Make Your Own IoT Twitter Bot

Internet of Things (IoT) is one of the big buzzwords making the rounds these days.

The basic concept is to have lightweight computing platforms integrated with everyday devices turning them into ‘smart’ devices. For example take your good old electricity meter embed a computing platform on it and connect it to the Internet – you get a Smart Meter!

Basic ingredients of an IoT ‘device’ include:

  • A data source, usually a sensor (e.g. temperature sensor, electricity meter, camera)
  • A lightweight computing platform with:
    • low power requirements
    • network connectivity (wireless and/or wired)
    • OS to run apps
  • Power connection
  • Data connection (mobile data, ethernet or WiFi)

In this post I wanted to build a basic IoT sensor using an off-the-shelf computing platform to show how easy it is!

This is also to encourage people to do their own IoT projects!

Raspberry Pi and Tessel2 platforms are two obvious choices for the computing platform.

I decided to use Tessel2 which is lot less powerful than Pi (sort of like comparing a Ford Focus with a Ferrari F40).

Tessel2 has a 580MHz processor, 64MB RAM and 32MB flash (just for comparison Pi3B has a Quad Core 1.2GHz processor, 1GB RAM) – both have Wifi and Ethernet built in.

Pi comes with a Debian based OS with full desktop-class capabilities (GUI, Applications etc.) where as Tessel2 just supports Node.JS based apps (it is running Open WRT) and has no GUI capabilities. Therefore it is lot closer to a IoT platform in terms of capabilities, power requirements and form factor.

Temperature Tweeter Architecture

Temperature Tweeter Architecture


Computing Platform: Tessel2

Tessel2 has a set of basic hardware features which includes USB2.0 ports, sensor sockets (where you can plug in different modules such as temperature, GPS, bluetooth) and one Ethernet socket.

Since there is no UI for the Tessel2 OS you have to install the ‘t2’ command line tool to interact with your tessel.

The Tessel2 website has an excellent ‘first steps’ section here.

If you are blessed with a Windows 10 based system you might have some issues with detecting the Tessel2. One solution is to install ‘generic USB drivers’ here. But Google is your friend in case you run into the dreaded: ‘Detected a Tessel that is booting’ message.

Data Source: Climate Sensor Module

The sensor module we use as a data source for this example is the climate sensor which gives the ambient temperature and the relative humidity. The sensor module can be purchased separately and you can connect up to two modules at a time.

Power and Data:

As the sensor is based indoors we use a standard micro-USB power supply. For external use we can use a power bank. The data connection is provided through a wired connection (Ethernet) – again as we are indoors.

The Node.JS Application

Start by creating a folder for your Tessel2 app and initialise the project by using the ‘t2 init’ command within that folder.

Create the node.js app to read data from the sensor and then use the ‘twitter’ api to create a tweet with the data. The application is really simple but shows off the power of Node.JS and the large ecosystem of libraries available for it.

One good thing about the Tessel2 is that because it is such a lightweight platform you really cannot run fulll sized Node.JS apps on it. As a comparison, a single Node.JS instance can use up to 1.8GB of RAM on a 64-bit machine where as Tessel2 has only 64MB RAM in total for everything that is running on it!

Most common type of applications that you will find yourself writing, in the IoT space, will involve reading some value from a sensor or attached device then either exposing it via a REST server running on Tessel2 itself (pull) or by calling a remote server to write the data (push).

In other words you will just end up writing pipelines to run on Tessel2 which read from a source and write to a destination. You can also provide support for cross cutting concerns such as logging, authentication and remote access.

If you want to Tweet the data then you will need to register a Twitter account and create a ‘Twitter app’ from your account settings. All the keys and secrets are then generated for you. The Twitter API for Node.JS is really easy to use as well. All the info is here.

The implementation can be found here:

Few Pointers

Don’t put any complex calculations, data processing or analytics functionality in the pipeline if possible. The idea is also that IoT devices should be deploy and forget.

Be careful of the libraries you use. Certain objects such as ‘clients’ and ‘responses’ can be quite large considering the fact that you only have less than 64MB of RAM to play around with. So you might want to run the program locally and profile the memory use just to be sure.

‘t2 run’ command allows you to test your program on the tessel2 while getting console output to your terminal. This is an excellent way of testing your programs. Once you are ready to ‘deploy and forget’ your Tessel2 just use the ‘t2 push’ command to load your Node.JS app on the device. Thereafter every time the device restarts it will launch your app.


This is the code for the ‘Climate Tweeter’:

‘npm install’ will get you all the imports.

var Twitter = require('twitter');
var tessel = require('tessel');
var climatelib = require('climate-si7020');
// Init Climate Module
var climate = climatelib.use(tessel.port['B']);
var data = {
  consumer_key: 'your consumer key',
  consumer_secret: 'your consumer secret',
  access_token_key: 'your access token key',
  access_token_secret: 'your access token secret'
var client = (new Twitter(data));
    if (climate_status) {
        // Read the Temperature and Humidity
        climate.readTemperature('c', function (err, temp) {
          climate.readHumidity(function (err, humid) {
            // Output Tweet
            var output = (new Date())+',Bristol UK,Home,Temp(C):'+ (temp.toFixed(2)-5) + ', Humidity(%RH):'+ humid.toFixed(2);
            //Tweet to Twitter
  'statuses/update', {status : output}, function(error, tweet, response) {
                    if (error) {												
                        console.error("Error: ",error);
climate.on('ready', function () {
  console.log('Connected to climate module');
  // Climate module on and working - we can start reading data from it
  climate_status = true;

What next?

