Sunday 19 June 2016

Where IOT is Going Wrong

Where is IOT Going Wrong ?
 We’ve all seen the predictions. Apparently IOT will be bigger than sliced bread, Ben Hur and everything in between. My earlier blog referred you to the hype curve and explained some realities but let me expand on why I think many companies will go broke or simply fizzle out. It boils down to this:

No-one is thinking clearly about the problems IOT should really be solving and how.
It comes back to the definition of IOT: what is it really? Once you strip out web sites, remote applications, phones, tablets, email and server to server functionality you are left with the “things” that IOT is really referring to - remote monitoring of small or single purpose devices. And this is where the concentration of IOT development is taking place. Let me explain where I believe it has all gone badly astray. There are four key problems I would like to explore with you:
·         An over emphasis on the value of LoRa
·         An inadequate understanding or visibility of true cost
·         Little genuine perception of IOT framework complexity
·         A complete lack of genuine applications making sense of it all
1.            The Inadequacy of LoRa
I almost wish no-one had thought of this: low powered WiFi devices that send tiny bits of data on an infrequent basis. Almost useless. 12 byte messages and 144 per day limits just won’t cut it for 90% of the remote monitoring requirements out there. There is no alert immediacy possible, no genuine data trending possible and no aggregation of multiple sensors possible. We can’t send commands back to the device or sensor and we can’t put intelligence at the point of collection. Even in agricultural scenarios, there are very few practical uses for this. You might think soil moisture or silo levels might be a viable sensor usage for LoRa but this only makes sense if you have software to aggregate and manage the readings off line. But it is useless for weather stations, water monitoring, hot house monitoring, stock monitoring and almost everything else. It is no good for building event monitoring, patient and medical alerts and just about any machine monitoring. LoRa really isn’t a useful data transmission methodology except in all but a very few specialist areas. Keep clear of it unless you really know how you are going to deal with the lack of data flow. Please make sure you know what LoRa solutions will NOT do for you.
2.            Nobody Told Me It Would Cost this Much!
The cost of solutions I see being constructed and pedalled over the web is going to horrify people and scare off any genuine take-up. Part of this is because software vendors are wrapping up solutions independently to hardware vendors. This always means two people need a cut and you will have two sides of the equation to deal with. But, did they tell you what the monthly costs will be? There are going to be Telco bandwidth charges and then Azure (or Amazon or Google or…) framework charges. Just dealing with data is fraught. 3G/4G data costs are still expensive the world over and if your solution doesn’t act nicely over the link, you will be up for data costs you didn’t expect. If you start from the software side (someone selling you a data solution), make sure you understand the cost of sensors and probes. Most websites will not detail all this up front because they know you will panic when you see the numbers.
3.            The Complexity is not for the Feint Hearted
If you are doing this yourself then be ready for the complexity and cost of collecting data for display. Making 24/7 auto scalable solutions is not easy on any of the Big Three platforms, despite the spin and hype they throw at you. For what it’s worth, only the Microsoft platform comes close to offering an API approach but this aspect of the industry is very immature. The platforms are not stable and their APIs have significant bugs. Your client is not going to listen to you telling him that Microsoft broke your connection last night and that’s why you didn’t get 12 hours of important data. The other complexity is having the software cooperate with the hardware in order to yield meaningful stats and alerts. Unfortunately, it requires a symbiotic relationship between the managing and reporting software and the hardware offering up the data. You simply will not be able to pick and choose from different vendors. This will dramatically complicate the design and installation of IOT systems.
4.            Where are the Apps!
This is my big question. Right now, they just aren’t out there. Basically we’ve put the cart before the horse. We need to know what we want and why before we go around spending millions on solutions to nothing. Perhaps if you want refrigeration alarm panels you can find something but what about all those supposed agricultural solutions? Where are they? What about those alarm monitors? There’s basically nothing out there yet so it is no point getting excited about a data collection capability until you have something that can deal with that data. Sounds simple, doesn’t it? Forget about the glitzy examples. Apparently there is an app out there that analyses driving behaviour and rates you against your friends. Duh! Well that’s going to save industry millions …not! So then the problem is that no-one is building practical apps that are solving actual problems and so saving people money or risk. This is a bit of a problem.
In summary, the IOT industry is busy tearing itself apart to be the first out there with really poor solutions. The customers are waiting for someone to build meaningful apps and no-one wants to tell the truth about cost less they scare them all away. We’ve got a long way to go…

