The Air Quality Monitor – Data processing

After implementing and deploying the DSM501a based Air quality monitor, data was collected into an InfluxDB database and consumed by a Grafana Dashboard:

DSM501a and BMP180 Grafana Dashboard

While we can see that Temperature (yes it’s hot!) and Pressure looks ok, data collected from the DSM501a sensor is just a mess. It’s just a bunch of samples jumping around across several values, and so doesn’t look very promising that we can take meaningful data out of it.

So we might need to process the data so that it makes sense, and for that based on the fact that:

  1. Data sample is slow: 2 samples per minute
  2. We don’t want high variations, but smooth out the data

I’ve choosen to filter data using an IIR (Infinite Response Filter) LPF (Low pass filter) to remove any high frequency data/noise, and hence obtain a smoother output.
Did it work? Yes it did:

Original data vs IIR LPF filtered data

As we can see for each collected particle size of 1.0 and 2.5 we’ve filtered it with an IIR LPF that smoothed out any wild transitions while keeping the fundamental and underlying data validation.

IIR implementation is quite simple since it is only a set of additions, subtractions and multiplications with some factors that define the behavior of the filter.

IIR Filter

(The picture was taken from here that also explains nicely what is an IIR and FIR filters).

The input x[]n is DSM501a sample time at t=0, t=30s, t=60s, … and so on, and y[]n is the corresponding output. The b0,b1,b2 and a0,a1 and a2 are the filter factors, that define the filter response. For testing purposes I’ve just choose factors for a 1KHz Low Pass filter and tested it during several days, and hence the above output that can be seen on the Grafana dashboard.

The IIR filtering process is done on Node-Red but it can be done easily also on the ESP8266 since there is no complicated math/algorithms involved.

Node-Red IIR LPF filter

The function that implements the IIR LPF filter is (Note that on the code I use the a’s as the input factors and b’s as the output factors which is the contrary of the above IIR picture):

// IIR LPF factors
  f_a0 = 0.0010227586546542474;     // Input factors
  f_a1 = 0.002045517309308495;
  f_a2 = 0.0010227586546542474;
  f_b1 = -1.9066459797557103;       // Output factors
  f_b2 = 0.9107370143743273;

// PPM 1.0 input variables
var i0_c10 = msg.payload.cPM10;
var i1_c10 = context.get('i1_c10') || 0;
var i2_c10 = context.get('i2_c10') || 0;

// PPM 1.0 output variables
var o0_c10 = context.get('o0_c10') || 0;
var o1_c10 = context.get('o1_c10') || 0;

// Calculate the IIR
var lpf =   i0_c10 * f_a0 + 
            i1_c10 * f_a1 + 
            i2_c10 * f_a2 -         // We add the negative output factors
            o0_c10 * f_b1 - 
            o1_c10 * f_b2;
// Memorize the variables
context.set( 'i2_c10' , i1_c10 );
context.set( 'i1_c10' , i0_c10 );

context.set( 'o1_c10' , o0_c10 );
context.set( 'o0_c10' , lpf );

// PPM 2.5 input variables
var i0_c25 = msg.payload.cPM25;
var i1_c25 = context.get('i1_c25') || 0;
var i2_c25 = context.get('i2_c25') || 0;

// PPM 1.0 output variables
var o0_c25 = context.get('o0_c25') || 0;
var o1_c25 = context.get('o1_c25') || 0;

// Calculate the IIR
var lpf25 =   i0_c25 * f_a0 + 
              i1_c25 * f_a1 + 
              i2_c25 * f_a2 -         // We add the negative output factors
              o0_c25 * f_b1 - 
              o1_c25 * f_b2;
// Memorize the variables
context.set( 'i2_c25' , i1_c25 );
context.set( 'i1_c25' , i0_c25 );

context.set( 'o1_c25' , o0_c25 );
context.set( 'o0_c25' , lpf25 );

msg.payload = {}
msg.payload.cfP10 = lpf;
msg.payload.cfP25 = lpf25;

return msg;

We maintain the filter state (the two previous samples from the sensor) on Node-Red global variables (which will be reset if Node-red is restarted), and calculate for each PM1.0 and PM2.5 sample the filtered value, which depends on the previous samples. The final output is then fed to an InfluxDB sink node which saves the filtered data.
The complete code is at this gist.

While still this being a test by using a probably LPF filter that is not adequate to the sampled data (it was designed for Audio at 96Khz sample rate), it shows that we can do some simple processing to clean up inbound data so that it makes more sense. This mechanisms of using digital filtering signals (DSP) are applied widely in other systems such as electrocardiogram sensors or other biometric sensors the remove or damp signal noise. In this case we can see that after the filtering data looks much more promising to be processed and so be used to calculate the Air Quality Index without the index jumping around as the samples jump.

ESP32/ESP8266 MQTT Socket error on client – Disconnecting

When using the MQTT library for the ESP8266 or ESP32, namely this one, when publishing data on the Mosquitto I got the bellow error, followed immediately by a client disconnect:

1589388307: New client connected from as ESP32-node (c1, k60, u'ESP32ETHE').
1589388312: Socket error on client ESP32-node, disconnecting.

One of the key issues with this library is first to ensure that the loop() function is periodically called before the MQTT connection timeout is reached.
But this was not the issue.

The issue was that the message payload for a specific topic was too big for the pre-allocated buffer of the MQTT client. So

MQTTClient mqttClient;

must be changed to this

MQTTClient mqttClient(1024); 

where 1024 is the maximum expected payload size. So we can changed to smaller or bigger depending on the situation.

