First and foremost, let’s discuss the script which sends the sensor readings as HTTP requests. It is a modified version of the example script provided directly by ada fruit’s documentation. The data structure of the incoming data readings are already laid out in convenient dictionary matter, so we simply need to iterate through and make the values match up with how we lay out our schema on container creation. So first, here’s the script for container creation:<\/p>\n
\n
import http.client\nimport json\n\nconn = http.client.HTTPSConnection(\"cloud5197.griddb.com\")\npayload = json.dumps({\n \"container_name\": \"aqdata\",\n \"container_type\": \"TIME_SERIES\",\n \"rowkey\": True,\n \"columns\": [\n {\n \"name\": \"ts\",\n \"type\": \"TIMESTAMP\"\n },\n {\n \"name\": \"pm1\",\n \"type\": \"DOUBLE\"\n },\n {\n \"name\": \"pm25\",\n \"type\": \"DOUBLE\"\n },\n {\n \"name\": \"pm10\",\n \"type\": \"DOUBLE\"\n },\n {\n \"name\": \"pm1e\",\n \"type\": \"DOUBLE\"\n },\n {\n \"name\": \"pm25e\",\n \"type\": \"DOUBLE\"\n },\n {\n \"name\": \"pm10e\",\n \"type\": \"DOUBLE\"\n },\n {\n \"name\": \"particles03\",\n \"type\": \"DOUBLE\"\n },\n {\n \"name\": \"particles05\",\n \"type\": \"DOUBLE\"\n },\n {\n \"name\": \"particles10\",\n \"type\": \"DOUBLE\"\n },\n {\n \"name\": \"particles25\",\n \"type\": \"DOUBLE\"\n },\n {\n \"name\": \"particles50\",\n \"type\": \"DOUBLE\"\n },\n {\n \"name\": \"particles100\",\n \"type\": \"DOUBLE\"\n }\n ]\n})\nheaders = {\n 'Content-Type': 'application\/json',\n 'Authorization': 'Basic <redacted>'\n}\nconn.request(\"POST\", \"\/griddb\/v2\/gs_clustermfcloud5197\/dbs\/B2xcGQJy\/containers\", payload, headers)\nres = conn.getresponse()\ndata = res.read()\nprint(data.decode(\"utf-8\"))<\/redacted><\/code><\/pre>\n<\/div>\nFor the schema, we simply just used a 1:1 mapping to the aqdata<\/code> dictionary returned by our sensor readings. Even if we don’t intend to use all of these data points, the data readouts are so small, we keep them in.<\/p>\nNext, let’s take a look at the script that will push sensor readings to our GridDB Cloud instance. It will read the sensor data every 1 second, and then make an HTTP Request every second to push that data into the Cloud.<\/p>\n
\n
# SPDX-FileCopyrightText: 2021 ladyada for Adafruit Industries\n# SPDX-License-Identifier: MIT\n\n\"\"\"\nExample sketch to connect to PM2.5 sensor with either I2C or UART.\n\"\"\"\n\n# pylint: disable=unused-import\nimport time\nimport datetime\nimport board\nimport busio\nfrom digitalio import DigitalInOut, Direction, Pull\nfrom adafruit_pm25.i2c import PM25_I2C\n\n\nimport http.client\nimport json\n\nconn = http.client.HTTPSConnection(\"cloud5197.griddb.com\")\nheaders = {\n 'Content-Type': 'application\/json',\n 'Authorization': 'Basic <redacted>'\n}\n\nreset_pin = None\ni2c = busio.I2C(board.SCL, board.SDA, frequency=100000)\n# Connect to a PM2.5 sensor over I2C\npm25 = PM25_I2C(i2c, reset_pin)\n\nprint(\"Found PM2.5 sensor, reading data...\")\n\nwhile True:\n time.sleep(1)\n\n try:\n aqdata = pm25.read()\n # print(aqdata)\n current_time = datetime.datetime.utcnow().replace(microsecond=0)\n now = current_time.strftime('%Y-%m-%dT%H:%M:%S.%fZ')\n # print(now)\n temp = []\n temp.append(now)\n except RuntimeError:\n print(\"Unable to read from sensor, retrying...\")\n continue\n \n for data in aqdata.values():\n temp.append(data)\n payload = json.dumps([temp])\n print(payload)\n conn.request(\"PUT\", \"\/griddb\/v2\/gs_clustermfcloud5197\/dbs\/B2xcGQJy\/containers\/aqdata\/rows\", payload, headers)\n res = conn.getresponse()\n data = res.read()\n print(data.decode(\"utf-8\"))<\/redacted><\/code><\/pre>\n<\/div>\nAs explained above, there is nothing fancy or extraordinary about this script; it simply reads sensor data and then immediately pushes it out to the Cloud with a timestamp attached. The one thing to note, though, is that the GridDB Cloud is by default in UTC Time, so I have altered the timestamps to be attached to the data results to match UTC for consistency’s sake.<\/p>\n
And now that we have our data available in the cloud, we can move on to integrating it with other technologies.<\/p>\n
