From smartphones to medical devices, connected cars to robo-bosses, everyone has read plenty about how the Internet of Things (IoT) has the potential to transform everyday lives and unlock unprecedented growth within companies.
With a number of benefits on offer, it’s perhaps of little wonder why the IoT will be adopted by 85% of businesses by 2019. However, organisations should approach this mass adoption with caution.
Where there are rewards, they are also risks, and if businesses don’t overcome the challenges the ‘dark side’ of IoT presents, they will certainly be heading for trouble.
Challenge #1: The data deluge
Since the rise of the IoT, businesses now have access to more data than ever before. And it’s only going to continue growing. In fact, it is predicted that by 2020 the amount of information produced by the IoT will account for about 10% of data on earth.
While this explosion in data, in many cases, is a blessing, it can also be a curse given that sometimes it’s almost too much for businesses to handle. In some cases, the total amount of data being collected from devices may be so great that moving it all over the network to a central location may not be a viable solution.
Take, for example, a sensor set up in a large industrial food storage warehouse and distribution centre which uses internet-connected devices to regulate temperatures. To serve its various purposes, including maintenance, the sensor needs to transmit information such as temperature, humidity, battery level, motion/position changes and so on.
This data could be transmitted every 30 seconds and it’s quite likely this sensor is not alone. So what you end up with are hundreds of sensors reporting information on numerous factors, as often as twice every minute. That’s a lot of data to process.
What’s needed is an integration solution with the ability to aggregate only the desired data from wherever it resides, normalise it into common data models, and make it accessible for monitoring, reporting, and maintenance purposes.
Challenge #2: Not understanding the data
Once you have the vast amounts of data under control, it’s now a case of making that data meaningful, rather than just manageable.
However, this is often easier said than done. Given the range and diversity of IoT use cases, it’s unlikely that a single vendor can create a comprehensive solution for an environment of a large-scale enterprise, or warehouse, for example.
A fully functional, secure and robust IoT environment requires a complete peer-to-peer solution in which devices from one vendor can translate the information from the devices of all other vendors.
Yet, with numerous connected devices in a given environment, the involvement of hundreds of vendors as well as a number of legacy devices, this approach just isn’t feasible.
Without a better solution, businesses face a dilemma. So a more practical solution, to ensure all data can be understood and fully utilised across the deployment, is to create a smart hub model which intelligently brings all the data together.
Here, one or more IoT gateways and IoT central servers are constantly receiving data from all the devices and sensors. A rules engine can then analyse the incoming data and the hub can then pass on appropriate commands.
What’s more, to make the system fully ‘understand the data’, we come back to the importance of having a common data model in place which makes it possible to compare and integrate data from any vendor’s device.
Challenge #3: Securing the weakest link
While multiple connected devices working together is essential for large-scale IoT use cases, security is always going to be a concern.
In today’s threat landscape, the overall security profile is only as strong as the weakest device. If the security on a particular vendor’s sensor at the aforementioned warehouse is weak, it is an easy target for hackers.
What’s more, if other sensors and devices depend on data from that weak sensor, there’s the very real chance of the others being compromised too.
To solve this problem, the warehouse IoT peer-to-peer model must be implemented in a way that enables the system to double-check a particular sensor’s reading by comparing it with other physically co-located sensors to confirm that reading.
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For example, if one outdoor sensor is reading particularly high while its neighbouring sensors uniformly read a lower temperature, then the system should not make an immediate decision to adjust the temperature of the warehouse.
Instead, the system should issue an alert to validate the functionality of that sensor and to check the physical area around the sensor.
Challenge #4: Unpredictable outcomes
Businesses are now making progress on defining their IoT strategies, and many are successfully deploying solutions. People will start to see a shift from business solving problems via IoT, to IoT itself creating more hurdles to overcome.
For example, there is a natural inclination for businesses to first identify and solve for a specific goal, such as the monitoring and controlling the HVAC of a building to be more efficient.
In any of these organisations, there will be multiple IoT deployments that are solving specific challenges that mostly depend on their own devices, events, and data.
IoT will evolve not only to have multiples of these specialised ecosystems but now an organisation may also want to support correlating situations across these ecosystems. This will present a new level of unpredictable outcomes.
For instance, a motion sensor on an HVAC panel triggers the security system or an unforeseen data model change from one vendor throws an unrealised dependent ecosystem into complete chaos due to bad or missing data.
With more IoT deployments, ecosystems get exponentially larger and a new level of technology challenges will need to be solved.
First decision is key to success
Although it’s impossible to predict all the future needs for an IoT strategy, especially with the landscape changing so fast, it will be critical to look down the road. The choice of technologies now will define how an organisation’s IoT platform will be able to adapt, scale and provide the agility needed to competitively evolve.
Those that get these decisions right, and overcome the dark side of IoT, will find themselves making smarter, faster and more accurate business decisions and quickly outpace their competitors.
Sourced by Michael Morton, chief technology officer, Dell Boomi