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JavaScript for Microcontrollers and IoT: A Web Server

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JavaScript for Microcontrollers and IoT: A Web Server

With the grunt work done, let's see if it's worth using JavaScript over C to create an IoT app that can handle request routing and uses JWTs.

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In our last post from the JavaScript for Microcontrollers and IoT series, we talked about building a sensor hub. We succeeded, but our use of JavaScript remained small in contrast to the amount of C code that was necessary to write. In this post, we take our sensor hub and expand it using JavaScript to act as a web server in our local network. The web server will display readings from the sensors but only for authenticated users. Will it be as easy as it looks? Read on to find out!


In our last post, we finally used all the code we developed in the first post for something useful. We created a small script that was in charge of reading values from sensors and then sending that information to different destinations. We sent the code to the cloud, to be handled by a Webtask that in turn could do useful stuff with it (like sending an email when thresholds were exceeded), and we also sent the readings to a computer in the local network. However, there was no way for a user in the local network to simply take a look at the readings (unless they set up a web server on a computer). We also came to the conclusion that the added complexity of setting up a JavaScript interpreter and then exposing the C API through it was simply not worth it for small scripts. Things could be different if the JavaScript code were bigger, or, in other words, if most of the development happened on the JavaScript side of things. So for this post, we decided to run the experiment: let's write something bigger in JavaScript and see where that gets us.

The Plan

We already have the sensor hub, so the next logical step is to have some way to see the readings from any smart device in the local network. One simple way to do that is to simply have a webpage served by the microcontroller. We could put the readings there!

Now, if you recall what we saw of the Particle API in previous posts, you may remember we did have TCP sockets and WiFi. That's great! However, for this, we are missing a key part of the puzzle: an HTTP server. What we want to do should be simple enough, and luckily, HTTP is, for the most part, rather simple for small tasks like ours. Could we integrate a small HTTP server using JavaScript in our sensor-hub example? It turns out we can.

For our example, we have decided to use http-parser-js, a JavaScript-only implementation of Node's HTTP parser. Node's HTTP parser is written in C, so we could actually use that instead, but the point of using JavaScript on a microcontroller is to write less error prone C code and more JavaScript. The JavaScript version should be simpler to use, as long as our interpreter is up to the task.

Using http-parser-js, we developed a small example that passes data from Particle's TCP API to the parser and then back to user-specified handlers, in the spirit of the rather simple Express library (although with a completely different, and much simpler API).

We also decided to add a small authentication screen to the sensor readings page. Since our example is only meant to run on the local network, as there is no SSL/TLS available on the Particle API yet, this is mostly for educational or testing purposes. For this, we will use auth0.js, which lets us add authentication to a page with only a few lines of code.

An interesting side of adding authentication is actually having the microcontroller validate the credentials. Since Auth0 implements OpenID Connect, we will learn how to validate JWTs on the microcontroller too! For this task, we decided to use our clean-room implementation of JWT HMAC signatures from the JWT Handbook. Why? Because it has no external dependencies and it is very small. Of course, for production uses you should prefer a well-tested library with better error handling and not an educational one. Still, this poses an interesting challenge for our interpreter of choice: JerryScript.

Some of the libraries we decided to use for this require ECMAScript 2015. JerryScript, the interpreter we have been using so far, only support ECMAScript 5.1, so we will also learn to use a transpiler and bundler to accomplish our mission. For this task, we will be using Rollup and Babel. Rollup produces very small code, and size is always important when working with microcontrollers.

To sum up: We will expand our JerryPhoton library to support incoming TCP connections (listening TCP sockets). We will parse HTTP requests using http-parser-js. We will write a small HTTP class that will read the HTTP request and dispatch it to the right handler. We will convert all the JavaScript code into a single bundle using only ECMAScript 5.1. We will embed an HTML web page inside our JavaScript code using Rollup and then serve it according to the HTTP request. We will validate JWTs to protect API endpoints using our educational, clean-room implementation of HMAC signatures from the JWT Handbook. We will rely on auth0.js to perform the authentication for us.

Looks like quite a ride, so buckle up!


Incoming TCP Connections

The Particle API provides a convenient class to handle incoming TCP connections: TCPServer. Fortunately, TCPServer instances return TCPClient instances once the connection is established, so most of the hard work is already done in JerryPhoton (which provides a wrapper for TCPClient instances in JavaScript). We just need to create a new JavaScript object to expose the functionality (ṕhoton.TCPServer) and include a single method in it: available.

