There’s no doubt about it––the Internet of Things (IoT) is shaping up to be the mother of all technology trends in 2016. There are endless statistics on how many devices are or will be coming online; just within the home, everything from doorbells, to refrigerators, to light bulbs has become internet-enabled.
As IoT devices infiltrate many product ecosystems, they’re becoming more a part of our lives. But while these devices gather lots of data, they’re not very intelligent or self-aware in their own right. That can be a problem. We need the right intelligence and security onboard, in the devices, in order for them to become aware when they might be manipulated or failing.
I call it the “Intelligence of Things,” and that’s when things will get really interesting for IoT.
The power of self-learning analytics
Self-learning streaming models based on continuous inputs are a mainstay of the financial world––in fighting payment card fraud, for example––and, increasingly, cybersecurity. As applied to IoT devices, self-learning models monitor their environment and gather data, and thus can make a determination if the behavior of user or environment they’re embedded in is normal or abnormal.
With self-learning models on board, IoT devices can warn of an unsafe situation, or an impending failure. Stoked by streaming analytics, the device will infer that something about its environment or itself is failing, and issue an appropriate warning. Interestingly, IoT devices within the home can communicate with one another; in this way, the web of devices can build a larger consciousness of the house based on their collective inputs.
IoT spotlight: air conditioners
Here’s an example. In recent years, commercial air conditioning units have become targets for vandals, looking to strip the $80-$100 of copper contained therein. Some companies have taken to elaborate measures to protect their A/C units, ranging from lighting and security cameras, to special alarms, to putting a GPS tracker on the unit.
Outfitting the air conditioner with an IoT sensor would allow it to collect a constant stream information on many operational functions. Should a component begin to malfunction, the IoT device could trigger an alarm, notifying the facilities manager that maintenance is required. In this way, should the device be intruded upon by vandals, the IoT sensor would detect abnormal activity and trigger an appropriate alarm.
Here’s another, even more dramatic air conditioner example. In 2015, Formula 1 race car driven Jenson Button and his wife were robbed in their villa in the south of France, reportedly after burglars pumped an anesthetic gas through the house’s air conditioning system. (Shockingly, this kind of attack does not seem to be uncommon in the region.)
Again, an IoT sensor could have detected tampering with the unit, or air pressure changes as the anesthetic gas was introduced into the system, triggering an alarm. The alarm from the air conditioning unit, coupled with inputs from the villa’s IoT home monitoring system, could have alerted the owners (or a private security monitoring company) of suspicious activity.
On a more pedestrian level, even the lowly IoT doorbell could benefit from self-learning. Earlier this year hackers figured out how to compromise the Ring IoT doorbell, to extract the home WiFi network’s password. Putting more intelligence on-board in the device could have triggered an alert that the doorbell was being tampered with. This information could’ve been cross-checked with the motion-triggered alerts that Ring already supplies.
Achieving the “Intelligence of Things”
Moving from vanilla IoT to the “Intelligence of Things” requires a change in mindset of these devices. Instead of just collecting data for apps, comparison to rules thresholds, or binary commands, they need to self-monitor their state in the environment. They must measure their own “self” which, for an IoT device, requires streaming behavioral analytics.
Real-time behavioral awareness is a fixture in certain domains such as payment card fraud detection. The analytic possibilities get even more impressive and interesting when data from disparate devices is fed into a centralized analytic fabric. Now we’re talking about the “Intelligence of Things.” Thus elevated, traditional IoT can move, categorically, beyond ho-hum information reporting and rules, on to predictive outcomes. By leveraging the predictive power of self-learning analytics, IoT can facilitate exciting positive outcomes such as:
- Self-tuning energy consumption based on individuals’ movement patterns within a home
- Optimized automated security based on normal historical movements, temperature and humidity control––this capability is applicable to everything from indoor and outdoor lighting to the air conditioner/home invasion scenario described above
- Numerous other health and safety scenarios