4 ms·

> Think about every single iPhone and every single Galaxy S in the world as a useful sensor array _and_ a parallel processor... Sadly, until we have a breakthr

> Think about every single iPhone and every single Galaxy S in the world as a useful sensor array _and_ a parallel processor... Sadly, until we have a breakthrough in battery technology, all of those will be parallel processors programmed to be asleep as often as they can.

cryptoz 11y ago
Sure, that's true - but they can just as happily run their GPUs if they are plugged in and charging without any battery issues. Although for the moment you are right, they aren't that useful as a powerful distributed supercomputer. They ARE, however, really really good at collecting live weather data and sending that to the supercomputer to process. One step at a time.
- nradov 11y ago
  Smart phones are good at collecting live barometric pressure data but not much else. Temperature sensors aren't very useful because they are poorly calibrated, you can't reliably tell whether someone is outdoors, and the reading can be thrown off by the heat generated by the device or the person holding it. UV sensors are seldom pointed in the right direction. And I haven't seen a phone with a wind speed or precipitation sensor yet.
  - avar 11y ago
    I genuinely don't mean to sound passive aggressive when I ask, do you know anything about this subject or are you just guessing? I don't know anything about it, but I can think of numerous ways to mitigate the issues you mention. E.g.: * Does it actually matter that the absolute value of temperature sensors is inaccurate, or is it enough that they have good relative accuracy (do they?) * Do you really need to tell if someone is indoors or outdoors given a large enough sample of users in a given area? That also seems like something you could detect with heuristics (work hours, jumps up/down in temperature). There's a lot of ways to tease useful signals from noisy data. Is it really the case that smart phone temperature sensors in the aggregate are useless as an input into weather forecasting models?
    - mrow84 11y ago
      I also know nothing about the limitations of the temperature sensors on smartphones (I don't even know of any phones that have temperature sensors). What I do know is that whilst teasing signals out of noisy data is achievable when the data are unbiased, the possibility of biases makes it significantly harder, especially when you don't know what the biases might be. This can be a significant problem with all sorts of data that you might think were fairly good, including both in-situ and satellite measurements.
    - NickNameNick 11y ago
      I'm not sure how useful the usual techniques for normalising sensors would be for moving sensors. Fixed weather stations have corrective mappings associated with them. For example the station at the airport might be assumed to be 1 degree warmer than the generalised temperature for your city. It might be known to collect 5% more rain when the wind is northerly, and 8% less for southerly. These mappings are very important, because they let you shut-down a weather station, and build a new one in a different location, and compare the data from the two. if the weather stations aren't in consistent locations, with discoverable local behaviors, then converting the raw data into generalised data about the area is going to be very difficult.
    - nradov 11y ago
      I'm not an expert but have played around with sensors on some Android devices. The absolute thermometer accuracy will vary by device. When the temperature rises by 1℃ over the course of an hour is that because it actually got warmer, or is because the user moved inside or put it in a warm jacket pocket or ran another app that caused the CPU to heat up? In theory I suppose if you had thousands of devices in a small area uploading simultaneous temperature readings you could perform a regression analysis and extract a little bit of signal from the noise. But in practice I just don't see how you could ever get enough device density in a small area to make that work. Remember that in urban areas the vast majority of smart phones spend most of their time inside climate-controlled vehicles and buildings, so any temperature readings from those are worthless for weather prediction. But they do still give useful barometric pressure data because the pressure is fairly consistent inside and outside for a given location and altitude.

cryptoz 11y ago

Sure, that's true - but they can just as happily run their GPUs if they are plugged in and charging without any battery issues. Although for the moment you are right, they aren't that useful as a powerful distributed supercomputer. They ARE, however, really really good at collecting live weather data and sending that to the supercomputer to process. One step at a time.

nradov 11y ago
Smart phones are good at collecting live barometric pressure data but not much else. Temperature sensors aren't very useful because they are poorly calibrated, you can't reliably tell whether someone is outdoors, and the reading can be thrown off by the heat generated by the device or the person holding it. UV sensors are seldom pointed in the right direction. And I haven't seen a phone with a wind speed or precipitation sensor yet.
- avar 11y ago
  I genuinely don't mean to sound passive aggressive when I ask, do you know anything about this subject or are you just guessing? I don't know anything about it, but I can think of numerous ways to mitigate the issues you mention. E.g.: * Does it actually matter that the absolute value of temperature sensors is inaccurate, or is it enough that they have good relative accuracy (do they?) * Do you really need to tell if someone is indoors or outdoors given a large enough sample of users in a given area? That also seems like something you could detect with heuristics (work hours, jumps up/down in temperature). There's a lot of ways to tease useful signals from noisy data. Is it really the case that smart phone temperature sensors in the aggregate are useless as an input into weather forecasting models?
  - mrow84 11y ago
    I also know nothing about the limitations of the temperature sensors on smartphones (I don't even know of any phones that have temperature sensors). What I do know is that whilst teasing signals out of noisy data is achievable when the data are unbiased, the possibility of biases makes it significantly harder, especially when you don't know what the biases might be. This can be a significant problem with all sorts of data that you might think were fairly good, including both in-situ and satellite measurements.
  - NickNameNick 11y ago
    I'm not sure how useful the usual techniques for normalising sensors would be for moving sensors. Fixed weather stations have corrective mappings associated with them. For example the station at the airport might be assumed to be 1 degree warmer than the generalised temperature for your city. It might be known to collect 5% more rain when the wind is northerly, and 8% less for southerly. These mappings are very important, because they let you shut-down a weather station, and build a new one in a different location, and compare the data from the two. if the weather stations aren't in consistent locations, with discoverable local behaviors, then converting the raw data into generalised data about the area is going to be very difficult.
  - nradov 11y ago
    I'm not an expert but have played around with sensors on some Android devices. The absolute thermometer accuracy will vary by device. When the temperature rises by 1℃ over the course of an hour is that because it actually got warmer, or is because the user moved inside or put it in a warm jacket pocket or ran another app that caused the CPU to heat up? In theory I suppose if you had thousands of devices in a small area uploading simultaneous temperature readings you could perform a regression analysis and extract a little bit of signal from the noise. But in practice I just don't see how you could ever get enough device density in a small area to make that work. Remember that in urban areas the vast majority of smart phones spend most of their time inside climate-controlled vehicles and buildings, so any temperature readings from those are worthless for weather prediction. But they do still give useful barometric pressure data because the pressure is fairly consistent inside and outside for a given location and altitude.