Beautiful interactive IoT dashboards - IN JUST 15 MINUTES

in this demo we're going to show you how to create beautiful dashboards like this from your IOT data in just a few minutes our use case for this demo is water on roads which can cause real chaos so when big roads are built often a gully or sump is built next to them into which the water can quickly flow and then be slowly pumped out afterwards so at each site we have a sump in it a pump which takes energy to pump the water and then a rain collector which measures how much is raining there are four numbers associated with each site sump level the amount of water in the sump sump limit which is how full the sump can get before it overflows sump pump energy which is the amount of energy consumed by the pump and precipitation intensity which tells us about rainfall all these values are collected in real time by a wireless gateway which sends them over the internet to the cloud so the first thing to do with our new device pilot account is to connect it to that cloud and get hold of the data in this case the data happens to be in the AWS IOT cloud so we're going to show you how easy it is to integrate with that referring to you AWS account you just need to put in your endpoint and your account number and then we give you instructions as to how to create a policy in your AWS IOT account which allows device pilot to see the data in that account and also to extract a private key and a certificate from your AWS IOT account which you then paste into device pilot to allow it to connect and as soon as you've done that device pilot connects and then it can take up to a couple of minutes for the data to start flowing into device pilot and you're integrated so let's go and look at the data the first thing we see in the top left of the view page is a list of the devices and if we select one of them we can then see all the individual messages were getting from that device each message has a unique ID a unique timestamp and then whatever properties the device is sending now that level of detail is probably a bit more than you need for most purposes so we're going to close that window and then notice up at the top of the page there are little buttons that let you turn on and off all the different windows on this page to show different aspects of the devices that you're looking at on the right-hand side we see the details window which shows every detail of this device we can select another device and see of the details of that device each device has a unique ID we see when it was last seen and first seen and then in this case it's got a whole load of address properties that tell us where it is we have firmware versions we have its latitude longitude and we have the various bits of telemetry which we're collecting the precipitation intensity the sump level and so on one of the other windows we can turn on is a map so by selecting a device we can immediately see where that device is device pilot also stores all the data that's sent to it so we can see how values on a device change over time so here if we set up the history chart to show us three different properties that we're interested in the first one is the precipitation intensity how much is raining at the moment the next one is the level in the sump how full our sump is and the final one will be the energy that the sump pump consumes so if we select all those three and say Save Changes then on the history chart we'll see how those values have changed over time off of the top of the screen we can set the time range that we're interested in if we set that to 14 days then we can see how these three value have changed over the last 14 days for the currently selected device here we can see the rainfall level the level in the sump and the energy consumed by the pump changing continuously over that time and if we choose another device we can see its behavior which is being completely different so perhaps we'd like to see these values on the list next to the devices so let's choose some of the device properties to show on that list the administrative area one of the address values what else have we got let's show the precipitation intensity the level of the sump the limits of the sump and how much energy is being consumed by the pump now we've got all of these extra properties on our list we can even sort by them so if we sort by sump level we can see the devices that have the most full sump and here we have the one in Philly where the sump is clearly filling up if we now sue min and only look at three days the last three days we can see that it's been raining continuously the sump has been filling up continuously the pump is running at 2.7 kilowatts continuously trying to empty the sump but it doesn't look like it's having much luck so let's try and filter all that for all the devices that are in that state so the way we create a filter is we just say create filter we give it a name so let's call this one pump running flat out and then the filter simply says that's true if the sump pump energy consumption is more than let's say 2.6 kilowatts and that's true for ten of our 20 devices right now so if we Save Changes we then see just those pumps that are running flat out on our list let's create another filter which detects other conditions that we might care about in this case we want to discover sumps that are overflowing so in this case we'll define that as the sump level property being greater than or equal to some limit property and we see that seven of our 20 devices are currently in that state so filters are just a very useful way to select devices according to their state at any moment in time so now let's move to the cohort page to do slightly more sophisticated analyses and now we're going to try and find out what the maximum value of any sump has been over a certain time period notice at the top it says 24 hours that's a time period so we're asking what's the maximum value that the sump of any device has got - over the last 24 hours and the answers 15 point 1 millimeters let's extend that back to the last 30 days and run the question again and it's still 15 point 1 millimeters so that means over the last 30 days no device has got more no sump has got more full than 15 point 1 millimeters to look in that a little bit more detail if we want to know how full sumps have got over time what's the maximum value over time hour-by-hour over the last 30 days we can just group by time and if we're wondering how that splits up by by region for example by country we can just group by that as well and if we change it to be a more of a bar chart rather than a line chart we can see that it's recently the ones in the united kingdom that have been overflowing not the ones in netherlands so