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Influxdb has been installed with Docker and is already running.
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If we want to see what's inside, then we can connect to it.
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Thanks to a user interface available on port 8086.
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If you've used my configuration file, the username and password are "admin" for the login and "univ-lorawan"
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for the password.
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We could even build dashboard with this influxdb container, but it's not widely used, so we're going
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to stick with the original ID to use Grafana to build our dashboard for us.
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This simple interface is just going to be useful to dive into the database and verify the stored data.
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On the left menu, we can click on the Data Explorer section where we'll find our bucket called IoT-platform
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...
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This bucket has been created during installation exactly as it was instructed in the Docker compose
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configuration file.
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A bucket is just a name of a place where we're going to store our data called measurements.
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Of course, our bucket is empty right now because we haven't stored anything yet, so let's do it.
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So back in Node-RED, we need to use a node called Influxdb out and that is with this node that we're
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going to feed the database with the data received by our subscriber.
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So the first idea is to connect directly the MQTT subscriber to the influxdb node.
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If this inflow node wants to be able to connect to the database, we need to configure it so it can
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have the credentials.
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So we click on the influxdb out node, we give a name to the node, let's call it InfluxDB.
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Then we have to add the information of the influxdb
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instance, exactly
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in the same way we add a new MQTT broker earlier, but this time this is where stands our database.
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So we click on Add a new influxdb.
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Let's give again a name to this influxdb instance, and we choose the version.
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In our case, we're using version two.
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We now need the database URL, Iot-app.univ-lorawan.fr on port 8086 and the token again, you need to provide
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the one we put in the configuration file or the one you've created in influxdb.
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The organization is training-usmb and the bucket is iot-platform.
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Again, same information that is in the Docker compose file.
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And this time we'll ask him to store a new set of data.
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We'll ask it to do it in a measurement called LoRaWAN sensor.
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But of course you can choose your own measurement name.
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So now, what happens if a new uplink frame is received?
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Nothing much except that we still receive the debug information from the MQTT subscriber.
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So let's see what has been stored in the database in the IoT platform bucket.
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A new measurement called lorawan-sensors has appeared.
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That is a very good news.
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That means that we didn't meet any issues to connect to our database.
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But if we try to see what's in our measurement, there is nothing.
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Actually, this is quite normal because of course we can't just send the overall object from the MQTT
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subscriber with everything in it influxdb doesn't understand what we really want to do, and when
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it tries to guess, then it mostly finds string value and influxdb can only deal with numbers.
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I remind you that we only want to store the temperature and the RSSI of the LoRaWAN device, so we need to
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extract this data from this object and we want that the number stored in the database is stored with
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the name of the value, which will be "temperature" or "RSSI", depending on the value we store.
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If we want to do that, then we need to provide a clear message to the database.
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This is not complicated at all, but we need to comply with a specific format.
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So we need to put a new node between the MQTT subscriber and Influxdb.
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This new node is going to build the message to send to the Influxdb with only the relevant value.
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This node is called function.
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And inside we have to provide some JavaScript code.
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We could have done this in no code by using only nodes, but in the end I think it's more complicated.
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So to make things easy, I provide a template which is this one.
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This is an array of two objects.
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One is here and the other one is there.
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The first one at the top is a list of the value we want to store in the database.
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For each of them, we need to provide a name and where to find the value.
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So, for example, the first value we're going to store will be called "temperature".
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And to get the corresponding temperature value, we need to follow the corresponding path of the object
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coming from the MQTT subscriber.
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So if we look at the object provided by the subscriber, then we see that to get the temperature we
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need to select the field:
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"Msg.payload" It's the standard preamble for an output object.
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Then "uplink message", "decoded payload", "temperature."
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This is what we need to provide on the first line.
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In Node-RED there is a very useful button.
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We want to pick up the path of a value called copy path so we don't have to remember all the object
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hierarchy.
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If we follow the same process, then for the second line we can call the value RSSI and to pick up the
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value we go back on the object and we copy the path.
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There we go.
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Now there is another object at the bottom for Influxdb.
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This object represents the tag that we want to provide.
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It's optional, but maybe it could be very useful to know from which device ID or from which application
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the data comes from.
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So the tags are often information that can't be represented in a graph because it's usually a string
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anyway.
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But for example, it can be very useful to filter a range of value among a dataset.
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So when we query the database, if we use tags, then we'll be able to select only the temperature of
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a specific device, for example.
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Great.
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Let's try that.
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For each temperature on our RSSI value stored in the database, we're going to store the name of the
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device.
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So we write here "device".
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And the corresponding name can be found here.
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So once again, we copy the path and we provide it to our JSON object.
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Okay, we're done.
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So now each time a temperature is sent of a LoRaWAN, a JSON object with this specific format is built
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using this function and it contains these informations:
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Temperature, RSSI and the device name.
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Of course, you can add as many values as you want in the function.
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So let's have a look at the database now.
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We select the bucket, the measurement, and here we can see all the stored data.
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Temperature, RSSI and the corresponding device name.
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And that's exactly what we wanted.
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If we submit the query corresponding to the selected field, then we can have the data overview.
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If you're not happy with the query, then you can change the settings and submit a new one.
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Great.
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So now let's finish the most interesting part of our IoT platform.
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We're going to create a dashboard with two charts the temperature and the RSSI.
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And for that, we're going to use Grafana.
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Let's see how it works in the next video.