Geoff Schaller

Tuesday 14 June 2016

The Cost of IOT - What is involved

The Cost of IOT – What is involved

 Aside from capability, one of the big ticket items that will hold IOT back is the cost. All the hype in the world won’t propel IOT to stardom if the cost does not match the delivery benefit. And right now, no-one seems to be talking cost so we will lay out some of the components for you to consider. But as you will see below, don’t start asking about cost until you know your provider understands the business end to end.

1.            The Sensor
Most unfortunately, this is the murkiest of corners in the IOT space. You can get cheap one-wire based temperature sensors for $5 but it takes an electrician (or experienced data cable handler) to wire it in. $65 gets you a light bulb monitor but there are a lot of light bulbs to hook up. A decent soil moisture probe can cost anything from $400 to $2000. Measuring temperature, humidity, pH or water quality is similarly variable except now the price range is $5 to thousands. Setting up a door switch has no sensor cost but probably an hour’s labour to fit.
The further problem is that if you use LoRa fitted transmitters, you may be constrained to purchasing from a limited range of compatible devices and there is often a price penalty for this. Non-conforming devices are still possible but you’re up for an installation charge. When it is LoRa there is a per sensor charge which might be $150/year. If you use 3G/4G connectivity, there is usually a single $150/year charge which can cover up from one to 20 or more sensors. Those sensors will mostly incur an installation charge but not an additional monthly charge.
We can leave the discussion around sensor technology to another day but suffice to say there are four main types: digital, one wire, low res analogue and hi res analogue. The technology behind the brain dictates which of these technologies are possible but also the technology of the remote sensor plays a huge part. Cheap sensors are only possible where the main controlling unit allows them to be connected and the right I/O technology is in place to utilise them. So when you are discussing your solution with your IoT provider, you need to know what sensors they are proposing for you, what they will cost and if they come with monthly charges.
2.            The Brain
Almost all IOT implementations have some central controller. Whether it is LAN, LoRa or 3G based, there is always a cost in the order of $2000 per year. Sometimes this is a monthly based charge where you never own the unit and others are outright purchase. The LoRa brains communicate wirelessly to their sensors whereas the 3G ones are mostly hard wired up. In something like a hothouse where 20 sensors are common, it is unlikely to be serviced by LoRa technology. Grain silos separated by distance might be more economically connected via LoRa.
One of the key attributes of the brain is its capacity to run code and here is where there is a massive difference in capability. With Yun or Arduino combinations, the code base possible is extremely limited because the memory space is limited – 256KB. The code, often C based, is very limited. More complex boards such as the Raspberry PI and DragonBoard allow the deployment of a “full” operating system such as Linux or Windows 10 IOT Core. They have up to 1GB of on-board RAM and now permit the execution of complex code. This allows us to contemplate pseudo sensors such as airlocks or pressure differential sets and proactively manage outgoing data streams. Assuming there are applications written to exploit the bigger operating system, there is a clear advantage with Dragon Boards or Raspberry PI but there will also be a higher cost.
3.            Installation Costs
We can’t escape this and essentially is a standard electrician’s rate. The only difference between a LoRa solution and a 3G one is that there is more wiring for the typical 3G because each sensor needs to be wired in to the brain. Having said that, the 3G sensors are often a fraction of the price of LoRa sensor probes and do not incur monthly charges on a per sensor basis. A complex hothouse might take 20-30 man hours to wire up so there might be a once off cost of $2000 to contemplate but there are no monthly sensor fees that would easily exceed this on an annual basis.
Any solution should expect at least $400 for installation of the brain.
4.            Bandwidth charges (SIM cards)
We have already covered the monthly charges found on both 3G and LoRa installations but what must be considered is excess charges in the 3G environment if the data flow is not contained. If your solution uses for JSON, which has a data density of only around 5%, you could be up for GBs per month per device. Now your bandwidth charge per device is looking more like $30/month instead of less than $15. LoRa will never suffer this expense because it cannot transmit that much data. This also implies that LoRa would never be deployed in environments where detailed trending data needs to be collected.
