Internet of everything: The move from M2M to IoT
The numbers DO NOT STACK UP. The United Nations forecast that by 2030, we will have 17% more people to feed, yet today 10% of the world population is undernourished and 33% of the food we produce ends up being wasted.
There is no doubt in anyone’s mind that the world’s primary industries need to become more efficient if we want to satisfy the demand imposed by the exponential increase in population we will be facing in the coming years. It is true for food, but also for energy in the context of global warming, and all other primary resources across the globe.
This is the reason why those industries have been leveraging advances in technology to make their operations more efficient. Over the last 20 years, this sector has evolved significantly, however never with the speed of evolution or the focus as what the Internet of Things attracts today.
More advanced industries are already making the change from M2M to IoT. In mines for example, safety and incidents have been greatly avoided by the deployment of personal and vehicle tracking devices, with a system constantly and digitally aware of its surroundings. Anyone that has seen an Amazon warehouse robot at work would understand the same - more sensors, more connections, means more automation, and less opportunities for human error in the chain.
MASSIVE IoT moves one step ahead, and is tightly linked to the decreasing cost of ‘sensors’ and connectivity, enabling a multitude of new connections and parameters for better decision making.
M2M - machine-to-machine communications
The name in itself defines the approach, but to provide more detail, we are talking here of machines in the same system communicating together without human intervention. Think a red light car sensor, a seeder control system, an ATM or POS solution - solutions we now take for granted that replaced over time some manual parts of our day-to-day, but always deployed for a single use-case. This is an area where the terms ‘master’ and ‘slave’ systems are found, the master being the brain, the slave executing the commands.
There are many ways to enable m2m communications, from direct cabling (still today) to wireless technologies, and for the most part consist of add-ons to existing equipment, which is therefore not built to communicate ubiquitously by design. Those systems also typically are quite costly, both upfront but also as running costs, like power, and require in-depth analysis to understand the data coming out (if ever).
M2M communications already is a fantastic step in making industries more efficient, but due to its siloed nature misses out on the benefits a completely intelligent autonomous system could bring, this is what the Internet of Things is about.
IoT - monitor, control, report… autonomously across everything
The Internet of Things brings together the M2M silos, to enable better decision-making taking into account a much bigger ecosystem of data points.
This is the difference between a sensor-enabled traffic light (reacting only to the cars presenting at the red light), and a connected traffic cloud (changing the duration of the lights based on the overall traffic patterns in the area to streamline commuting). We are now talking of ‘things’ that are built to communicate, and therefore become ‘smart’, sensors that are not expensive and very precise, as before, but cheap enough that they can be deployed at scale, providing better analytical behaviour models to a central brain, that can make those decisions based on a whole of ecosystem view. This is true Massive IOT.
With cheap sensors also comes cheap connectivity, designed to handle large amounts of varied sensors, and maintain the battery life of those sensors for years. Think scattering biodegradable sensors while sowing in a field, providing micro level data back to your sprayer or sprinkler, to ensure you right-size the amount of fertilizer or water required per crop. Constantly monitoring the corrosion of an asset to be able to accurately forecast maintenance requirements, or the temperature and humidity of each box of fresh goods, impacting the cooling systems from the moment it is packaged to the moment you bring it home.
And all of this is enabled by the availability of data, a lot of data… too much data.
The rise of the small data revolution
So of course more sensing means more data points, small data packets providing better granularity, and more precise data means better decision. That is if you can make sense of any of it. And this is where IoT makes it or breaks it - if you cannot simply access the decision support you need from the data points you get, then you’re drinking data from the firehose, and the solution is then to close the tap. This is where being able to rationalise the data points into usable data insights as quickly as possible is important - this is where Edge computing is key.
To be successful, the brain of an IoT systems needs to be able to sort the data and rationalize it across time or space to make sense of it all.
This is where the biggest change from traditional systems and ways of thinking is: previously you needed to see all the data from those precise systems to make sense of it and be able to impact other machines - today, the systems make sense of it for you and you only need higher level reporting to monitor your operation.
And what about areas where connectivity is scarce?
90% of the Earth is still not covered by mobile network connectivity, that means most of the world’s primary industries are located in areas that are very badly covered or not covered at all by traditional networks.
Some farmers still drive 10 hours in a day to take a sensor reading of the water level in the tanks at the other side of their properties, food is still wasted at an impressive rate in the supply chain, and monitoring our impact on Earth is still not a financially viable exercise. These industries and geographical areas are the ones that have most to gain from being connected, from an efficiency, productivity, and environmental perspective.
Things have been happening in these areas, timidly, limited by the significant costs that are required, and the surfaces to cover. Point-to-point radio links, a number of satellite connections, and some intensive grunt work in the background to correlate direct data streams, 3rd party data (Bureau of Meteorology for example) and personal gut feel have been deployed over time, which has helped.
But now is the time for real in situ IoT, abstracting the complexity of the data to facilitate the decisions, fully integrated in the operation, and cost-effective.