Category Archives: Raspberry Pi

Matters relating to the development of Raspberry Pi applications, hardware and software

Node-RED and the Internet of Things

Here at Cranfield University we are putting in place plans related to the new ‘Living Laboratory’ project, part of our ‘Urban Observatory’. This project sits within the wider UKCRIC initiative, across a number of universities. Of the many experiments in development, we are gathering environmental data from IoT devices and building data dashboards to show the data and related analyses.

In this blog we investigate the use of Node-RED ( as a programming tool for wiring together hardware devices, APIs and online services, using its browser-based editor to wire together flows using the wide range of nodes in the palette that can be deployed to its runtime in a single-click. Node-RED provides graphical programming tool for Node-JS that permits complex programs to be built pictorially with great ease. To undertake the project, we used a WIO Node device collecting temperature values, exposing these values via a web service, and the Node-RED receiving device being a Raspberry Pi.

Sourcing temperature data – the Wio Node

The Wio Node temperature sensor was described in an earlier blog here ( Temperature values are extracted via a web-based API call, with the REST URL taking the form, thus:

The temperature values are then returned as a JSON string, appearing thus:


Preparing the Raspberry Pi – installing Node-RED

To prepare the Raspberry Pi and install Node-RED, we first followed instructions to install Node-JS on the Pi at Next we followed the instructions on the Node-RED site ( In brief, we ran the Node-RED upgrade script:

bash <(curl -sL

We then set Node-RED to start automatically on boot, with:

sudo systemctl enable nodered.service

Running Node-RED

The Raspberry Pi was then rebooted. We were then able to start using the Node-RED editor (, calling the web-based interface with the URL (the IP address being that if the Raspberry Pi):


The general Node-RED interface, ‘palette’ to the left, properties to the right, and design canvas centrally.

Node-RED allows installation of many modules, one of which permits data dashboards. The data dashboard module is described at Installation can be via npm, as described at the link above. However, we used the ‘Manage Palette’ option within the graphical interface to install the new functions.

With this installed, the next task was to develop the ‘flow’, or programme. This starts with a HTTP GET call to the WIO Node as described above. For this the ‘http request’ node is called, and configured with the URI to the temperature value. After consideration of the various configuration options, we elected to return a ‘parsed JSON object’.

To drive the process whereby the URI is called continuously, the http request call is preceded with an ‘inject node’, set to run continuously on a timed basis (shown here at 5 seconds, although that could be a longer period).

The data that is returned from this process, the ‘payload’, can now be passed directly to the first element of the dashboard – the gauge. The payload JSON object has a member ‘temperature’, referenced via the value format {{payload.temperature}}.

The next dashboard elements we wanted are firstly a line graph of temperature over time, and secondly a custom node recording the ‘minimum’ and ‘maximum’ temperatures over time. These nodes will need data prepared in a particular way. The graph, or chart, needs data in the form described at

{topic:"temperature", payload:22}

In addition, further JSON elements for minimum and maximum values will be required. In order to construct a revised message payload, a custom script is required. Explanations are in the code below:

// Create a new empty object 'newMsg' to return at the end
// then fill it with another empty object 'bounds'
var newMsg={bounds:{}}; // create

// Create two local variables min and max initialised from the persistent 
// context variables of the same names where these values exist, or else
// seed with values we know are off the scale
var min=context.get('min') || 100;
var max=context.get('max') || -100;

// Set an element 'topic' and give the value the string 'temperature'
newMsg.topic = 'temperature';
// Set the payload element to the incoming message payload temperature
newMsg.payload = msg.payload.temperature

// update the min and max, comparing the incoming values to the context
if (msg.payload.temperature < min) {
   newMsg.bounds.min = msg.payload.temperature;
   context.set('min', msg.payload.temperature);
} else {
   newMsg.bounds.min = min;
if (msg.payload.temperature > max) {
   newMsg.bounds.max = msg.payload.temperature;
   context.set('max', msg.payload.temperature);
} else {
   newMsg.bounds.max = max;

// and finally return the new object 'newMsg'
return newMsg;

What is always a good idea when processing data is to have a debug that shows the whole message object constructed by this process. To do this, a ‘debug node’ is added and configured – here to show the ‘complete msg object’. We can see the min and max are contained in the bounds node, and that the ‘topic’ and ‘payload’ elements are correctly configured.

As a result, the two additional dashboard node widgets can be added, first the chart node. The line interpolation is set here to ‘bezier’ to provide a smoother visualisation. The time interval is set to 15 minutes.

Next we wanted to add a new custom node widget to show a running maximum and minimum value. To do this, we added a ‘Template node’ and configured it thus:

<div layout="row" layout-align="start center">
  <span flex>Temp Min: </span>
  <span flex>Temp Max: </span>
<div layout="row" layout-align="start center" ng-repeat="bounds in msg">
  <span flex style="color: green">{{bounds.min}}</span>
  <span flex style="color: red">{{bounds.max}}</span>

Once these elements are all in place, the ‘flow’ programme can be deployed. This commences the running of the code, and then the dashboard can be accessed. The easiest means to do this is to follow the link in the properties section as shown:

The result is the display of the dashboard. To get this to display as required, one can change the visual style (e.g. to ‘dark’), and the dimensions of the canvas. Node dashboard widgets are always rendered to the top left according to the layout properties.


In this blog, we have shown how the Node-RED environment can be used to streamline Node-JS code, with customised elements, and inclusion of libraries of functionality (dashboard). Node-RED is a powerful yet easy to configure environment that is cable of a whole range of functionality though its graphical ‘flows’. There are many example flows available on websites that can be downloaded and tested. Flows are designed to be easily imported and exported. Below is the export for the flow described above – to load it, select ‘Import’ and ‘Clipboard’ from the main menu options and paste in the following.