The takeaway is that data cost is proportional to the data need. More data means more cost. When choosing your IoT provider you will need to know what data volume your hardware will pass or what it will cost you if it is variable.
5.            Platform Charges
Comparing platforms is quite difficult. Here are some examples for sort of equivalent IoT suites:
·         Amazon - $8 per million messages
·         Cloudera – Well they won’t tell you directly because their data hub is kind of a one stop shop but it runs on Azure. Essentially the pricing model is per minute but as it’s based on MS Azure, you can expect it to be about the same
·         Azure -  $50/month for up to 400,000 messages per day ($4.10 per million messages)
And there is a curious thing going on here. Find IoT platforms and find their pricing! If you search for IoT frameworks in your favourite browser you will get the likes of Dell, IBM, Bosch, Rockwell, Cisco, Intel, GE and even Facebook, all touting their expertise and capacity. Their websites are full of spin and promise and whilst they may be investing in devices, networks and sensors, they are not building platforms the way Amazon and Microsoft have. Well at least not yet and nothing that provides a pricing model. For now, this places us back to the above three.
Amazon charges per message whereas Microsoft charge per service. Each are scalable but in different ways and on a different basis. For example, if you have a slow message day, the Amazon cost is lower but you can’t automatically ramp up from one pricing tier to another. Microsoft sells you a pipe of a certain width and you scale within automatically. But it isn’t as simple as this and there are other services and capabilities you also need to take into account.  On top of the platform there are storage charges ($5/month per client) and web service charges (that sort of work out to around $15/server). In a genuine multi-tenanted approach, it is conceivable that we are looking at a further $15 - $25 per month per device/customer, depending on how the environment is configured.
The trick here is to find a provider that can roll all this together for you and offer a multi-tenanted solution so that a bunch of IoT users get to share in the lower pricing possible with scale. If you are to do this yourself, you will have to come to terms with this complexity.
6.            BI and Visualisation Charges
Not surprisingly, this can be the most expensive component of all. If you want to use Microsoft’s BI and Stream Analytics services, as powerful as they are, they are also very expensive. You would be looking at $25/user per month per service. Amazon, Cloudera, DigitalOcean and others are all just as pricey. However, if there is purpose written software servicing the specific market vertical being monitored, there must be a software charge of some kind. Most of the companies racing to build IoT style sensors do not also build the software applications to manage and trend the collected data so this makes finding a solution partner difficult. Equally, software developers, who usually do not have industry IP to start with, do not readily jump into bed with hardware partners because it locks your software tight against a specific hardware strategy. I am predicting a lot tears and wrist slashing will take place over the various partnerships that seem to have formed to date. Most of the agricultural based software I’ve seen is seriously expensive – out of reach to the mass market. Similarly, wiring up your home, be it for medical monitoring or turning your heating on, costs a bomb and those who dipped their toes in this market have all gone broke.
7.            Research and Development
If nothing else, I hope I have conveyed to you the complexity involved. How you go about finding a technology partner to build an IoT solution will be the subject of another blog but your first priority is to establish your role in the chain. Are you the consumer or the provider? Are you doing hardware or software? Which platform are you going to use? Who will be your project integrator? Are you going to do all this yourself? You can see that the sheer research and development involved is not trivial. Most companies like ourselves have been toying with this for years and it isn’t being made easier by changing technology – at the sensor and brain end as well as the platforms in the middle. This research is expensive and that cost must find its way into the solutions somehow but there is no doubt a world full of companies racing forward to get your attention. Just be prepared for the cost.
Geoff Schaller

IOT Data Behaving Badly

IOT Data Behaving Badly

 One of the biggest sins being perpetrated by so-called IOT technology companies is that they simply don’t understand data: its cost and the use to which it is going to be put. Getting either one of these attributes wrong is damaging but getting both wrong will be a business funeral. More importantly, it will cause a loss of business confidence in the IOT space. We all have a duty to correct this.