Home Assistant<\/h3>\n
Home automation software comes in many varieties, with many companies providing their own hubs sold alongside their own products. Home assistant is unique in that it is, 1. completely open source, and 2. made to integrate with all manner of physical and virtual devices. For example, in my own personal home environment, I have Home Assistant running on a Raspberry Pi. To allow it to communicate with other physical devices, I have installed a Zigbee\/Z-Wave USB Stick. If you are unfamiliar, Zigbee and Z-Wave are protocols used by smart home devices to communicate with their hubs. In my case, most of my smart light bulbs communicate through Zigbee, for example.<\/p>\n
In any case, through Home Assistant, we can create various scripts\/automations for getting things done. Some examples can be: turn on bedroom lights at 1% at wake up time, or play my Spotify playlist every day at lunch time, etc etc. In the case of our air quality sensor, we can send out a direct HTTP Query against our sensor data and do something<\/code> if our readings are higher than a certain threshold. For this blog, I have set up my home to turn my living room light bulbs on<\/code> and to red<\/code> if the pm1<\/code> particles are above a certain threshold, over the past 1 hour. But of course, because Home Assistant is flexible, you could set up any sort of notification method you’d like, including emails, phone push notifications, or even turning on your robovac!<\/p>\nSo, to continue on, we will need to first formulate our query on sensor readings, learn how to make HTTP Requests through Home Assistant, and learn how to act on the data returned by our query. After we set up our notifcation system for high sensor reading averages, we will also want to display all sensor readings in our Home Assistant dashboard.<\/p>\n
Formulating our Query<\/h4>\n
First, let’s get our query settled. From my cursory research, pm1<\/code> particles are potentially the most threatening to our health because those particles are so<\/em> tiny they can be inhaled and absorbed directly through the lungs; we will want to set up an alert for these particles.<\/p>\nBefore we begin, Let’s use cURL<\/code> or Postman to test our HTTP Queries until we get what we are looking for; in my case, I used postman until I was happy with the results. The query I settled is the following: SELECT AVG(pm1) FROM aqdata WHERE ts > TIMESTAMP_ADD(HOUR, NOW(), -1) AND ts < NOW() AND pm1 > 10 \"<\/code>. This query will look at the data from the past 1 hour and will return with data if the avg value is over 10. Though please note I did not do strict research on what consitutes an unhealthy amount of pm1<\/code> particles, this is simply for demo purposes. But now that we have our query, we can figure out how to make HTTP REQUESTS<\/code> through Home Assistant.<\/p>\nMaking HTTP Requests with Home Assistant<\/h4>\n
Within the \/config<\/code> directory of the Home Assistant, there are a bunch of yaml<\/code> files which are used to make configuration and automation changes to the software itself — very flexible and customizable. In our case, we want to add what is called a rest_command<\/code> inside of the configuration yaml. We also would like to add an automation of what action to take based on the returned data from our rest_command<\/code> — this will happen in the scripts.yaml<\/code> file. Please note that all yaml<\/code> files are included in the source code in the GitHub page linked above.<\/p>\nSo, here is what our configuration.yaml<\/code> file will need to make the HTTP Request:<\/p>\n\n