<div layout="row" layout-align="start center">[{"id":"d988539b.52bdc8","type":"tab","label":"Temperature","disabled":false,"info":""},{"id":"35963a2e.6aa056","type":"tab","label":"Temperature","disabled":false,"info":""},{"id":"166841a0.b19cce","type":"mqtt-broker","z":"","broker":"","port":"1883","clientid":"Teste","usetls":false,"compatmode":true,"keepalive":"60","cleansession":true,"birthTopic":"","birthQos":"0","birthPayload":"","willTopic":"","willQos":"0","willPayload":""},{"id":"a76a54d5.4c5998","type":"ui_tab","z":"d988539b.52bdc8","name":"ESP_DTH11","icon":"dashboard","order":3,"disabled":false,"hidden":false},{"id":"519167a8.570e5","type":"ui_group","z":"d988539b.52bdc8","name":"DHT11","tab":"a76a54d5.4c5998","order":1,"disp":true,"width":"12","collapse":false},{"id":"1785bc54.de4d24","type":"ui_base","theme":{"name":"theme-dark","lightTheme":{"default":"#0094CE","baseColor":"#0094CE","baseFont":"-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Oxygen-Sans,Ubuntu,Cantarell,Helvetica Neue,sans-serif","edited":true,"reset":false},"darkTheme":{"default":"#097479","baseColor":"#097479","baseFont":"-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Oxygen-Sans,Ubuntu,Cantarell,Helvetica Neue,sans-serif","edited":true,"reset":false},"customTheme":{"name":"Untitled Theme 1","default":"#4B7930","baseColor":"#4B7930","baseFont":"-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Oxygen-Sans,Ubuntu,Cantarell,Helvetica Neue,sans-serif"},"themeState":{"base-color":{"default":"#097479","value":"#097479","edited":false},"page-titlebar-backgroundColor":{"value":"#097479","edited":false},"page-backgroundColor":{"value":"#111111","edited":false},"page-sidebar-backgroundColor":{"value":"#000000","edited":false},"group-textColor":{"value":"#0eb8c0","edited":false},"group-borderColor":{"value":"#555555","edited":false},"group-backgroundColor":{"value":"#333333","edited":false},"widget-textColor":{"value":"#eeeeee","edited":false},"widget-backgroundColor":{"value":"#097479","edited":false},"widget-borderColor":{"value":"#333333","edited":false},"base-font":{"value":"-apple-system,BlinkMacSystemFont,Segoe UI,Roboto,Oxygen-Sans,Ubuntu,Cantarell,Helvetica Neue,sans-serif"}},"angularTheme":{"primary":"indigo","accents":"blue","warn":"red","background":"grey"}},"site":{"name":"Node-RED Dashboard","hideToolbar":"false","allowSwipe":"false","lockMenu":"false","allowTempTheme":"true","dateFormat":"DD/MM/YYYY","sizes":{"sx":48,"sy":48,"gx":6,"gy":6,"cx":6,"cy":6,"px":0,"py":0}}},{"id":"749056a0.0a1d28","type":"ui_group","z":"","name":"Chart","tab":null,"order":2,"disp":true,"width":"12","collapse":false},{"id":"684a7caa.4db0f4","type":"ui_group","z":"","name":"Chart","tab":"a76a54d5.4c5998","order":2,"disp":true,"width":"12","collapse":false},{"id":"84ea1128.ec6fd","type":"ui_tab","z":"35963a2e.6aa056","name":"ESP_DTH11","icon":"dashboard","order":3,"disabled":false,"hidden":false},{"id":"c86b0ed1.65efc8","type":"ui_group","z":"35963a2e.6aa056","name":"DHT11","tab":"84ea1128.ec6fd","order":1,"disp":true,"width":"12","collapse":false},{"id":"a7d331dd.9d8078","type":"debug","z":"d988539b.52bdc8","name":"Message object","active":true,"tosidebar":true,"console":true,"tostatus":false,"complete":"true","targetType":"full","x":1129.75,"y":286.9166564941406,"wires":[]},{"id":"aa810265.1f789","type":"ui_gauge","z":"d988539b.52bdc8","name":"Gauge","group":"519167a8.570e5","order":0,"width":"6","height":"2","gtype":"gage","title":"Temperature","label":"Celsius","format":"{{payload.temperature}}","min":0,"max":"60","colors":["#00b500","#e6e600","#ca3838"],"seg1":"25","seg2":"28","x":1086.833251953125,"y":432.8055419921875,"wires":[]},{"id":"aa922201.f96eb8","type":"inject","z":"d988539b.52bdc8","name":"","topic":"","payload":"","payloadType":"date","repeat":"","crontab":"","once":true,"onceDelay":0.1,"x":401.5,"y":394,"wires":[["cbeca854.f6174"]]},{"id":"cbeca854.f6174","type":"http request","z":"d988539b.52bdc8","name":"Wio Temperature","method":"GET","ret":"obj","paytoqs":false,"url":"","tls":"","proxy":"","authType":"basic","x":610.5,"y":394,"wires":[["aa810265.1f789","cd69dcd.1c5d3a"]]},{"id":"af52c259.fecbd8","type":"ui_chart","z":"d988539b.52bdc8","name":"Chart","group":"684a7caa.4db0f4","order":2,"width":"12","height":"7","label":"Temperature chart","chartType":"line","legend":"true","xformat":"HH:mm:ss","interpolate":"bezier","nodata":"","dot":false,"ymin":"","ymax":"","removeOlder":"15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               ","removeOlderPoints":"50","removeOlderUnit":"60","cutout":0,"useOneColor":false,"colors":["#1f77b4","#aec7e8","#ff7f0e","#2ca02c","#98df8a","#d62728","#ff9896","#9467bd","#c5b0d5"],"useOldStyle":false,"outputs":1,"x":1087.8333740234375,"y":384.22222900390625,"wires":[[]]},{"id":"cd69dcd.1c5d3a","type":"function","z":"d988539b.52bdc8","name":"Process temperature","func":"var newMsg={bounds:{}};\nvar min=context.get('min') || 100;\nvar max=context.get('max') || -100;\n\n//\nnewMsg.topic = 'temperature';\nnewMsg.payload = msg.payload.temperature\n\nif (msg.payload.temperature &lt; min) {\n   newMsg.bounds.min = msg.payload.temperature;\n   context.set('min', msg.payload.temperature);\n} else {\n   newMsg.bounds.min = min;\n}\nif (msg.payload.temperature &gt; max) {\n   newMsg.bounds.max = msg.payload.temperature;\n   context.set('max', msg.payload.temperature);\n} else {\n   newMsg.bounds.max = max;\n}\n\nreturn newMsg;","outputs":1,"noerr":0,"x":875.5,"y":336,"wires":[["af52c259.fecbd8","a7d331dd.9d8078","81bda4f5.6f104"]]},{"id":"259fa218.53bdbe","type":"comment","z":"d988539b.52bdc8","name":"Useful links","info":"see:\n\n\n\n","x":400.5,"y":337,"wires":[]},{"id":"81bda4f5.6f104","type":"ui_template","z":"d988539b.52bdc8","group":"519167a8.570e5","name":"Max and Min","order":2,"width":"6","height":"2","format":"</p><div layout="\&quot;row\&quot;" layout-align="\&quot;start" center\"="">\n  <span flex="">Temp Min: </span>\n  <span flex="">Temp Max: </span>\n</div>
<p>\n</p><div layout="\&quot;row\&quot;" layout-align="\&quot;start" center\"="" ng-repeat="\&quot;bounds" in="" msg\"="">\n  <span flex="" style="\&quot;color:" green\"="">{{bounds.min}}</span>\n  <span flex="" style="\&quot;color:" red\"="">{{bounds.max}}</span>\n</div>
<p>\n","storeOutMessages":true,"fwdInMessages":true,"templateScope":"local","x":1109.4444122314453,"y":336.6666717529297,"wires":[[]]},{"id":"f5bb5785.45e55","type":"debug","z":"35963a2e.6aa056","name":"Message object","active":true,"tosidebar":true,"console":true,"tostatus":false,"complete":"true","targetType":"full","x":1129.75,"y":286.9166564941406,"wires":[]},{"id":"27187c33.85c07c","type":"ui_gauge","z":"35963a2e.6aa056","name":"Gauge","group":"c86b0ed1.65efc8","order":0,"width":"6","height":"2","gtype":"gage","title":"Temperature","label":"Celsius","format":"{{payload.temperature}}","min":0,"max":"60","colors":["#00b500","#e6e600","#ca3838"],"seg1":"25","seg2":"28","x":1086.833251953125,"y":432.8055419921875,"wires":[]},{"id":"cd94fffc.6f0da8","type":"inject","z":"35963a2e.6aa056","name":"","topic":"","payload":"","payloadType":"date","repeat":"","crontab":"","once":true,"onceDelay":0.1,"x":401.5,"y":394,"wires":[["ab8d1686.0264d"]]},{"id":"ab8d1686.0264d","type":"http request","z":"35963a2e.6aa056","name":"Wio Temperature","method":"GET","ret":"obj","paytoqs":false,"url":";<your token="" here="">&gt;","tls":"","proxy":"","authType":"basic","x":610.5,"y":394,"wires":[["27187c33.85c07c","ce9f0009.faab98"]]},{"id":"dbe1013a.863be8","type":"ui_chart","z":"35963a2e.6aa056","name":"Chart","group":"684a7caa.4db0f4","order":2,"width":"12","height":"7","label":"Temperature chart","chartType":"line","legend":"true","xformat":"HH:mm:ss","interpolate":"bezier","nodata":"","dot":false,"ymin":"","ymax":"","removeOlder":"15                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               ","removeOlderPoints":"50","removeOlderUnit":"60","cutout":0,"useOneColor":false,"colors":["#1f77b4","#aec7e8","#ff7f0e","#2ca02c","#98df8a","#d62728","#ff9896","#9467bd","#c5b0d5"],"useOldStyle":false,"outputs":1,"x":1087.8333740234375,"y":384.22222900390625,"wires":[[]]},{"id":"ce9f0009.faab98","type":"function","z":"35963a2e.6aa056","name":"Process temperature","func":"var newMsg={bounds:{}};\nvar min=context.get('min') || 100;\nvar max=context.get('max') || -100;\n\nnewMsg.topic = 'temperature';\nnewMsg.payload = msg.payload.temperature\n\nif (msg.payload.temperature &lt; min) {\n   newMsg.bounds.min = msg.payload.temperature;\n   context.set('min', msg.payload.temperature);\n} else {\n   newMsg.bounds.min = min;\n}\nif (msg.payload.temperature &gt; max) {\n   newMsg.bounds.max = msg.payload.temperature;\n   context.set('max', msg.payload.temperature);\n} else {\n   newMsg.bounds.max = max;\n}\n\nreturn newMsg;","outputs":1,"noerr":0,"x":875.5,"y":336,"wires":[["dbe1013a.863be8","f5bb5785.45e55","b809b11b.7f47c8"]]},{"id":"b809b11b.7f47c8","type":"ui_template","z":"35963a2e.6aa056","group":"c86b0ed1.65efc8","name":"Max and Min","order":2,"width":"6","height":"2","format":"</your></p><div layout="\&quot;row\&quot;" layout-align="\&quot;start" center\"="">\n  <span flex="">Temp Min: </span>\n  <span flex="">Temp Max: </span>\n</div>
<p>\n</p><div layout="\&quot;row\&quot;" layout-align="\&quot;start" center\"="" ng-repeat="\&quot;bounds" in="" msg\"="">\n  <span flex="" style="\&quot;color:" green\"="">{{bounds.min}}</span>\n  <span flex="" style="\&quot;color:" red\"="">{{bounds.max}}</span>\n</div>

