Until quite recently, machines didn’t do much talking. In industry, they took instructions from programmable logic controllers (PLCs) and handed basic performance or status data back via supervisory control and data acquisition (SCADA) networks. For consumers, the Internet fridge was still an urban legend, and wearables were limited to digital watches without connections to the rest of the world.
Since then, IoT has evolved in ways that make earlier connectedness (or lack of it) look primitive. In a world where users can control building facility systems, media devices like hi-fi amplifiers, and wind turbines from their smartphones, IoT has made giant strides forwards. Beyond talking toasters, self-stocking fridges, and smart lawn sprinklers, IoT is now also being used in food product tracking, beacons to guide shopper purchases in retail stores, and vehicle fleet management and control, to mention just a few examples.
New Models for Massive Deployment
Enterprise thinking on IoT has evolved as well. Information and operational technology (IT and OT) teams are getting to grips with moving from small-scale implementations to widespread deployment. Data processing is increasingly being pushed out closer to devices and machines, to avoid saturating links with data centers and to reduce response times between machines now producing more data and receiving instructions about what to do about it. Fog computing that data places processing in or close to IoT devices and edge/core network models are in vogue, rather than previous approaches of shipping all the data back up to the cloud.
Smarter Outcomes via Intelligence
Business intelligence (BI) and artificial intelligence (AI) have been increasingly used to make sense of the increased volumes of data coming back from machines, devices, and any other things now connected and able to converse with the Internet. BI applications have been coming down in price and rising in capability. Best-of-breed BI solutions make it affordable and easy for non-specialist users to mash up multiple data sources, including machine data and glean operational and business insights for better results. AI technologies such as machine learning are helping machines to help themselves by automatically recognizing new situations and taking appropriate action.
Expectations Keeping Pace with Technology
User and customer expectations have moved on as well. The idea of talking to natural language processing agents like Amazon’s Alexa to get things done is no longer seen as bizarre, either in the consumer or the business arena. A few years ago, only a few things could be controlled or managed by mobile computing device apps. In a few years’ time, we can expect the opposite: only a few things will still be unable to communicate with such apps.
Hackers and Attackers
Naturally, these substantial changes have not escaped the notice of hackers, cybercriminals, and cyber terrorists either. They have developed new malware to attack industrial networks and new strategies to take control of connected devices to launch attacks (DDoS specifically) on different organizations. Key recent examples have been the “Industroyer” virus attacking power generating facilities in Kiev, Ukraine, and the Mirai malware used to build the botnet that swamped out the Dyn company and temporarily shut down communications for some of Dyn’s high-profile customers like Twitter.
Invent Your Own Future
How will IoT evolve over the next few years? The enterprises applying IoT technologies and solutions may have the most accurate answer, because, as the saying goes, the best way to know what the future holds is to invent it for yourself.