#configuration.yaml\nrest_command:\n griddb_cloud_get_aqdata:\n url: https:\/\/cloud5197.griddb.com\/griddb\/v2\/gs_clustermfcloud5197\/dbs\/B2xcGQJy\/sql\n method: post\n content_type: \"application\/json\"\n headers:\n authorization: \"Basic <redacted>\"\n payload: '[{\"type\" : \"sql-select\", \"stmt\" : \"SELECT AVG(pm1) FROM aqdata WHERE ts > TIMESTAMP_ADD(HOUR, NOW(), -1) AND ts < NOW() AND pm1 > 10 \"}]'<\/redacted><\/code><\/pre>\n<\/div>\nYou can see here we included all neccesary things to make an HTTP Sql Select request to our Cloud instance. Here, we are naming our rest_command<\/code> as griddb_cloud_get_aqdata<\/code>, so now in our scripts<\/code> file we can directly call upon this service<\/code><\/p>\n\n
#scripts.yaml\nget_griddb_data:\n sequence:\n - service: rest_command.griddb_cloud_get_aqdata\n response_variable: aqdata\n - if: \"{{ aqdata['status'] == 200 }}\"\n then:\n - alias: Parse data\n variables:\n results: \"{ {aqdata['content']['results'] }}\"\n - if: \"{{ results != 'null'}}\"\n then:\n service: light.turn_on\n target:\n entity_id: light.living_room\n data:\n rgb_color:\n - 240\n - 0\n - 0<\/code><\/pre>\n<\/div>\nAs you can see, we are calling rest_command.griddb_cloud_get_aqdata<\/code> as a service. This will run the HTTP Request and then parse the resulting data. If the results are null (meaning no data over our threshold), nothing will happen, but if we do get some data, we can take some action — or in this case, change the living room lights to on<\/code> and change the RGB to completely red, this way everybody in the home knows that the air quality is compromised in some way.<\/p>\nSo to explain a bit more about how the yaml files work, the configuration yaml allows you to create services<\/code>, in our case, the HTTP Request. The scripts file is for actions which may be run many times in many different spots, a bit analoguous to functions in software. And lastly we will use automations.yaml<\/code> which sets up the trigger to when our script should be running. In our case, we want it run every 10 minutes — that is, every 10 minutes our Home Assistant will look at the average pm1<\/code> levels over the past 1 hour and take action if it is too high.<\/p>\n\n
#automations.yaml\n- id: '1713475588605'\n alias: Get GridDB Data\n description: get the griddb data\n trigger:\n - platform: time_pattern\n minutes: \/10\n condition: []\n action:\n - service: script.get_griddb_data\n mode: single<\/code><\/pre>\n<\/div>\nYou can see here that the automation is calling upon our script to get the GridDB data. You can also see that our trigger is every 10 minutes.<\/p>\n
Displaying Sensor Data Onto our Home Assistant Dashboard<\/h4>\n
The last thing we would like to accomplish is to show our sensor readings directly onto the Home Assistant Dashboard. This will allow for all home users to constantly be aware othe readings. To do this, we will need to use the sensors.yaml<\/code> file and establish new virtual “sensors” of HTTP Requests reading the sensor avg sensor data. For this, we needed to formulate a new SQL Query and this time around, I think we could make do with average readings for the past 24 hours. That query looks like this: SELECT ROUND(AVG(pm1)),ROUND(AVG(pm25)),ROUND(AVG(pm10)),ROUND(AVG(particles03)),ROUND(AVG(particles05)),ROUND(AVG(particles10)),ROUND(AVG(particles25)),ROUND(AVG(particles50)),ROUND(AVG(particles100)) FROM aqdata WHERE ts > TIMESTAMP_ADD(DAY, NOW(), -1) AND ts < NOW()<\/code>. We simply grab all rounded averages from the past 1 day and use that info to be displayed in the dashboard.<\/p>\n\n