Logging footfall counts with a Raspberry Pi and camera – technical considerations

Here at Cranfield University we are putting in place plans related to the new ‘Living Laboratory’ project, part of our ‘Urban Observatory’. This project sits within the wider UKCRIC initiative, across a number of universities. Of the many experiments in development, we are exploring machine vision as a means to provide  footfall counts of pedestrian traffic in parts of the campus. This blog provides summarises some of the technical considerations relating to this work.

Siting the Raspberry Pi and camera

Earlier blogs here on GeoThread have described the use of a Raspberry Pi Zero running the Kerboros software, which uses OpenCV to capture movement in different ways from an attached, or network connected, camera. One of the great additions to Kerboros is a feature permitting ‘counting’ of objects passing between two identified ‘fences’ within the image view. The results of an event being triggered in this way can be output to a range of IO streams. Thus IO can send a result to a stored image file to disk (Disk), send a snippet of video to disk (Video), trigger the GPIO pins on the Raspberry Pi (GPIO), send a packet to a TCP Socket (TCPSocket), send a HTTP POST REST communication (Webhook), run a local (Script), trigger a MQ Telemetry Transport communication (MQTT) or send a push notification message (Pushbullet) – a great selection of choices, to which is also added the ability to write to the Amazon S3 cloud as well as local disk. Outputs can be made in either single streams, or multiple streams – thus one can save an image to disk ‘and‘ run a script for example.

In an earlier blog, we had logged movements in this way to a bash script that executed a series of one-line Python commands to store the triggered output data to disk, appending to a CSV file. However, this approach only offers a limited solution, especially if there are multiple cameras involved. A better strategy is to write data out to a REST endpoint on a web server, using the Webhook capability. In this way the posted JSON data can be collated into a central database (from multiple camera sources).

The option for Webhook was selected, with the URL form of:

The reason for this port number, and REST endpoint are made clear in the NodeJS receiver code below.

We set up a separate server running Linux Ubuntu to receive these data. For a database, we selected Postgres as a popular and powerful Open Source SQL database. As a server, able to receive the HTTP POST communication, we selected NodeJS. Once installed, we also used the Node Package Manager (NPM) to install the Express lightweight web server, as well as the Node Postgres framework, node-postgres.

# Update the new server
sudo apt-get update
sudo apt-get upgrade

# Install curl, to enable loading to software repositories
sudo apt install curl

#Installation of Node:
curl -sL | sudo -E bash -<sudo apt-get install -y nodejs
node -v
npm -version

#Installation of Postgres:
#To install PostgreSQL, as well as the necessary server software, we ran the following command:
sudo apt-get install postgresql postgresql-contrib
sudo apt install postgresql-10-postgis-2.4
sudo apt install postgresql-10-postgis-scripts

We then configured Postgres to accept communication from the Raspberry Pi devices

sudo nano /etc/postgresql/10/main/pg_hba.conf<br>
#      host     all     all     <<IPADDRESS>>/24     md5

sudo nano /etc/postgresql/10/main/postgresql.conf
# search for 'listen' in file
#      listen_addresses = '<<SETTING>>'

We then used PSQL (the Postgres command line utility) to update the system password, and then to add a new user to Postgres to own the resultant database.

