IoT in Healthcare Use Cases eBook
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Last time I introduced the idea that the evolution of the Internet of Things will follow a clearly defined path as the value and complexity of IOT offerings increases over time. I also suggested that IOT itself was just a natural extension of the Machine to Machine (M2M) technologies that have been steadily developing since Brian Kernihgan and his team at Bell Labs typed those immortal worlds “hello, world” back in 1974 and kick started the era of the connected programmable computer.
Obviously none of this is exactly breaking news so apart from contributing to my case for winning next years Nobel Prize for ‘Stating the Blatantly Obvious’ I’m probably going to need to pad this theory out a bit to keep you all reading.
So here goes.
Traditional M2M communication has been a pretty synchronous affair with event captures (temperature, inventory levels, door activations etc) being relayed to a central point for processing and analysis and maybe in some more ‘hi-tech’ setups some form of outbound action kicked off to reset a trigger, open a valve or as in a particularly brilliant use case I saw a few years ago boil a kettle (long story). However, use cases are evolving now that are pushing this basic technology to its limits and now standard Machine to Machine communication is giving way to the more intelligent and connected concept of IOT.
Take for example a simple location sensor attached to bus on a city street. In the world of M2M this sensor may have triggered an event each time it drove through a ‘gateway’ on certain locations around its route. This data could have been transmitted to a central point and a rough estimate of its arrival time at the next stop could be ascertained. Nice, simple, but synchronous use of basic technology. Now let’s fast forward to 2014 and the world of IOT and imagine a similar, but slightly more intelligent sensor, that’s just a little more connected. This sensor isn’t dumb so it can do far more than just trigger a location event, it can now tell the ‘gateway’ when its approaching, it can tell it how fast its going, what its average speed has been since the last stop, how many other sensors it has passed, how many empty parking spaces it saw and most importantly what passengers are on the bus and what their typical travel patterns are so we can decide if we need to delay the departure of the next bus so that these guys can catch it. It can even receive data from the gateway (or a centralized site) telling it what traffic is like ahead, how many people are waiting at its next stop and if the bus behind it has been delayed. This is amazing information for the driver, the passengers, the route planner, the city authorities and me sitting in my office so I can figure out exactly when I have to leave so I don’t end up standing in the rain or missing my ride home.
In the world of M2M we knew where the bus was and that was about it. It was a nice piece of information to have but in reality that bus could have been just about to hit a serious traffic bottleneck and any data we sent would become completely redundant. Now with the added intelligence provided by IOT technology we have similar priced sensors that provide a more holistic view of our bus and in turn a whole lot more value.
However, its not just about the sensors.
Even though I could write a whole blog about the importance of intelligence at the edge in a successful IOT installation (and probably will at some stage soon) the real differentiation between M2M & IOT is in what we do with this data after we collect it and this is where the cloud based management and analytics of the IOT world start to come into play.
In my old M2M analogy another functions of those location gateways was to let the bus communicate with a set of traffic lights to ‘prioritize’ its travel ie. “I have 40 passengers onboard, please change your light to let me through”. This was a neat function and I’ve seen it promoted in the past as a ‘part of a major traffic management policy’ in some cities around the world but in reality it’s deeply flawed. Just because I have more people in my bus than you have in your car does that mean I should get priority ? What else happens if I switch that light, does it block another bus further back in the line that has 50 passengers, what about an ambulance? Maybe it just slows down a line of cars coming out of a tunnel that creates a much bigger bottleneck in another part of the city. So we were actually making small decisions that could have big consequences based on lots of single data points around very busy city streets, WHAT WERE WE THINKING !!!!
Today however, with the power of the Internet of Things behind us we can proactively manage the situation with remarkable accuracy. We can decide when and where to give priority based on real data from all across the city, we can slow down, re-route or even stop buses traveling into certain areas of the city while simultaneously re-deploying other vehicles to take up the slack. We can reprogram light sequences, push information to street signs or reduce or remove parking costs on certain streets in order to ‘guide’ drivers into less congested areas. All of this can be done because we have the raw data to make decisions that we never could have gotten in the old synchronous world of M2M and we have the ability with platforms like Davra IoT AEP to analyse and react to that data in real time.
Absolutely not. The world of M2M has just evolved into IOT, like terminals, evolved into PC’s which in turn evolved into laptops, tablets, smart phones and now to wearable’s. Just like in the world of personal computing, IOT Phase 1 is just a logical transition, the next step IOT Phase 2 – CHARGE (the introduction of pay per use), is where things start to get really interesting.
Tune in next week too see why…
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