#sensors.yaml\n- platform: rest\n resource: https:\/\/cloud5197.griddb.com\/griddb\/v2\/gs_clustermfcloud5197\/dbs\/B2xcGQJy\/sql\n method: POST\n headers:\n authorization: \"Basic <redacted>\"\n Content-Type: application\/json\n payload: '[{\"type\" : \"sql-select\", \"stmt\" : \"SELECT ROUND(AVG(pm1)),ROUND(AVG(pm25)),ROUND(AVG(pm10)),ROUND(AVG(particles03)),ROUND(AVG(particles05)),ROUND(AVG(particles10)),ROUND(AVG(particles25)),ROUND(AVG(particles50)),ROUND(AVG(particles100)) FROM aqdata WHERE ts > TIMESTAMP_ADD(DAY, NOW(), -1) AND ts < NOW() \"}]'\n value_template: \"{{ value_json[0].results[0][0] }}\"\n name: \"Particle Matter 1\"\n scan_interval: 3600<\/redacted><\/code><\/pre>\n<\/div>\nHere we use the value_template<\/code> as the result that will be shown when we select this sensor as an entity. Unfortunately, I could not figure out how to use one singular HTTP Request with multiple values, so I needed to make each value as its own unique HTTP Request, just with a different index array position for the results. The example shown above, for instance, is for pm1<\/code> as it is index 0 of our result array due to the container schema.<\/p>\nSo now that we have our sensor set up, we can go to the dashboard and add it to be displayed. We can click edit (the pencil in the top right corner), then add card, then glance card, and then manually add all of your sensors. For me, the text editor looks like this:<\/p>\n
\n
show_name: true\nshow_icon: true\nshow_state: true\ntype: glance\nentities:\n - entity: sensor.particle_matter_1\n - entity: sensor.particle_matter_2_5\n - entity: sensor.particle_matter_10\n - entity: sensor.particles_03\n - entity: sensor.particles_05\n - entity: sensor.particles_10\n - entity: sensor.particles_25\n - entity: sensor.particles_50\n - entity: sensor.particles_100\ntitle: Daily Air Quality Averages\ncolumns: 3\nstate_color: false<\/code><\/pre>\n<\/div>\n![\"\"](\"https:\/\/griddb.net\/wp-content\/uploads\/2024\/05\/home-assistant-dashboard.png\")
<\/a><\/p>\nAnd now we can have our daily averages at a glance. Of course, this just a few of things you can do with this information being so readily available; this really is the beauty of having the open source Home Assistant powering your home automations, and why it’s great to have GridDB Cloud hosting all of your data — it can be accessible from anywhere and you can upload data from anywhere, as long as you have an internet connection. You could, of course, monitor poor air in remote locations, or in locations of loved ones far away and take actions however you see fit.<\/p>\n
Conclusion<\/h2>\n
In this article, we have learned how to connect physical hardware sensor data and how to push all of that wonderful data onto GridDB Cloud. And then we learned that we can take action directly upon that data with Home Assistant.<\/p>\n","protected":false},"excerpt":{"rendered":"
Despite humanity’s (lackluster) efforts, climate change has become an omnipresent, undeniable force. Though there are many side effects that come with a rising global average temperature, today I want to focus on wildfires and their affect on the air quality of all surrounding areas; when a 100-acre-fire is burning up a Californian forest, all of […]<\/p>\n","protected":false},"author":1,"featured_media":30110,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[121],"tags":[],"class_list":["post-46801","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"acf":[],"yoast_head":"\n
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