# Change password for the postgres role/account:
sudo -u postgres psql
\password postgres
#enter a new secure password for the postgres user role account

#create a new user role/account for handling data, and allow it to create new databases

Show all the users to check this worked

# and then quit psql

Finally, a review of the commands to start, stop and restart Postgres.

sudo service postgresql start
sudo service postgresql stop
sudo service postgresql restart

Next, the new Postgres user was used to create a new table with the following fields

id integer NOT NULL DEFAULT [we used the data type SERIAL to outnumber records, and set this field as the Primary Key]
“regionCoordinates” character varying(30)
“numberOfChanges” integer
incoming integer
outgoing integer
“timestamp” character varying(30)
microseconds character varying(30)
token integer
“instanceName” character varying(30)

Next the nodeJS project environment was established:

mkdir node-api-postgres
cd node-api-postgres
npm init -y

# the editor 'nano' is used to inspect and edit the resultant file 'package.json', and to edit the description as required:
nano package.json  (to edit description)

Lastly, the node package manager npm was used to install the two required node libraries Express and pg:

npm i express pg

Next, drawing from the excellent LogRocket blog, an application was written to receive the data, comprising two files, ‘index.js’, and ‘queries.js’, as follows:


// index.js
const express = require('express')
const bodyParser = require('body-parser')
const app = express()
const db = require('./queries')
const port = 3000

     extended: true,

app.get('/', (request, response) =&gt; {
   response.send('Footfall counter - Service running')
})'/counter', db.createFootfall)

app.listen(port, () =&gt; {
   console.log('App running on port ${port}.')


// queries.js

const Pool = require('pg').Pool
const pool = new Pool({
   user: '<<username>>',
   host: '<<hostname>>',
   database: '<<database>>',
   password: '<<password>>',
   port: 5432,

const createFootfall = (request, response) => {
  const {regionCoordinates, numberOfChanges, incoming, outgoing, timestamp, microseconds, token, instanceName} = request.body
  pool.query('INSERT INTO <<tablename>> ("regionCoordinates", "numberOfChanges", "incoming", "outgoing", "timestamp", "microseconds", "token", "instanceName") VALUES ($1, $2, $3, $4, $5, $6, $7, $8) RETURNING *', [regionCoordinates, numberOfChanges, incoming, outgoing, timestamp, microseconds, token, instanceName], (error, result) => {
    if (error) {
    console.log(`Footfall added with the ID: ${result.rows[0].id}`)
    response.status(200).send(`Footfall added with ID: ${result.rows[0].id}\n`)

module.exports = {

One thing to note in the code above is the use of ‘RETURNING *’ within the SQL statement – this allows the ‘result’ object to access the full row of data, including the automatically generated Id reference. Otherwise this would not be accessible as it it generated on the server and not passed in by the INSERT statement.

Now the Node application is started up, listening on port 3000 on the REST endpoint /counter’ (both defined in index.js).

node index.js

The result is data streaming into the Postgres database.

Once this is in place, we can run a test to check the system can receive data. For this we can use the curl command again, like this:

curl --data "regionCoordinates=[413,323,617,406]&numberOfChanges=1496&incoming=1&outgoing=0&timestamp=1539760397&microseconds=6-928567&token=722&instanceName=LivingLabTest" http://<<IPADDRESS>>:3000/counter
Footfall added with ID: 1

The result is a new record is added to the database. To check the data row was added correctly, a quick way is to use the graphical pgAdmin tool that is used to interact with Postgres databases. Set up a new connection to the server database, and inspect the table to see the record, thus:

pgAdmin utility

The next step is to connect to the Raspberry Pi Zero running Kerboros, and configure its output to the IO output ‘Webhook’ taking, as noted above, a URL form of:

Note here the port number of 3000 referenced, and also a request from Manchester. Once the Kerboros software is updated, data should be seen to be arriving at the database. pgAdmin can be used again to inspect the result.

A final step then is to set up a daemon service that can stop and start the node programme. This means that if the server is rebooted, the We followed the Hackernoon blog and took the following steps:

First, a file was created “/etc/systemd/system/node-api-postgres.service”, with the following content:

Description=Node.js node-api-postgres Footfall service



Next, make the file executable:

chmod +x index.js 

Next, the service can be prepared:

sudo systemctl enable node-api-postgres.service

If the service file needs any editing after the service is prepared, the daemons need to all be reloaded, thus:

systemctl daemon-reload

Finally, the service may now be started, stopped or restarted:

sudo systemctl start node-api-postgres.service
sudo systemctl stop node-api-postgres.service
sudo systemctl restart node-api-postgres.service

A command can also be added to restart the Postgres database on a reboot:

update-rc.d postgresql enable

Conclusion and Epilogue
This blog has shown how to capture footfall counting data sourced from a Rasberry Pi with a camera running Kerboros, to a separate server running the database Postgres, using NodeJS and related packages. The result is a robust logging environment capable of receiving data from one or more cameras logged to database.
In order to operate the system, the general instructions are as follows:
First, log onto the Unix box

ssh <<USER>>@<<IPADDRESS>>

Next, update and upgrade the system (do this regularly)

sudo apt-get update
sudo apt-get upgrade

Ensure Postgres is running

sudo service postgresql start
sudo service postgresql stop
sudo service postgresql restart

Next, ensure the Node app is running

sudo systemctl start node-api-postgres.service

You should now just wait for data to arrive – using pgAdmin to inspect and interrogate the database table.

Logging footfall counts with a Raspberry Pi and camera – ethical considerations

Here at Cranfield University we are putting in place plans related to the new ‘Living Laboratory’ project, part of our ‘Urban Observatory’. This project sits within the wider UKCRIC initiative, across a number of universities. Of the many experiments in development, we are exploring machine vision as a means to provide  footfall counts of pedestrian traffic in parts of the campus. This blog provides summarises some of the ethical considerations relating to this work.

An important early part of this project involves preparing, submitting and securing ethical approval for the planned work. In the first instance we are running an experiment in a particular campus office area. Before commencing any technical work, a full approval case has to be prepared and submitted for assessment.

In the case of this experiment, we are intending to log only a count of pedestrian movements, with no personally identifiable imagery captured. Informed written consent is obtained from all residents of the office area, and signage put up for visitors, see below. The case outlining these intentions is drawn up and submitted.

These matters are raised here as a precursor to the technical description to follow, as IoT projects require careful consideration of privacy matters.

Note, image above is shown with the specific details removed

Machine Vision with a Raspberry Pi

In this blog we will describe the steps needed to do some machine vision using the Raspberry Pi Zeros we described in the earlier blog. Here at Cranfield University we are building these amazing devices into our research. In this case we are interested in using the Pi as a device for counting pedestrians passing a site – trying to understand how different design choices influence people’s choice of walking routes.

   Installation of Kerberos
   Configuration of Kerberos
   Configuration of the Pi
   Output and Data Capture from Kerberos


In the earlier blog we showed how to set up the Raspberry Pi Zero W, connecting up the new v2 camera in a case and connecting power. Once we had installed Rasbian on a new microSD card all was ready to go.

