(the lest significant digit on it is still in hundreds of liters….)īut though, I kinda live in a place where water isn’t even remotely expensive. Though, one can check current water usage if one stares at it for a while. Or… just go camping!Īnd what if I did have my own tank? Wouldn’t I have to take care to make sure bacteria and other nasties don’t grow in it? Worse, when the next do not drink advisory hits will it not contaminate my tank before I get the notification and turn off the supply? It sounds like a whole lot of wasted effort, money and space to me.Īnd here I sit in a house that has the same old water meter since probably sometime in the 70’s…(if not older) I can bring my camping water bag with me and fill it there! If somehow both failed simultaneously (has never happened to me) I could go to relatives houses with their own wells and generators. It’s water comes form a different source. I actually avoided the water an extra day because I had plenty and I figured better safe than sorry. Before I left I filled my water bag from the campsite water supply, a well with a hand pump. Also fortunate, someone called me and let me know it was happening. I was lucky and missed most of that 2 days because I was away camping. We had a do not drink advisory for a whole 2 days due to toxic algae.
All lasted less than a day with maybe a boil advisory for the following day.
I could probably count them on one hand if I could remember them. In 40 years I have only seen a handful of water outages.
Posted in green hacks, home hacks Tagged data analysis, data mining, machine learning, privacy, water use Post navigation
#Aqua sense sensus water meter series
The information he’s collected on using Python to classify time series data and create visualizations will undoubtedly be of interest to anyone who’s got a big data problem they’re looking to solve. But on the other hand, his write-up is a fantastic look at how you can put machine learning to work in even the most unlikely of applications. Especially since we’ve already seen how utility meters can be sniffed with nothing more exotic than an RTL-SDR. In terms of the privacy implications of what has discovered, we’re mildly horrified. Want to know how restful somebody’s sleep was? A count of how many times the toilet was flushed overnight could give you an idea. Want to know how many kids are in the family? Monitoring for frequent baths that don’t fill the tub all the way would be a good start. But was able to pick up even more subtle differences, such as which individual toilet in the home had been flushed and when.įurther, if you watch the data long enough, you can even start to identify information about individuals within the home. Appliances that always use the same amount of water, like an ice maker or dishwasher, are obvious spikes among the noise. The key is that every water-consuming device in your home has a discernible “fingerprint” that, with enough time, can be identified and tracked. By polling a whole-house water flow meter every second and running the resulting data through various machine learning algorithms, found there is a lot of personal information hidden in this seemingly innocuous data stream. After all, what could anyone possibly learn from studying how much water you use? Well, as has proven with his fascinating water-meter data research, it turns out you can learn a whole hell of a lot by watching water use patterns. We’re at the point where a sizable chunk of people believe their smartphone is listening in on their personal conversations and tailoring advertisements to overheard keywords, yet it’s unlikely they’re troubled enough by the idea that they’d actually turn off the phone.īut even the most privacy-conscious among us probably wouldn’t consider our water usage to be any great secret. For better or for worse, they’ve come to accept the fact that data about their lives is constantly being collected and analyzed. The average person has become depressingly comfortable with the surveillance dystopia we live in.
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