With this change, the issue was gone. So moral of the story: beware of payload size.

ESP32 TTGO board and Nordic Thingy:52 sensor device

The Nordic Thingy:52 is a 40€/50€ device based on the nRF52832 Nordic microcontroller that has, in a ready to use package, several environmental sensors, that can be accessed by low power Bluetooth (BLE). Nordic provides a complete solution that comprises the Thingy:52 firmware already flashed on the device (Source at GitHub) and an very nice Android Nordic Thingy:52 application, with also sources available at GitHub.

Anyway I have some of these devices for some months now, for other uses, but I decided to test the ESP32 based boards, since the ESP32 has Bluetooth and theoretically can connect and gather gather data from the Thingy. So this post is about the use of the TTGO ESP32 Lora based boards with an OLED to gather data, show it on the OLED, and send it to The Things Network. Seems simple, right?

So when a application connects to the Thingy:52 it can be notified when a sensor value changes throught the standard BLE notification mechanisms. The way the Thingy firmware works, this notification happens at a fixed intervals instead of a value change, and that interval, 5 seconds, 10 seconds, be defined by the Android App or programmatically by our application.

The application is developed by using the PlatformIO and for using the ESP32 Bluetooth interface, I’ve used the NKolban ESP32 BLE Library that happens to be library 1841 at the Platformio repository.

To cut a long story short, as still of today, the ESP32 BLE library doesn’t work correctly with the Thingy:52 notifications. This means that the application subscribes to have notifications, but those never happen. Fortunately someone already hit this problem and solved the issue, but the correction still hasn’t hit the library.

So basically to have my code example to work the following steps are needed:

  1. 1. Clone the TTGO ESP32 repository from . The repository uses the PlatformIO to build the application.
  2. 2. At the root of the repository run the command pio run so that the libraries are downloaded and installed.

At this point we need to correct the Arduino ESP32 library to add the patch to the notification issue.
Just execute the command:

[pcortex@pcortex:ESP32_NordicThingy|master *]$ cd .piolibsdeps/ESP32\ BLE\ Arduino_ID1841/src

At this directory (.piolibsdeps/ESP32\ BLE\ Arduino_ID1841/src edit the file BLERemoteDescriptor.cpp and at around line 151 (the exact line number will probably change in the future) we must change the ::esp_ble_gattc_write_char_descr function parameters:

 * @brief Write data to the BLE Remote Descriptor.
 * @param [in] data The data to send to the remote descriptor.
 * @param [in] length The length of the data to send.
 * @param [in] response True if we expect a response.
void BLERemoteDescriptor::writeValue(
        uint8_t* data,
        size_t   length,
        bool     response) {
    ESP_LOGD(LOG_TAG, ">> writeValue: %s", toString().c_str());
    // Check to see that we are connected.
    if (!getRemoteCharacteristic()->getRemoteService()->getClient()->isConnected()) {
        ESP_LOGE(LOG_TAG, "Disconnected");
        throw BLEDisconnectedException();

    esp_err_t errRc = ::esp_ble_gattc_write_char_descr(
        length,                           // Data length
        data,                             // Data
    if (errRc != ESP_OK) {
        ESP_LOGE(LOG_TAG, "esp_ble_gattc_write_char_descr: %d", errRc);
    ESP_LOGD(LOG_TAG, "<< writeValue");
} // writeValue

We need to change the highlighted line to:

    esp_err_t errRc = ::esp_ble_gattc_write_char_descr(
        length,                           // Data length
        data,                             // Data

With this change, the code at my github repositories has a working example:

– The ESP32 connects to the Nordic Thingy:52 device.
– It programs the Nordic Device to notify sensor values each 5 seconds (in real use cases it should be much larger period)
– Current sensor data is shown on the serial terminal.

What needs to be done:
– When notified by the Thingy:52, the ESP32 shows the new data on the OLED screen (WIP – Work in progress).
– To keep the application obeying the ISM bands duty cycle, it collects the data, calculates the medium, and sends the data to the Things network each 10 minutes (Also work in progress).

Simple BLE bridge to TTN Lora using the TTGO ESP32 LoRa32 board

The TTGO LoRa32 is an ESP32 based board that features Wifi and BlueTooth low energy but also includes an external Lora chip, in my case the SX1276 868Mhz version.

The following code/hack is just to test the feasibility of bridging BLE devices over the ESP32 and then to Lorawan, more specifically sending BLE data to the LoraWan TTN network.

I’m using Neil Koban ESP32 BLE library, that under platformIO is library number 1841 and the base ABP code for connecting to TTN.

In simple terms this code just makes the ESP32 to emulate a BLE UART device for sending and receiving data. It does that by using the Nordic UART known UUID for specifying the BLE UART service and using also the Nordic mobile applications, that supports such device, for sending/receiving data.

Using the Nordic mobile Android phone applications, data can be sent to the Lora32 board either by using the excellent Nordic Connect application or by also using the simpler and direct Nordic UART application.

The tests program just receives data through BLE and buffers it onto an internal message buffer that, periodically, is sent through Lora to the TTN network. I’ve decided arbitrary that the buffer is 32 bytes maximum. We should keep our message size to the necessary minimum, and also just send few messages to keep the lorawan duty factor usage within the required limits.

So, using the following code we can use our phone to scan from the ESP32 BLE device named TTGOLORAESP32 connect to it and send data to the device.

After a while, when the transmission event fires up, data is transmitted, and the BLE device just receives a simple notification with the EV_TXCOMPLETE message.

That’s it.