A bit of research was needed to understand the various options for machine vision on a Pi. There are three levels we might want. First a simple motion detection with the camera would give a presence or absence of activity, but not much more. This could be useful when pointing the camera directly at a location. Second, we can use more sophisticated approaches to consider detecting movement passing across the camera’s view, for example left to right or vice versa. This could be useful when pointing the camera transverse to a route along which pedestrians are travelling. Thirdly, and with the ultimate sophistication, we could try and classify the image to detect what the ‘objects’ passing across the view are. Classifier models might for example detect adults, young persons, and other items such as bicycles and push buggies etc. Needless to say, we wanted to start off easy and then work up the list!

Looking at the various software tools available, it is clear that many solutions draw on OpenCV (Open Source Computer Vision Library) ( OpenCV is an open source computer vision and machine learning software library, built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception. There are many other potential libraries for machine vision – for example, SOD (, and other libraries such as Dlib ( OpenCV can be daunting, and there are wrappers such as SimpleCV ( to try and simplify the process.


We then looked at options for toolkits that use these basic building blocks. A useful reference is Jason Antman’s blog here Although not Jason’s final choice, the tool that stuck out to us was Kerberos (, developed by Cedric Verstraeten and grown out of his earlier OpenCV project (


Kerberos has a number of key resources:
Main home website –
Documentation –
Git –
Helpdesk –
Corporate –
Gitter –

Although the full source for Kerberos is available, and also a docker implementation, what we really liked was the SD image for the Raspberry Pi Zero – so really made for the job.

Installation of Kerberos:

We downloaded the cross-platform installer from the Kerberos website. This is based on the Etcher tool, used to install Rasbian so familiar to any Pi user. In our case we selected the Mac installer, downloading an installer dmg file (c.80Mb). Then, ensuring the Micro SD card destined for the Pi was in a flash writer dongle attached to the Mac, we were able to easily install the image. The Etcher app asks a couple of questions on the way about the WiFi network SSID and WiFi and system passcodes, as well as a name for the device, and writes these details onto the SD card with the rest of the image. As a result, on inserting the SD card and booting the Pi with the Kerberos image, the device started up and connected correctly and without issue on the WiFi network. A check on the router on our closed network showed the device had correctly registered itself at IP address

Management of the Pi and camera is achieved via app running on a web server on the Pi. So to access our device, we entered browsed the URL

Configuration of Kerberos:

The dashboard app provides complete control over the operation of the Pi and camera.The image here shows the ‘heatmap’ camera view, and statistical graphs and charts of timings of activations.
. To configure the many settings we headed over to for the documentation. The concept is that the image processing is undertaken on the ‘Machinery’ configuration, and that the ‘Web’ then controls access to the results.

Selecting ‘Configuration’ we could start adjusting the settings for the Machinery as we required. There are default settings for all the options.
However, the settings you will use depend on the application for the device. We followed the settings for ‘People Counter‘ recommended both in the docs, and a subsequent blog. It seems that there the settings are very sensitive, so one has to adjust until the desired results are obtained.

Being on a Raspberry Pi, one can also ssh connect directly to the device on a terminal connection (eg from terminal on the Mac, or via Putty from a PC). Connect to the device with the command:

ssh root@
cd /data/machinery/config

This takes you to the location of the configuration files, as written out by the web app. Below are the settings we used to get the People Counter working (the values here correspond to the settings in the web app).

less config.xml
<!--?xml version="1.0"?-->
        <name type="text">Stationery</name>
        <logging type="bool">false</logging>
        <timezone type="timezone">Europe-London</timezone>
        <capture file="capture.xml">RaspiCamera</capture>
        <stream file="stream.xml">Mjpg</stream>
        <condition file="condition.xml" type="multiple">Enabled</condition>
        <algorithm file="algorithm.xml">DifferentialCollins</algorithm>
        <expositor file="expositor.xml">Rectangle</expositor>
        <heuristic file="heuristic.xml">Counter</heuristic>
        <io file="io.xml" type="multiple">Webhook</io>
        <cloud file="cloud.xml">S3</cloud>
less capture.xml
<!--?xml version="1.0"?-->
        <url type="text">xxxxxxxxx</url>
        <framewidth type="number">640</framewidth>
        <frameheight type="number">480</frameheight>
        <delay type="number">500</delay>
        <angle type="number">0</angle>
        <framewidth type="number">640</framewidth>
        <frameheight type="number">480</frameheight>
        <devicenumber type="number">0</devicenumber>
        <fourcc type="text">MJPG</fourcc>
        <delay type="number">500</delay>
        <angle type="number">0</angle>
        <framewidth type="number">640</framewidth>
        <frameheight type="number">480</frameheight>
        <delay type="number">500</delay>
        <angle type="number">0</angle>
        <framerate type="number">20</framerate>
        <sharpness type="number">0</sharpness>
        <saturation type="number">0</saturation>
        <contrast type="number">0</contrast>
        <brightness type="number">50</brightness>
        <framewidth type="number">640</framewidth>
        <frameheight type="number">480</frameheight>
        <path type="text">0</path>
        <delay type="number">500</delay>
        <angle type="number">0</angle>
less stream.xml
<!--?xml version="1.0"?-->
    	<enabled type="bool">true</enabled>
    	<streamport type="number">8889</streamport>
    	<quality type="number">75</quality>
    	<fps type="number">15</fps>
    	<username type="text"></username>
    	<password type="text"></password>
less condition.xml
<!--?xml version="1.0"?-->
        <times type="timeselection">0:01,23:59-0:01,23:59-0:01,23:59-0:01,23:59
        <delay type="number">10000</delay>
    	<active type="bool">true</active>
        <delay type="number">5000</delay>
less algorithm.xml
<!--?xml version="1.0"?-->
		<erode type="number">5</erode>
    	        <threshold type="number">15</threshold>
		<shadows type="text">false</shadows>
		<history type="number">15</history>
		<nmixtures type="number">5</nmixtures>
		<ratio type="number">1</ratio>
		<erode type="number">5</erode>
		<dilate type="number">7</dilate>
    	<threshold type="number">10</threshold>
less expositor.xml
<!--?xml version="1.0"?-->
		    <x1 type="number">0</x1>
		    <y1 type="number">0</y1>
		    <x2 type="number">800</x2>
		    <y2 type="number">600</y2>
	    <region type="hullselection">779,588|781,28|588,48|377,31|193,31|32
less heuristic.xml
<!--?xml version="1.0"?-->
	    <minimumchanges type="number">20</minimumchanges>
	    <minimumduration type="number">2</minimumduration>
        <nomotiondelaytime type="number">1000</nomotiondelaytime>
	    <appearance type="number">3</appearance>
	    <maxdistance type="number">140</maxdistance>
	    <minarea type="number">200</minarea>
	    <onlytruewhencounted type="bool">false</onlytruewhencounted>
	    <minimumchanges type="number">5</minimumchanges>
        <nomotiondelaytime type="number">100</nomotiondelaytime>
		<markers type="twolines">34,29|36,461|617,22|614,461</markers>