I am going to divide this discussion into the three components: cost, usage and control. Whilst all three attributes have some common territory, they each drive different fundamental properties of the IOT space and all influence business outcomes. The underlying discussion is about the technology chosen to deliver data. Capability and cost are directly related as we shall see.
1.            Data Costs
The cost problem is a function of the technology employed and there are two sources of data cost: the telecommunications network and management framework. Starting with the telecoms side first, there are essentially three available avenues to the internet:
·         Local Area Network – this is a low or no cost connectivity solution but isn’t likely to be widely available, especially in remote monitoring scenarios. But for building or factory networks, it is a genuine option. Any data volume is possible and two way flows are implicit.

·         3G/4G Data – This is provided by all major Telcos. By obtaining a data SIM (and not just any old SIM) you can connect anywhere within a cell tower radius. Mostly this is around 3km from a tower but in rural areas without hilly terrain, this can reach 7-8km. Any data volume is possible. The cost here is based on the SIM card but will be at least $10/month for up to 1GB per month. Two-way data flow is a genuine capability. Battery powered options are not practical unless you include solar powered chargers.

·         LoRa 900Mhz Data – Low power wide area networks are being progressively installed into Telco towers throughout the world. In Australia they can only be found in capital cities right now but a rural rollout is envisaged over the next 24 months. Line of sight can yield up to a 25km range with quite a decent battery life, depending on data flow and sensor type. However, you are limited to 144 messages a day per device and usually something quite small like 12 bytes per message. This is also very definitely a one-way solution. Only data coming out from the device is possible and will not be controlling devices or updating its firmware remotely. The cost however can be as low as $10/year per device, not counting base stations or repeaters.
These communication costs need to be viewed as a per device cost but the device may be aggregating multiple sensors, except in a LoRa situation where aggregation is not possible. When using 3G/4G, care must be taken with data flows or the bandwidth charges are going to go through the roof. There is also a question of who owns the telecommunications contract and who will pay the excess charges when they occur. The big thing to note is that cost is measured in bytes.
The other data cost arises where these data packets are sent. Whether this is Amazon AWS, Cloudera, Microsoft Azure or the host of other platforms, there is a cost and they are roughly similar. They all have a free tier but no scalable commercial solution is likely to survive in the free tier alone. For example, Microsoft offers the following for the Azure IoT Hub:

The basis here is in messages per day. If you have a device that wants to send one message a second, this is 86,400 messages per day – not even 5 devices for the $50 tier. If you only need to send one message an hour, then your $50 tier supports some 16,000 devices. The complication though is what these services count as a “message”. Command calls, device lookups, heart beats and other network calls all count as a message. Unfortunately, it doesn’t end there. On top of this you need data storage and web jobs to manage the data flow, as well as visualisation resources or software. More monthly costs and more management complexity. Unless you are designing a genuine multi-tenant solution then these costs will dwarf the platform cost. If you are going multi-tenant, then there is management software to write. More costs.
The summary I offer you here is that if you design badly or don’t know what you need and why, it is going to break the piggy bank. Most of the commercial solutions on offer do not properly disclose all these costs or come with so many zeros in the price tag they are untenable.
2.            Using Data Properly
In many senses, this is an easier discussion because we should be able to match the data flow requirements to the application. Why then do I see so many solutions being proposed to consumers that cannot deliver what is going to be expected? What is happening is that solution deliverers are concentrating on one specific communications technology and trying to flatten out all data needs into the one model. Duh! Not going to work. Let’s look at some examples and discuss where integration might be of value.
Various companies are targeting LoRa solutions at the agricultural sector and there are three very popular implementations: soil moisture, weather stations and stock counting. You could easily argue that soil moisture won’t change much in an hour so hourly reporting is fine. But what about weather? Do you want to wait one hour to find out the wind picked up to gale force or that a cloud burst occurred over your irrigated paddocks? Stock counting provides another insight into some low-brow thinking out there. Perhaps the aggregate can be sent hourly but if I am loading pens or counting stock through a dip, I need to know immediately the pen is full, not an hour later.
Silo and water level monitoring offers a different challenge. The devices are often very remote and usually do not have a local electricity source. It would seem obvious that a LoRa solution is perfect and sometimes it will be. But in the case of the water tank, we were asked to add EC and pH monitoring. Oops, they also wanted to monitor back pressure in the outflow line to detect leaks. I now have four LoRa devices and it is starting to look like a 3G battery powered central control unit on 4G with a solar charger is a more practical and cost effective solution.
If we aren’t talking remote, such as in the agricultural sense, then the rules are different. We can take as much data as we can generate, assuming the underlying bandwidth will cope. In a secure facility you might want to monitor every door and every access keypad and on a busy day it might generate a lot of data. Whatever the source or reason, the communication side is not really a problem but the data visualisation might be.
And here lies the next challenge. Are you dumping all that data on the client and expecting them to make head or tails from it or are you going to provide aggregation outcomes, alerts or management statistics of value? There are some very nice monitoring kits coming out of Scandinavia right now but apart from needing a genius electrician to install, you are left to design your own visualisations and set your own alerts. They also charge like a wounded bull per sensor. In itself this is quite a technical operation and if we all leave this to our prospective users to design, very few are going to get value for their trouble. Monitoring is fine but doing something sensible with that data is crucial to market acceptance.
3.            Control
By control l mean feedback. Refrigeration is a really good example. If a door is left open for too long, I want to set off an audible alarm in the building. Seed potato must be stored between 3.8C and 5.5C – too warm and it sprouts, too cold and it dies. If the temperature goes outside these limits I need to turn the unit up or down immediately, not wait for a human to respond to an SMS. (They could be at a party!) Being alerted that I have a disaster on my hands is not enough if I cannot get to unit fast enough. Even the silo solution has feedback potential. Outflow usage is slow – no problem - but when the truck is blowing the new grain in, it would be nice to know when it is nearing full so that we don’t overflow the silo. We could shut off the pump or sound a klaxon to force the onsite user to manual control.
There is also a common scenario where once a problem arises, a technician wants to see much denser data in order to assess what parts or equipment to bring with him on the inevitable call out. Unless there is some way to proactively change the data flow out of the device, it cannot happen. This might turn a low data flow pattern temporarily into a high flow pattern. There are countless examples of this. Heating and cooling, mechanical doors, alarms, lifts, plant and machinery and many, many more. To me, it isn’t enough to just monitor data; we need to provide value back to the source and help control problem situations.
In summary, our technology choice will dictate or compromise the capability we can supply the client. It will also dramatically influence the cost and complexity of providing that service. What does the customer expect from the data? One shoe size does not fit all and unless you are going to help the client do something practical with all that data then everyone is wasting their time.
Geoff Schaller

Where is IOT At - The Trough of Disillusionment

Where is IOT At? The Trough of Disillusionment

Everybody is familiar with the Hype Curve. Right now, IOT has just gone through the Peak of Inflated Expectations and is heading for the Trough of Disillusionment. Everybody is getting excited about the potential but outcomes are simply not going to match expectations. Disillusionment is certain to set in. We will investigate why and explain how to move back up to the Slope of Enlightenment.

Software companies and hardware vendors worldwide are rushing to jump onto the IOT bandwagon. Microsoft, Google and Amazon are racing to build massive platforms to collect and manage IOT data. Telcos are out there actively spruiking SIM card plans and LoRa networks to capture the IOT traffic (and I will explain some of these terms later). Hardware vendors are scrambling to build sensors and probes that can be connected to these networks and platforms and all of them are there expecting to make their fortune by being first. Most are heading in the wrong direction and almost all of them are in for a big shock when they discover that the consumer wont’ come to the party. Not yet, at least.