The interesting thing is that I did not need anything external to the Tessel2 to make this work. I did not have to setup any servers etc. I can very easily convert the device to work outdoors as well. I could hook up a camera (via USB) to make this a ‘live’ webcam, attach a GPS and mobile data module with a power pack (for backup) and connect it to your car (via the power port or lighter) – you have a car tracking device.



Bots using Microsoft Bot Platform and Heroku: Customer Life-cycle Management

This post is about using the Microsoft Bot Platform with Heroku to build a bot!

The demo scenario is very simple:

  1. User starts the conversation
  2. Bot asks for an account number
  3. Customer provides an account number or indicates they are not a customer
  4. Bot retrieves details if available for a personalised greeting and asks how can it be of help today
  5. Customer states the problem/reason for contact
  6. Bot uses sentiment analysis to provide the appropriate response


Bots are nothing but automated programs that carry out some well defined set of tasks. They are old technology (think web-crawlers).

Recent developments such as Facebook/Skype platform APIs being made available for free, easy availability of cloud-computing platforms and relative sophistication of machine learning as a service  has renewed interest in this technology especially for customer life-cycle management applications.

Three main components of a modern, customer facing bot app are:

  • Communication Platform (e.g. Facebook Messenger, Web-portal,  Skype etc.): the eyes, ears and mouth of the bot
  • Machine Learning Platform: the brain of the bot
  • Back end APIs for integration with other systems (e.g. order management): the hands of the bot

Other aspects include giving a proper face to the bot in terms of branding but from a technical perspective above three are complete.

Heroku Setup

Heroku provides various flavours of virtual containers (including a ‘free’ and ‘hobby’ ones) for different types of applications. To be clear: a ‘dyno’ is a lightweight Linux container which runs a single command that you specify.

Another important reason to use Heroku is that it provides a ‘https’ endpoint for your app which makes it more secure. This is very important as most platforms will not allow you to use a plain ‘http’ endpoint (e.g. Facebook Messenger). So unless you are ready to fork out big bucks for proper web-hosting and SSL certificates start out with something like Heroku.

Therefore for a Node.JS dyno you will run something like node <js file name>.

The cool thing about Heroku (in my view) is that it integrates with Git so deploying your code is as simple as ‘git push heroku <branch name to push from>’.

You will need to follow a step by step process to make yourself comfortable with Heroku (including installing the Heroku CLI) here:

We will be using a Node.JS flavour of Heroku ‘dynos’.

Heroku has an excellent ‘hello world’ guide here:


Microsoft Bot Platform

The Microsoft Bot Platform allows you to create, test and publish bots easily. It also provides connectivity to a large number of communication platforms (such as Facebook Messenger). Registration and publishing is FREE at the time of writing.

You can find more information on the Node.js base framework here:

The dialog framework in the MS Bot Platform is based on REST paths. This is a very important concept to master before you can start building bots.


Microsoft provide a publishing platform to register your bot.

Once you have the bot correctly published on a channel (e.g. Web, Skype etc.) messages will be passed on to it via the web-hook.

You need to provide an endpoint (i.e. the web-hook) to a web app in Node.JS which implements the bot dialog framework to publish your bot. This web app is in essence the front door to your ‘bot’.

You can test the bot locally by downloading the Microsoft Bot Framework simulator.

The demo architecture is outlined below:

Bot Demo Architecture

Bot Demo Architecture

Detailed Architecture for the Demo

There are three main components to the above architecture as used for the demo:

  1. Publish the bot in the Bot Registry (Microsoft) for a channel – you will need your Custom Bot application endpoint to complete this step,in the demo I am publishing only to a web-channel which is the easiest to work with in my opinion. Once registered you will get an application id and secret which you will need to add to the bot app to ‘authorise’ it.
  2. Custom Bot Application (Node.JS) with the embedded bot dialog – the endpoint where the app is deployed needs to be public, a HTTPS endpoint is always better! I have used Heroku to deploy my app which gives me a public HTTPS endpoint to use in the above step.
  3. Machine Learning Services – to provide functionality to make the Bot intelligent, we can have a statically scripted bot with just the embedded dialog but where is the fun in that? For the demo I am using Watson Sentiment Analysis API to detect the users sentiment during the chat.

*One item that I have purposely left out within the Custom Bot app, in the architecture, is the service that provides access to the data which drives the dialog (i.e. Customer Information based on the Account Number). In the demo a dummy service is used that returns hard coded values for Customer Name when queried using an Account Number.

The main custom bot app Javascript file is available below, right click and save-as to download.

Microsoft Bot Demo App