we can name that as maximum some levels and save it so we now have what's called a KPI which we can then use again in other contexts let's create another KPI in this case we're going to just measure the mean the average value of precipitation how much is raining and again we're going to group by country and group by hour change it so that it's over the last 30 days again so this basically says per country how hard has it been raining on average over the last 30 days and again we'll give that a name so we can use it later and save it let's create another KPI this time the metric we're going to use is a slightly more sophisticated one which is measuring the percentage of time where some filter is true so remember that pump running flat out filter that we defined earlier on well this is going to measure how often any pump is running flat out over a period of time and again split by country and this time grouped by week so what this shows us is that actually it's the pumps in the Netherlands that have had the biggest problems quite a big problem at the beginning of this month and another one at the end so let's name that as percentage of time pump running flat out and save it and then just one more KPI that we might be interested in how much is this all costing us so let's create a KPI which is all about the amount of energy that's being used by those pumps and so we're measuring instantaneous power at time intervals so if we sum our sum those values we actually get energy overtime kilowatt hours and so we're measuring the total amount of power consumption or energy consumption by our pumps over the last 30 days split by country again and in this case we can actually stack the columns because they're it's a sum so we can stack the results to get the total amount of energy consumed by our pumps by day over the last 30 days I'll call that pump energy so now we've got all these KPIs we're ready to do the final bit which is to put them all together into a dashboard when you first set up your device pilot account device pilot creates a few widgets on the dashboard to help you get going but we're going to unlock the dashboard and delete most of these because they're not things that we happen to want we'll leave the map widget though we'll just drag it over to the left all the widgets on the dashboard are fully interactive so you can you can click into things that are on the map and see clustered devices and individual devices and here it's showing you the identity the ID of each device but we can make it do other things as well so we could say well on the map I want you to show me one of those KPIs like what is the maximum level of each something and also show a tooltip when I hover over it like what is its administrative area for example and we can give the widget a name maximum sum levels over the last 30 days so let's change the KPI to be 30 days not 34 hours now let's go in and have a look at the ones in the Netherlands just for fun and now we see that as we hover we're actually seeing the hump levels so these are live some levels or live maximum over the last 30 days everything on the dashboard is live all the time and updating let's create another widget and in this case we'll show one of our KPIs we want to do the rainfall by locations so we just choose the matching KPI and again set the time period to 30 days it's often helpful if you choose the same time period for everything on each dashboard just so it's consistent otherwise it can be a bit confusing and here we see that that KPI that we created earlier all nicely positioned on the dashboard and and fully interactive and so on and we can do the same thing with the other KPIs we created so how much pump energy is being used by country over time the maximum levels that any sumps have got to overtime and the percentage of time that the pump is running flat out so we're in danger of not clearing the sump actually I've just realized that we forgot to create a KPI for what I wanted to do for the final widget so let's go back to the cohort page and create a new KPI which again is percentage of time but as a percentage of time where the sump is overflowing and again let's split it by country and by day and let's just have a look to see what that KPI will look like before we go to the dashboard it was good let's turn it into bars it's not turned into bars let's give it a name to bars and save it so now we can go to our we have to remember to unlock the dashboard again so we can change it give the widget a title choose the some overflow KPI Save Changes and we've got our dashboard and remember you can have multiple dashboards you can put them on the walls of your office everyone can see them and they update live so anyone will be knowing what's going on your business all the time not only are they live but they're actually interactive so you can click through from the dashboard to go in and have a look at that KPI in more detail and then you can actually click through from the KPI to see one bars worth of devices selected and go and look at them in more detail so these are the devices that were in that part of the KPI and now he defines him KPIs we can actually see KPIs in the view page as well so as well as seeing individual telemetry values we can also see these derived metrics that device pilot is calculating for us such as rainfall by location and because they're in the same list we can then sort by them and do all sorts of other interesting exploration so if we choose pump energy and we choose to sort by pump energy then at the end of the list will be the device with the most pump consumption and indeed we see one of our old friends there with the with the sump just continuing to get deeper and deeper even as the pump carries on running so I hope that shown you how in about quarter of an hour we've gone from fresh data ingested in device pilots into having a really quite nice good-looking meaningful useful dashboard that everyone in the company can use to do their job so just to recap what we've seen we've seen device properties that raw telemetry that comes in from the devices we then seen how we can use filters to identify devices where the properties are in a particular state we then seen how we can use those filters and properties to build KPIs that tell us the key metrics that we care about and how they change over time and finally we've seen how to combine those KPIs onto good-looking interactive dashboards thanks so much for watching contact us today to get working on your first device pilot dashboards

Channel: DevicePilot

Views: 3,620

Keywords: IoT, dashboard, metrics, devicepilot, analytics, monitoring, KPI, SLA, performance, agile

Id: zDDPiLDNxLk

Length: 16min 36sec (996 seconds)

Published: Mon Apr 15 2019