Remembering back to our definition of IOT, we are concentrating on remote monitoring and control of devices. Please don’t forget the ‘control’ aspect but the data volume will come from monitoring and alerting. There are essentially three things that are going to spoil this party.
I call them the Three C’s of Reality:
·         Cost
·         Complexity
·         Confinement
First and foremost is cost. This IOT thing is new. The networks are new. The data centres managing this are new. The hardware is new. New means expensive because of the research and development effort required and those participating have to earn a living or recover costs. In another blog I will explain the specific problems of data transmission cost but just think of the relative rate of return that IOT remote might offer. Some examples will help. The farmer considers soil moisture monitoring to help determine planting conditions or irrigation needs. He has two choices: go down to the paddock once a week and stick his finger in the soil or buy a remote monitoring kit so that he can read this from the comfort of his living room. Would he pay $10,000 for the remote monitor? Unlikely. $1000…? Maybe. However, if he owned 20 properties or 6000 hectares, he might. But even the larger farm owner probably already has resources in place to monitor this manually that are just a cost-free adjunct to other responsibilities. What about refrigeration monitoring? Here you could add up the cost of the stock and thus what an unnoticed breakdown might cost you. But you still aren’t going to pay for automation if it costs more than a human to put in place when the human can also provide other services. No home consumer is going to add automated ordering to his fridge or control to his heater if it costs thousands to install. Right now, IOT is expensive and you can read my other blogs to find out why in some detail. Whilst the cost is high, take up will be slow. Cost needs to become relevant to the solution being proposed.
The second reality check comes from complexity. This isn’t for the feint hearted. Just writing the device level software is hard work, let alone the electronics of wiring sensors and cards together to something that can do the transmission. Installing IOT devices isn’t just a matter of plugging two things you want together because you need to acquire compatible hardware. And even if you get all that together and work out how to move the data through the network and into your hands, you need software to visualise, trend and respond to it. The people working in these three zones will need to be specialists and this will only add further to the cost burden. It also reduces the number of people capable of delivering such services and that will restrict the volume of services that can be put in place. It will take some time before the implementation resources will get out there to support a burgeoning IOT market place.
Thirdly there is confinement. IOT solutions are going to be confined to specific niches for quite some time and mainly due to the reasons of complexity described above. Complexity will cause the confinement of solution availability. The flow-on is quite insidious. Lack of availability will cause people to stop looking and this will slow down demand growth or induce scepticism that it will ever be practical or available. Sentiments like these are a massive impediment to system take-up and are only overcome by marketing campaigns or direct action. By pushing people to expect IOT solutions and then not delivering will cause the Trough of Disillusionment.
Is there anything we can do to smooth out a seemingly inevitable Hype Curve? There is but people aren’t going to like what I suggest. Firstly, we need to slow down the hype – don’t speculate. We can’t deliver everything yet so cool down the rhetoric and focus on deliverable solutions. Secondly, we need to reduce our greed for profit and present affordable solutions that match the value of the service being protected or provided. If we are replacing humans, we have a direct labour cost comparison. If we are protecting stock, then we know the value of that stock. Computing these numbers is not rocket science. And finally, we have to deliver genuine and reliable outcomes that have some obvious productivity value. This will require better research and a cooperation between hardware, software and network vendors. That won’t come easily.
Geoff Schaller

What is IOT - A Newcommer's Primer

What is IOT? A Newcomer’s Primer
 IOT. The Internet of Things! Such an esoteric, pretentious yet potentially meaningless title for something we’ve always had: lots of things connected to the internet. So what is all the fuss? Well let me explain.
Before we dive into what users of this phrase seem to imply, it is instructive to review where we’ve been: what is the internet so far? Well it is quite simple.  The internet started out its life as an email network. This allowed us to exchange information.  Mail servers morphed into web servers and web sites were created to feed us stored information.  The next evolution was the addition of web services: little bits of programming logic to do stuff for us: calculations, lookups, translations, collection and storage or even push our information all over the world. Principally, the internet was a bunch of user level devices (laptops, PCs, tablets and phones) connected together via a bunch of servers and networks that help store, manage and forward traffic. So you see, the internet was all about us. I like to call this the IOU: The Internet of Us.