This is simple enough:

static jerry_value_t 
create_tcp_server(const jerry_value_t func,
                  const jerry_value_t thiz,
                  const jerry_value_t *args,
                  const jerry_length_t argscount) {
    jerry_value_t constructed = thiz;

    if(argscount != 1 || !jerry_value_is_number(*args)) {
        return jerry_create_error(JERRY_ERROR_COMMON,
            reinterpret_cast<const jerry_char_t*>("TCPServer: missing port"));

    const uint16_t port = static_cast<uint16_t>(jerry_get_number_value(*args));

    // Construct object if new was not used to call this function
        const jerry_value_t ownname = create_string("TCPServer");
        if(jerry_has_property(constructed, ownname)) {
            constructed = jerry_create_object();

        const jerry_value_t name = create_string("available");
        const jerry_value_t func =

        jerry_set_property(constructed, name, func);


    // Backing object
    TCPServer* server = new TCPServer(port);
    jerry_set_object_native_pointer(constructed, server, &server_native_info);

    return constructed;

And the available method:

static jerry_value_t 
tcp_server_available(const jerry_value_t func,
                     const jerry_value_t thiz,
                     const jerry_value_t *args,
                     const jerry_length_t argscount) {
    TCPServer* server = NULL;
    const jerry_object_native_info_t *native_info = NULL;

        reinterpret_cast<void**>(&server), &native_info);

    if(native_info != &server_native_info) {
        return jerry_create_error(JERRY_ERROR_TYPE,
            reinterpret_cast<const jerry_char_t*>(
                "TCPServer.available called with wrong this pointer"));

    TCPClient* client = new TCPClient(server->available());
    jerry_value_t jsclient = jerry_create_object();

    build_tcp_client_object(jsclient, client);

    return jsclient;

If you don't understand the signatures of these C++ functions read the first post in this series, where we explore the integration of JerryScript on the Particle Photon.

We can now use this object from within JavaScript like so:

var server = photon.TCPServer(80);
while(true) {
    var client = server.available();
    if(client.connected()) {
        // Do something with the client

Transpiling and Bundling Code

Before starting to work with our JavaScript code, we need to set up a way to bundle everything in a single JavaScript file so we can easily include it in our project. We also need a transpiler to convert ECMAScript 2015 code to ECMAScript 5.1 code. Let's take a look at how to do that with Rollup and Babel.

Rollup's main feature is to let you mix up JavaScript code with different module systems. In particular, Rollup was conceived to handle integration between CommonJS modules and ECMAScript 2015 modules seamlessly. There are other module bundlers that can do this, like Webpack, but Rollup is very simple to configure for minimum code size with the least possible amount of added support code in the resulting bundle. Our Rollup configuration is as follows:

import resolve from 'rollup-plugin-node-resolve';
import babel from 'rollup-plugin-babel';
import cjs from 'rollup-plugin-commonjs';
import strip from 'rollup-plugin-strip';
import html from 'rollup-plugin-html';
import uglify from 'rollup-plugin-uglify';
import {
from 'uglify-es';

export default {
    entry: './main.js',
    format: 'es',
    dest: './dist/main.bundle.js',
    useStrict: false,
    plugins: [
            include: '**/*.html',
            htmlMinifierOptions: {
                collapseWhitespace: true,
                collapseBooleanAttributes: true,
                conservativeCollapse: true,
                minifyJS: true
            debugger: true
            exclude: 'node_modules/**', // only transpile our source code
            presets: ['es2015-rollup']
        uglify({}, minify)

As you can see, we are using a number of plugins. These plugins give us the following functionality:

  • node-resolve: lets us resolve packages from within node_modules.
  • babel: converts ECMAScript 2015 code to ECMAScript 5.1 code. We are using the es2015-rollup Babel preset that basically converts everything to ECMAScript 5.1 except the module declarations. These declarations are handled internally by Rollup.
  • commonjs: handles require and module.exports usage from within modules.
  • strip: removes the use of common debugging calls like assert. We do not want (to save space) nor have that functionality in our interpreter so we need to remove that.
  • html: takes an HTML file and embeds it in the bundle inside a string. We will use this to integrate our webpage inside our bundle.
  • uglify: space is everything: we need to keep the size of the bundle as small as possible, so Uglify can help us to achieve that. Uglify does not support ECMAScript 2015 modules yet, so we need to use a specific minifier that can do that: that is what uglify-es provides.

With this pipeline, we will get a single JavaScript file with all we need. If you want to take a look at how the resulting code compares to the original code, comment the uglify call in the plugins array. The resulting code is pretty readable and has very little added support code.

Wait, how are we going to upload this to the microcontroller?