Note the settings above for the twolines markers on the video image – used for counting pedestrians passing from left to right, and from right to left, (coordinate position 0,0 is the top left corner)

less io.xml
<!--?xml version="1.0"?-->
        <fileformat type="text">timestamp_microseconds_instanceName_regionCoord
        <directory type="text">/etc/opt/kerberosio/capture/</directory>
        <markwithtimestamp type="bool">false</markwithtimestamp>
        <timestampcolor type="text">white</timestampcolor>
        <privacy type="bool">false</privacy>
        <throttler type="number">0</throttler>
        <fps type="number">30</fps>
        <recordafter type="number">5</recordafter>
        <maxduration type="number">30</maxduration>
        <extension type="number">mp4</extension>
        <codec type="number">h264</codec>
        <fileformat type="text">timestamp_microseconds_instanceName_regionCoord
        <directory type="text">/etc/opt/kerberosio/capture/</directory>
        <hardwaredirectory type="text">/etc/opt/kerberosio/h264/
        <enablehardwareencoding type="bool">true</enablehardwareencoding>
        <markwithtimestamp type="bool">false</markwithtimestamp>
        <timestampcolor type="text">white</timestampcolor>
        <privacy type="bool">false</privacy>
        <throttler type="number">0</throttler>
        <pin type="number">17</pin>
        <periods type="number">1</periods>
        <periodtime type="number">100000</periodtime>
        <throttler type="number">0</throttler>
        <server type="number">IP_ADDRESS:3000/counter</server>
        <port type="number"></port>
        <message type="text">motion-detected</message>
        <throttler type="number">0</throttler>
        <url type="text">IP_ADDRESS:3000/counter</url>
        <throttler type="number">500</throttler>
        <path type="text">/etc/opt/kerberosio/scripts/</path>
        <throttler type="number">0</throttler>
        <secure type="bool">false</secure>
        <verifycn type="bool">false</verifycn>
        <server type="number">IP_ADDRESS</server>
        <port type="number">1883</port>
        <clientid type="text"></clientid>
        <topic type="text">kios/mqtt</topic>
        <username type="text"></username>
        <password type="text"></password>
        <throttler type="number">0</throttler>
        <url type="text"></url>
        <token type="text">xxxxxx</token>
        <throttler type="number">10</throttler> 

Configuration of the Pi:

Another configuration required was to tun off the bright green LED on the Raspberry Pi as it draws attention when the unit is operating. To turn OFF the LEDs for Zero, we followed the instructions at
raspberry-pi. Note that unlike other Raspberry Pi models, the Raspberry Pi Zero only has one LED, led0 (labeled ‘ACT’ on the board). The LED defaults to on (brightness 0), and turns off (brightness 1) to indicate disk activity.

To turn off the LEDs interactively, the following commands can be run each time the Pi boots.

# Set the Pi Zero ACT LED trigger to 'none'.
echo none | sudo tee /sys/class/leds/led0/trigger
# Turn off the Pi Zero ACT LED.
echo 1 | sudo tee /sys/class/leds/led0/brightness

To make these settings permanent, add the following lines to the Pi’s ‘/boot/config.txt’ file and reboot:

# Disable the ACT LED on the Pi Zero.

Note the ‘/’filesystem is made read-only by default in the Kerberos build. To temporarily fix this to force read write for the root ‘/’ filesystem, type:

mount -o remount,rw /

Now the config.txt file can be edited normally, e.g. in the editor nano, and then the Pi can be rebooted.

cd /boot
nano config.txt

Output and Data Capture from Kerberos:

To obtain data from the tool, we are using the ‘script’ setting in io.xml, which runs the script ‘/data/’ (a bash script). This script just writes the data receives (a JSON structure) out to disk.


# -------------------------------------------
# This is an example script which illustrates
# how to use the Script IO device.

# --------------------------------------
# The first parameter is the JSON object
# e.g. {"regionCoordinates":[308,250,346,329],"numberOfChanges":194,"timestamp":"1486049622","microseconds":"6-161868","token":344,"pathToImage":"1486049622_6-161868_frontdoor_308-250-346-329_194_344.jpg","instanceName":"frontdoor"}


# -------------------------------------------
# You can use python to parse the JSON object
# and get the required fields

echo $JSON &amp;gt;&amp;gt; /data/capture_data.json

coordinates=$(echo $JSON | python -c "import sys, json; print json.load(sys.stdin)['regionCoordinates']")
changes=$(echo $JSON | python -c "import sys, json; print json.load(sys.stdin)['numberOfChanges']")
incoming=$(echo $JSON | python -c "import sys, json; print json.load(sys.stdin)['incoming']")
outgoing=$(echo $JSON | python -c "import sys, json; print json.load(sys.stdin)['outgoing']")
time=$(echo $JSON | python -c "import sys, json; print json.load(sys.stdin)['timestamp']")
microseconds=$(echo $JSON | python -c "import sys, json; print json.load(sys.stdin)['microseconds']")
token=$(echo $JSON | python -c "import sys, json; print json.load(sys.stdin)['token']")
instancename=$(echo $JSON | python -c "import sys, json; print json.load(sys.stdin)['instanceName']")

printf "%(%m/%d/%Y %T)T\t%d\t%d\t%d\t%d\n" "$time" "$time" "$changes" "$incoming" "$outgoing" &amp;gt;&amp;gt; /data/results.txt

Note the use of the parameters to convert the Julian timestamp to a readable date/time.

When an event triggers the system (someone walking past the camera view) two actions follow, an image is saved to disk, and the script is run, with a parameter of the JSON structure. The script then processes the JSON. The script here both writes out the whole JSON structure to a the file ‘capture_data.json’ (this is included as a debug and could be omitted), and also extracts out the data elements we actually wanted and writes these to a CSV file called ‘results.txt’.

A sample of ‘capture_data.json’ look like this:


A sample of ‘results.txt’ looks like this:

10/17/2018 08:17:08	1539760628	917	0	1
10/17/2018 08:17:18	1539760638	690	0	1
10/17/2018 08:18:56	1539760736	2937	0	1
10/17/2018 08:19:38	1539760778	3625	1	0
10/17/2018 08:22:05	1539760925	1066	1	0
10/17/2018 08:24:06	1539761046	2743	0	1
10/17/2018 08:24:45	1539761085	1043	1	0
10/17/2018 08:26:11	1539761171	322	0	1


This blog has shown how the Kerberos toolkit has been used with an inexpensive Raspberry Pi for detecting motion and also directional movement across the camera view. The data captures a JSON data structure for each event triggered, and a script extracts from this the data required, which is saved off to disk for later use.

There are still issues to grapple with – for example reduce false positives, and perhaps more importantly not missing events as they occur. The settings of the configuration machinery are very sensitive. The best approach is to successively vary these settings (particularly the expositor and heuristic settings) until the right result is obtained. Kerberos has a verbose setting for event logging, and inspecting the log with this switched on reveals that the Counter conditions are very sensitive – so many more people may be walking past the camera than are being directly logged as such (e.g. motion activations may be greater than count events).