Enter the concept of things. Well we always had things because you couldn’t use the internet without them: phones and tablets and laptops and websites... Phones in particular added a huge number of things. As of February 2014, phones outnumbered humans (6.8 billion) and given that by now, almost all will be smart phones, this makes the phone the most common device (or thing) connected to the internet. Add in Servers, PCs and all those other mobile devices and we’ve easily topped 10 billion things, even back two years ago. Clearly then, when someone talks about an Internet of Things, they must mean something other than just phones and PCs. And of course, they do. They are implying something completely different.

To understand what is meant by the things in IOT, we need review two important trends. Firstly, devices are getting smaller and more tightly purposed and this bumps up the volume somewhat. This might be my home security system connected to supply video feeds and alarms or it might be a temperature sensor on a freezer set to alert problems. It could be a soil moisture monitor stuck in some farmer’s paddock or a flow monitor in an irrigation manager. In other words, the interactive nature of the web, such as you or me browsing web sites, is slowly being expanded by things – devices - doing specific tasks. An average house could conceivably have 20 such devices, a farm hundreds and a factory or building, thousands. The second trend going on is that the traffic for these new devices is getting smaller and more frequent because it is getting closer to being just raw, unformatted data: nothing you and I could look at without a lot of interpretive software. This means though that there has to be something out there capturing, storing and using all that data. Do you see the trend? The internet was firstly a delivery platform for static information (emails), then it became a source of interactive data (web sites) and now it is turning into a giant multi-lane highway for endless streams of raw data (sensors).

So the things in IOT are really implied to be sensors, devices and switches that will send data to the internet or be controlled by data from the internet. This IOT network lives right alongside our existing IOU network. IOT can be considered as data for machine consumption whereas IOU is mostly data presented for direct human consumption. This distinction is important because of how their respective data streams are handled. IOU data is presented nicely formatted for direct human consumption – IOT data needs storage, conversion and interpretation. There is a cost to building and maintaining that IOT framework. For IOU, the cost is only in formatting and storing the data.

Mostly, web sites and web services are designed to be consumed by everyone or groups of people given access. Once we get down to these IOT sensors and devices, these are usually owned by individuals and they transmit their data to specific locations or applications. So now the internet is taking on the role of providing a free pathway for a whole range of devices to provide services and functions to which we subscribe or own and there are potentially tens of billions of them.

Some obvious examples include:

·         Environmental monitoring and reporting: commercial, agricultural or personal

·         Weather monitoring, water quality or sea level monitoring

·         Alerting mechanisms: temperature, proximity, access, security, levels…

·         Data collection for statistics or commerce or compliance

·         Remote control: turn things on or off

So whether it is agricultural monitoring, home security, traffic monitoring or just your fridge ordering more milk because you ran out, it is all about tasks, data and outcomes. Is it good? Maybe- I don’t want my fridge ordering 100 litres whilst I am away overseas on holiday. Is it inevitable? Definitely. Two years ago Gartner predicted 100 billion devices will be connected to the internet by 2020 but this turned out a tad ambitious and they have since revised it back to 40 billion. Whatever the real number – and nobody knows – it is large. The takeaway is that the number of devices will grow but the direction and speed is difficult to predict. I don’t think the reality will quite match the hype. Why? Because it is more expensive and complex to implement IOT than people expect and my next blog will explain this in more detail.

So the Internet of Things is really just a way of saying what has always been the case. The internet is full of things already but the diversity is growing and the usage changing. We are, however, moving from IOU only to IOT plus IOU: from a place where we were just connected to shared public resources that benefited all of us to one that includes potentially vast amounts of traffic that benefit individuals. The past two decades saw a concentration on public networks and universally accessed data but the way forward now is seeing a more private use of devices for personal and commercial benefit. The variety of things is endless but this is what this use of things really means: remote monitoring, management and control for a really wide range of devices.

This definition is important because it will shape the discussions going forward.

Geoff Schaller