another configuration might allow the local processor to store more context from a single sensor over a longer period of time… so the context is historical and local. In the case of the anti-lock brakes… the processor receives signals from a group of sensors and stores a very short historical context. This grouped historical context is very powerful…
- Another configuration might store the group context and then forward the event details to a bigger server that stores and analyzes a universal context of all things to look for patterns. Further, there could be a hierarchy of groups leading to a universal context.
- Finally, a server with some group context could summarize the details for that group and pass on only a summary over time up to another group server or to a universal server.
I suspect that you can see where I’m going. There is a trade-off is this picture between the advantages of pushing analytic processing close to the sensor and the associated requirement for more analytic processors, the advantages of intermediate analysis requiring more data movement but fewer analytic processors, and the advantage of a central analytic mother ship where all data is stored and analyzed. In the next version of this thread I’ll try to tease apart the trade-offs.