The commands below show how to access the log – it is also shown in the ‘System’ tab of the web dashboard. The command ‘tail -f’ is useful as it shows the log update in real time – helpful if the video live feed screen is being displayed alongside on-screen. Then you can see what is and isn’t being logged very easily.

cd /data/machinery/logs
tail -f log.stash

Ultimately, the Raspberry Pi may not have enough power to operate full classifier models, such as that developed by Joseph Redmon with the Darkweb YOLO tool he developed (‘You Only Look Once’) ( However, Kerberos itself has a cloud model that provides post-processing of images in the cloud on AWS servers, with classifier models available – perhaps something to try in a later blog.

Setting up a Raspberry Pi Zero W

Here on GeoThread, we have taken delivery of one of the new Raspberry Pi Zeros for some projects – so here is a blog on how we set it all up for some of the work we get up to at Cranfield University. We’ve had quite a few projects with these devices over the years – reported here on GeoThread, starting back in 2016 with the Raspberry Pi 3, with various tutorials presented. The Raspberry Pi Zero is the latest in the line of these excellent, inexpensive microcomputers, see Following on from the Pi A and B series devices, the Zero is a great entry level machine for learning coding and software development. We have a Raspberry Pi Zero W – the ‘W’ meaning it also has wireless and Bluetooth connectivity.

To get us going, we bought a kit that contained not only the Pi, but also an acrylic case and a power supply. There are many such kits available – we selected the Vilros offering. We also bought the new Raspberry Pi Camera v2 to fit in the case.

Raspberry Pi Zero, Vilros Starter Kit

Note that we have also bought (shown at the bottom centre), an HDMI Mini Type C (male) to HDMI Normal Type A (female) Monitor Cable Display Adapter – the Raspberry Pi Zero only has a HDMI Mini socket. In addition, you may also need to buy a single (or dual) Micro USB Male To USB 2.0 Female OTG (USB On the Go) cable for connecting a USB keyboard (or a USB keyboard and mouse). The HDMI and USB cables/adapters are to be used to get the unit up and running in the first instance, using a keyboard, mouse and monitor. Once the configuration is complete and the WiFi is connected, we will run the Raspberry Pi remotely and so disconnect the keyboard and screen.

We also bought one of the new ‘version 2’ cameras for the Raspberry Pi. This is an 8MPixel camera made by Sony, and a huge improvement over the original camera. It can fit into the case. The case has three alternative lids – shown here is the version with the camera mounting and aperture built in.

Raspberry Pi Zero and unboxing the camera v2

One thing to note immediately is that the Raspberry Pi Zero has a different camera cable connector size to the earlier models of the Pi. This means that you will need to obtain a new ribbon cable connector to replace the one provided. You can easily buy these separately, but the Vilros kit (like other kits) thoughtfully provide this.

Raspberry Pi Zero Camera v2 and case

Raspberry Pi Zero camera socket

The camera fixing has a small plastic bar that can be gently prised outwards to allow the old connector ribbon cable to be slipped out. The new cable can then be slotted in and the plastic bar pushed back into place to secure it. Be sure to insert the cable the right way up to allow contact.This is a delicate operation, but the cable is reasonably robust.

Raspberry Pi Zero Camera v2 ribbon cables

In the image above, the old connector has been removed, and the new one inserted. Note the new connector is very short, allowing it to be fitted inside the case. We are nearly ready to assemble the parts together in the case.

Raspberry Pi Zero Case Camera v2 – ready for fitting

The camera, with its new connector, can now be fitted to the Raspberry Pi Zero. The Zero has another plastic clip for slotting in the ribbon cable. As before, prise away the locking bar and gently insert the cable. When tightly fitted (the right way up to allow contact again), the bar can be pushed back in to grip the cable in place.

Raspberry Pi Zero Case Camera v2 – fitting the camera into the case

Once fitted, the case can be put together. Note that the Vilros kit comes with a small heatsink that can be fitted to the Pi to cool the 1GHz ARM CPU chip. However, with the camera fitted in the case, there is no room for the heatsink also. Time will tell if that is an issue!

Raspberry Pi Zero Case and Camera v2, fully Assembled

The Vilros kit comes with the power supply, fitted with the customary MicroUSB plug.

Raspberry Pi Zero Power Supply

Note that the power socket is the socket to the right, as shown.

Raspberry Pi Zero sockets

Before the case can be clicked into place, there is one last thing needed – the operating system. This is installed on an SD card.
We followed the very clear instructions on the Raspberry Pi website, here We downloaded the latest Raspbian img file and flashed it onto a new Micro-SD card. To do this on a MacBook Pro, we used the Etcher app (v1.4.4).

SD card Flashing with Etcher

Once the Micro SD card was flashed (e.g. the img file was copied onto it with Etcher), we could insert it carefully into the SD socket on the Raspberry Pi Zero. At this point the case was all clipped together.
We are now ready for the next stage of the project!

IOT Project – Using an ESP32 device to monitor a web service

There is a lot of interest in the Internet of Things here at Cranfield University. Especially now there is a new generation of super-cheap ESP8266 and ESP32 devices which can be deployed as IoT controllers. Many of these devices are now also available with in-built OLED screens – very helpful for showing messages and diagnostics.

We will use one of these devices to develop our project.

The project
The device
Coding the EPS32
Configuring the development environment
Connecting to the device
USB drivers
OLED Screen drivers
Configuring the Arduino IDE
Uploading the Source Code
The Source Code
– – Authorisation
In operation
Next steps

The project

Nowadays, web services are used for all sorts of applications – for providing access to data and functionality online. A useful application then for the EPS32 is for it to act as a monitor for a web service, repeatedly polling the service to see if it is operating correctly. If the web service goes down, we need to know – the EPS32 can keep an eye on the service and report any problems.

This project describes how an EPS32 device can be configured and programmed to monitor a web service. The web service we will monitor is developed (in node.js) with an API that includes a ‘current status’ call – if all is well calling this returns a success message which we can capture.

The device

For this project, we are using the TTGO-WiFi-Bluetooth-Battery-ESP32-Module-ESP32-0-96-inch-OLED-development-tool from Aliexpress, although these devices are widely available from many retailers. This particular model also has a battery holder for a long-use LIPO battery on the rear of the board.

Coding the EPS32

There are a few options for coding the ESP devices. Most easily, it is possible to use the Arduino development environment (with a few tweaks). Another possibility is using the Atom implementation at platformio: We used the Arduino tool.

Configuring the development environment

The Arduino development environment by default does not have the libraries and configurations to allow it to programme the EPS32. There are a few steps needed to enable this.

Connecting to the device

The EPS32 board has a micro-USB port, permitting connection to the programming computer. We used an Apple Mac laptop for programming – so a micro-USB to USB-C cable/convertor was required.

USB drivers

The EPS32 device needs a software driver installed on the programming computer. We used the Silicon Labs drivers available online here. There are other alternatives (some commercial) for drivers – notably the Mac-usb-serial drivers online.