Another thing we need is to find a way to get the bundle into the microcontroller. We will now be working with larger amounts of code, so we cannot use the simple upload functionality we developed in post 1. The upload functionality allowed us to dynamically run JavaScript code sent through a TCP socket. This was great, but to do so the code was first copied into RAM and then run from there. The Particle Photon does not have a lot of RAM, so we cannot waste it by keeping our script there. Fortunately, there is a way to embed our JavaScript bundle into the ROM!

The Particle API does not have a concept of a file or resource system, therefore anything that must be available to the C code in form of data must also be included in the code itself. Fortunately for us, this is very easy to do with some minor shell scripting. Once we have the JavaScript bundle we can convert it to a C-array using xxd, a tool to produce textual binary dumps. xxd conveniently provides an option to produce C-arrays as output.

Here's the whole JavaScript source to JavaScript bundle to C-array pipeline:

cd js
echo 'Are npm packages installed?'
if [ ! -d node_modules ]; then
    echo 'Nope, installing npm packages.'
    npm install
echo 'Yes, building JavaScript bundle using Rollup.'
node_modules/rollup/bin/rollup -c

cd dist
echo 'Putting JavaScript bundle into a C array.'
xxd -i main.bundle.js > ../../src/main.bundle.h
cd ../..

echo 'Making bundle C array static const.'
sed -i -e 's/^unsigned/static const/' src/main.bundle.h

The last command, sed, is necessary because we want to make sure our C-array gets stored in ROM and not in RAM. To tell the C compiler that, we need to make the array static and const. We also change the type from unsigned char to just char. This makes no difference for the data in it and matches the signature of the jerryphoton::eval() function.

Integrating the HTTP Parser

The first library that we are going to integrate is the HTTP parser (http-parser-js). This library is a simple JavaScript-only HTTP parser meant to work as a drop-in replacement for Node.js's C-based parser. It provides the exact same JavaScript API. However, since this parser was written with Node in mind, certain minor adaptations must be performed before we can use it in JerryScript. We'll talk about them here.

The first and biggest change has to do with the use of Node's Buffer object. Buffer is a Node-specific object and we can't use it here. There are two ways we could fix this here: we can rely on JerryScript's limited support for ECMAScript 2015's TypedArray, or we can use JavaScript strings. After taking a look at the code that uses Buffer we decided to go the String route. Let's take a look at the code:

HTTPParser.prototype.consumeLine = function() {
    var end = this.end,
        chunk = this.chunk;
    for (var i = this.offset; i < end; i++) {
        if (chunk[i] === 0x0a) { // \n
            var line = this.line + chunk.toString('ascii', this.offset, i);
            if (line.charAt(line.length - 1) === '\r') {
                line = line.substr(0, line.length - 1);
            this.line = '';
            this.offset = i + 1;
            return line;
    //line split over multiple chunks
    this.line += chunk.toString('ascii', this.offset, this.end);
    this.offset = this.end;

Fortunately for us, this is the only function where Buffer is used. The variable chunk is a Buffer. The methods used are array access (chunk[i]) and chunk.toString('ascii', ...). These uses are very simple to adapt to String:

HTTPParser.prototype.consumeLine = function() {
    var end = this.end,
        chunk = this.chunk;
    for (var i = this.offset; i < end; i++) {
        if (chunk.charCodeAt(i) === 0x0a) { // \n
            var line = this.line + chunk.substring(this.offset, i);
            if (line.charAt(line.length - 1) === '\r') {
                line = line.substr(0, line.length - 1);
            this.line = '';
            this.offset = i + 1;
            return line;
    //line split over multiple chunks
    this.line += chunk.substring(this.offset, this.end);
    this.offset = this.end;

We use chunk.charAt and chunk.substring. This will work for our simple usage.

Since we performed modifications to http-parser-js , we included it in our code rather than use the version from node_modules.

The HTTP Class

To process HTTP requests we will write a simple JavaScript class that will use http-praser-js and then call a user-defined handler with the parsed request. Since we are using Babel we will write an ECMAScript 2015 class:

export default class HTTP {
    constructor(tcpClient, handler) {
        this.client = tcpClient;

        this.parser = new HTTPParser('REQUEST');

        this.parser[HTTPParser.kOnHeadersComplete] = info => {
            handler(this, info.headers, info.method, info.url);

    process() {
        if(this.client.available() === 0) {


    isConnected() {
        return this.client.connected();

    sendHtml(html) {
        sendResponse(this.client, 200, 'html', html);

    sendJson(json) {
        sendResponse(this.client, 200, 'json', json);

    send401() {
        sendResponse(this.client, 401);

    close() {

The HTTP class takes a Particle TCPClient object and a JavaScript function as constructor parameters. When the process method is called, if an HTTP request is completely parsed, the handler will be called. If the HTTP request is not completely present in the TCPClient buffer, the state will be preserved for future calls (that is, one must call process repeatedly until the request is completely processed). The handler will receive the HTTP object, the headers, the name of the HTTP method and the full URL of the request.

