Smart phones were the harbingers of the connected future as they transformed from merely portals for consuming information into sensors and location-based signaling tools. But the Internet of Things (IoT) takes that to another level where relatively inexpensive devices can be connected and managed through automation suites, then mined for new information. As I write this, my Nest thermostat is reporting my energy usage, and my Enphase Energy hub is recording my solar power generation. In April, in Northern California, it is almost ideal conditions for both, with no need for climate control and ample sunshine. Finally, my Kevo locks let me know when people come and go from the house or when a door is left open.
We expect this automation trend to accelerate, too. From water sensors to warn you when your plumbing has gone awry to smart pill bottles to help insure accurate daily dosing for an increasingly aged population, the range of information that we will soon have access to will likely become overwhelming. Indeed, just managing all of the devices, protecting privacy while allowing access, and figuring out how to merge related data streams together is already becoming a growth industry.
The latter may be the most interesting and valuable part of the technology puzzle, as well as the most challenging. Data analysis traditionally falls into several areas. The most common is alerting and descriptive statistics. The smart pill bottle needs to, at a minimum, alert the patient when the dosing is incorrect. It might also provide aggregate statistics concerning compliance. Where it becomes more challenging is when those statistics are merged together with other information sources. What is the correlation of a medication with the successful treatment of the condition or even off-label impacts and adverse effects? This has traditionally been the domain of retrospective analysis with large variations in the data sets due to incomplete and inaccurate reporting and the challenge of collecting together sufficient records. While medical privacy laws like HIPAA play a part in blocking effective access to this data, reporting is at least as great a challenge.
We can see similar patterns in terms of energy monitoring and consumption, like with my solar array. We recently used connected temperature sensors in refrigerators at Dell to monitor usage and performance of the devices. An odd spike occurred in one on Friday afternoons, showing the impact of an ice cream party on the energy consumption of the device. While a humorous outcome, opening and closing refrigerators consumes around 7% of the total energy used by the devices, and broad monitoring patterns has the potential to help reduce this waste.
These kinds of outlier patterns in the data, whether in off-label effects of medications or in energy consumption by appliances, have a broad social impact. Social psychologists have known for some time that the setting a person is in can strongly affect their choices. For instance, when people see others around them picking up litter from the ground, they are much less likely to litter themselves. We can guess that the awareness that connected monitoring will bring to our lives will have a similar effect. While we may try to avoid leaving the fridge door open, seeing the impact of such actions on the building, the company, the city, the nation, and the world creates a network of awareness and expectations that reinforce better behavior.
