“Everything is a sensor for everything else”
I love this quote from David Weinberger because it captures an important change that’s happening right now. Information about the real world used to be scarce and hard to gather, and you were lucky if you had one way to measure a fact. Increasingly we have a lot of different instruments we can use to look at the same aspect of reality, and that’s going to radically change our future.
As an example, consider the humble pothole. Before computerization, someone in a truck would drive around town every year or so, see which roads were in bad repair, and write down a list on a clipboard. If a citizen phoned up city hall to complain, that would be added to another list, and someone who knew the road network would then sort through all the lists and decide which places to send crews out to.
The first wave of computerization moved those clipboard lists into spreadsheets and GIS systems on an office desktop, but didn’t change the process very much. We’re in the middle of the second wave right now, where instead of phone calls our cell phones can automatically report pot holes just from accelerometer data.
Using sensors to passively spot holes takes humans out of the loop, and means we can gather tens or hundreds of times the number of reports that we would if a person had to take time out of their day to submit one manually. This is only the beginning of the tidal wave of data though.
Think about all the different ways we’ll be able to detect potholes over the next few years. Police and other public workers are increasingly wearing cameras, patrol cars have had dashboard cameras for a while, and computer vision’s at the point where analyzing the video to estimate road repair needs isn’t outlandishly hard. We’re going to see a lot more satellites taking photos too, and as those get more frequent and detailed, those will be great sources to track road conditions over time.
Beyond imagery, connected cars are going to be transmitting a lot of data, and every suspension jolt can be used as a signal that the driver might have hit a hole, and even small swerves to avoid a hazard could be a sign of a potential problem. Cars are also increasingly gaining sensors like LIDAR, radar and sonar. Their job is to spot obstacles in the road, but as a by-product you could also use the data they’re gathering to spot pot holes and even early cracks in the road surface.
There will be a even more potential sources of data as networked sensors get cheap enough to throw into all sorts of objects. If bridges get load sensors to spot structural damage, the same data stream can be analyzed to see when vehicles are bouncing over holes. Drones will be packed with all sorts of instruments, some of which will end up scanning the road. As the costs of computing, sensing, and communicating fall, the world will be packed with networked sensors, some of which will be able to spot potholes even if their designers never planned for that.
With all of this information, you might have thousands or even millions of readings from a lot of different sources about a single hole in the road. That’s serious overkill for the original use case of just sending out maintenance crews to fix them! This abundance of data makes a lot of other applications possible though. Insurance companies will probably end up getting hold of connected-car data, even if it’s just in aggregate, and can use it to help improve their estimates of car damage likelihood by neighborhood. Data on potholes from public satellite imagery can be used by civic watchdogs to keep an eye on how well the authorities are doing on road repairs. Map software can pick cycling routes that will offer the smoothest ride, based on estimates on the state of the road surface.
These are all still applications focused on potholes though. Having this overwhelming amount of sensor information means that the same data set can be mined to understand apparently unrelated insights. How many potholes there are will be influenced by a lot of things; how much rain there was recently, how many vehicles drove on the road, how heavy they were, how fast they were going, and I’d bet there are other significant factors like earth movements, and nearby construction. Once you have a reliable survey of potholes with broad coverage and frequent updates, you can begin to pull those correlations out. The sheer quantity of measurements from many independent sources means that the noise level shrinks and smaller effects can be spotted. Maybe you can spot an upswing in the chemical industry by seeing that there are a lot more potholes near their factories, because the haulage trucks are more heavily laden? How about getting an early warning of a landslide by seeing an increase in road cracks, thanks to initial shifts in the soil below?
These are just examples I picked off the top of my head, but the key point is that as the sample sizes grow large enough, sensors can be used to measure apparently unrelated facts. There are only so many quantities we care about in the real world, but the number of sensor readings keeps growing incredibly rapidly, and it’s becoming possible to infer measurements that would once have needed their own dedicated instruments. The curse of ‘big data’ is spurious correlations, so it’s going to be a process of experimentation and innovation to discover which ones are practical and useful, but I’m certain we’re going to uncover some killer applications by substituting alternative sensor information in bulk for the readings you wish you had.
It also means that facts we want to hide, even private ones about ourselves, are going to be increasingly hard to keep secret as the chances to observe them through stray data exhaust grows, but that’s a discussion for a whole new post!
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