OLED Screen drivers

The EPS32 device also has an in-built OLED screen. Although very small, at 128*64 pixels, this mono display is quite large enough to show text messages with different fonts, simple bitmap graphics, progress bars and drawing elements (lines, rectangles etc.) – amazing! However, a library is needed to allow access to this device. We used the ThingPulse OLED library.

The library can be downloaded as a zip file from GitHub to the library folder in the Arduino installation folder. On the Mac for example this is:


This library comes with lots of example programmes showing how to programme the screen, how to encode bitmaps, add progress bars, create fonts etc. From the code below, it can be seen that the Sketch ‘SSD1306SimpleDemo.ino‘ offers a great starting point for learning, referencing for example the ways described at Squix for encoding images and fonts. I selected the Roboto Medium font and encoded the WiFi graphic (using this online tool).
When completed, the two header files for fonts and images respectively were:
fonts header file – fonts.h
images header file – images.h

Configuring the Arduino IDE

Configuring the Arduino IDEHaving installed these drivers and libraries, the Arduino IDE then needs to be configured.

To do this, the board was set as a device of type ‘ESP32 Arduino’ -> ‘ESP32 Dev Module’, the baud rate set to 115200. Having installed the USB driver above, the port could be set to ‘/dev/cu.SLAB_USBtoUART’.

Uploading the Source Code

The Arduino IDE allows one to compile and upload code to the device. Critically, one has to hold down the ‘Boot’ button on the device as the programme is uploaded (for a few seconds) to allow the device code to be uploaded. If the boot button is NOT held down, there will be errors reported and the code will not be uploaded! (this took ages to work out!!)

The Source Code

In coding the device in the Arduino development environment, one can refer usefully to the Arduino code reference. The final working code is shown below. Note the calls to the Serial monitor to allow debugging information to be shown while the device is connected to the computer.

// TTGO WiFi &amp; Bluetooth Battery ESP32 Module - webservices checker
// Import required libraries
#include "Wire.h"
#include "OLEDDisplayFonts.h"
#include "OLEDDisplay.h"
#include "OLEDDisplayUi.h"
#include "SSD1306Wire.h"
#include "SSD1306.h"
#include "images.h"
#include "fonts.h"
#include "WiFi.h"
#include "WiFiUdp.h"
#include "WiFiClient.h"
// The built-in OLED is a 128*64 mono pixel display
// i2c address = 0x3c
// SDA = 5
// SCL = 4
SSD1306 display(0x3c, 5, 4);

// WiFi parameters
const char* ssid = "MYSSID";
const char* password = "MYWIFIKEY";

// Web service to check
const int httpPort = 80;
const char* host = "MYWEBSERVICE_HOSTNAME";

void setup() {
	// Initialize the display

	// Start Serial
	// Connect to WiFi
	display.drawString(0, 0, "Going online");
	display.drawXbm(34, 14, WiFi_Logo_width, WiFi_Logo_height, 			 WiFi_Logo_bits);
	WiFi.begin(ssid, password);
	while (WiFi.status() != WL_CONNECTED) {
	Serial.println("WiFi now connected at address");
	// Print the IP address

void loop() {
	Serial.print("\r\nConnecting to ");
	display.drawString(0, 0, "Check web service");
	Serial.println("Check web service");

	// Setup URI for GET request
	// if service is up ok, return string will contain: 'Service running'

	WiFiClient client;
	if (!client.connect(host, httpPort)) {
		Serial.println("Connection failed");
		display.drawString(0, 0, "Connection failed");

	client.print("GET " + url + " HTTP/1.1\r\n");
	client.print("Host: " + (String)host + "\r\n");
	// If authorisation is needed it can go here
	//client.print("Authorization: Basic AUTHORISATION_HASH_CODE\r\n");
	client.print("User-Agent: Arduino/1.0\r\n");
	client.print("Cache-Control: no-cache\r\n\r\n");

	Serial.print("GET " + url + " HTTP/1.1\r\n");
	Serial.print("Host: " + (String)host + "\r\n");
	// If authorisation is needed it can go here
	//Serial.print("Authorization: Basic AUTHORISATION_HASH_CODE\r\n");
	Serial.print("User-Agent: Arduino/1.0\r\n");
	Serial.print("Cache-Control: no-cache\r\n\r\n");

// Here's an alternative form if the service API uses HTTP POST
client.print("POST " + url + " HTTP/1.1\r\n");
client.print("Host: " + (String)host + "\r\n");
// If authorisation is needed it can go here
//client.print("Authorization: Basic AUTHORISATION_HASH_CODE\r\n");
client.print("User-Agent: Arduino/1.0\r\n");
client.print("Cache-Control: no-cache\r\n\r\n");

	// Read all the lines of the reply from server
	bool running = false;
	while (client.available()) {
		String line = client.readStringUntil('\r\n');
	 	if (line == "Service running") {
	 		running = true;
	if (running == true) {
		display.drawString(0, 25, "Service up OK");
	} else {
	 	display.drawString(0, 25, "Service DOWN");
		// Text/email administrator

// Here's some alternative methods to read web output
while (client.available()) {
	 char c =;
int c = '\0';
unsigned long startTime = millis();
unsigned long httpResponseTimeOut = 10000; // 10 sec
while (client.connected() &amp;&amp; ((millis() - startTime) &lt; 	 	 	 httpResponseTimeOut)) {
	 if (client.available()) {
	 	 c =;
	} else {

	Serial.println("Closing connection");
	display.drawString(0, 0, "Closing connection");
	// progress bar
	for (int i=1; i<=28; i++) {
	 	float progress = (float) i / 28 * 100;
	 	delay(500); // = all adds up to delay 14000 (14 sec)
		// draw percentage as String
	 	display.drawProgressBar(0, 32, 120, 10, (uint8_t) progress);
	 	display.drawString(64, 15, "Sleeping " + String((int) progress) + "%");
	 	Serial.print((int) progress);Serial.print(",");
	delay (1000);


Note that the connection to the service can use either HTTP GET or POST according to need (POST is considered a better approach). A further embellishment for security is if the web service uses authorisation (username and password to connect). If it does, then a hash of the combination of username and password can be passed in the header as shown in the code. To do this we use the excellent Postman tool. Postman allows one to manually create a connection conversation with an API server, including say a basic authorisation, and then view the full code of this – which can be copied into the Arduino code as shown above.

Note that it is critical to have a second carriage return at the end of the HTTP conversation (shown as ‘\r\n‘ in the code – so the last item has ‘\r\n\r\n’ for the blank line). Without this blank line it will not work!

In operation

Here is a short video of the device in operation. Excuse the use of image stabilisation – original video was filmed handheld.

The code is designed to open a connection, check on the status of the web service, then sleep for a period before repeating in an endless loop.

Next steps

This code currently only flashes up on the tiny screen when the service is found to be up or down. To be really useful, the tool should be able to alert one or more administrators – perhaps by push messaging to their mobile phones, or email.

The next stage can add this capability using approaches using Prowl, and Avviso. Perhaps the subject of a future blog posting.