The HTTP class also provides a couple of simple methods for sending responses: sendHtml, sendJson and send401. The sendResponse private function handles all the work:

function sendResponse(client, status, type, data) {
    const msg = `HTTP/1.1 ${getStatus(status)}\r\n` + 
                `Server: Custom\r\n` + 
                getDataHeaders(type, data) +
                `Connection: Closed\r\n\r\n`;


    if (data) {
        const chunkSize = 256;
        for(let bytes = 0; bytes < data.length; bytes += chunkSize) {
            const chunk = data.substr(bytes, chunkSize);

            for (let written = 0; written < chunk.length;) {
                written += client.write(chunk.substring(written));

You may have noticed that the part of this function that writes to the socket is a bit contrived. This is necessary to keep RAM usage withing acceptable levels. Since we will be using this function to send an HTML page to a browser, depending on the size of the HTML page, it may be necessary to create a very big buffer to send the data (data from the JavaScript side is copied in the C side). To keep this at reasonable levels, we send the data in chunks: one 256-byte chunk at a time. You can experiment with different sizes of chunks, but we found that for our simple usage, this worked without problems.

The JWT Decoding and Verification Functions

As we mentioned before, we are going to use the basic JWT decoding and verification functions from the JWT Handbook. These functions are clean-room implementations of all the algorithms, down to Base64 encoding. These functions were written for educational purposes and are not ideal from a performance and security point of view. However, they are very small and the code is clear enough to be easily debugged.

DISCLAIMER: Do not use these functions in production. They were not tested in the wild and are only meant for educational purposes. Clarity of implementation was the main criteria used when they were written. You should not consider them secure.

The clean room implementation of HMAC signatures used in the JWT Handbook relies heavily on ECMAScript 2015 features. In particular, TypedArray classes are used everywhere. Fortunately for us JerryScript developers are already working on an implementation of typed arrays. However, at the time we wrote this, the implementations were incomplete. Nonetheless they are perfectly usable with a couple of adaptations. We also relied on ECMAScript 2015 new methods for String. These are also easy to replace. Let's take a look:


The only String method that was used from ECMAScript 2015 is endsWith. This method takes a string and checks whether the string used as this ends with the specified string. If it does, it returns true, otherwise it returns false.

JerryScript appears to be designed in such a way that changing the prototypes of built-in objects does not work correctly. Although this practice is frowned upon, it is sometimes useful. In this case we are trying to provide a polyfill, so it would certainly make sense to be able to do something like this. In any case, we can still provide free functions to do the same.

export function endsWith(thiz, str) {
    var idx = thiz.indexOf(str);
    if(idx === -1) {
        return false;
    return (idx + str.length) === thiz.length;

Unfortunately, this means we must now find all uses of endsWith and change them. You can find this function in utils.js.


The JWT Handbook examples make use of two unimplemented methods in JerryScript: set and fill. The set method provides a way to set the contents of a typed array with the elements from another array (either a common array or a typed array). The fill method, on the other hand, sets a number of elements all to the same value.

export function fill(arr, elem) {
    for(var i = 0; i < arr.length; ++i) {
        arr[i] = elem;

export function set(target, source, offset) {
    var off = offset ? offset : 0;
    for(var i = 0; (i < source.length) && ((i + off) < target.length); ++i) {
        target[i + off] = source[i];

Just like what happened with String, JerryScript does not allow us to modify the prototype of the TypedArray object, thus it is necessary to provide free functions as polyfills and then change the code manually.

You can find these functions in utils.js.


It would appear that the version of JerryScript that we used for this example has a bug in the implementation of TypedArray.of. For this reason, we repĺaced all uses of TypedArray.of for code like this:

const h_ = new Uint32Array(8);
set(h_, [

The HTTP Request Handler

The main business logic of our application is the HTTP request handler, which ties all other parts together. The handler takes an HTTP request and dispatches it to the right functions to act according to the URL. All of this code is located in our main.js function. Let's take a look:

import * as sensors from './sensors.js';
import HTTP from './http.js';
import page from './index.html';
import { jwtVerifyAndDecode } from './hs256.js';

// (...)

function handler(http, headers, method, url) {
    if(url.indexOf('/get-sensor-data') !== -1) {
        sendSensorData(http, headers);
    } else {

const server = photon.TCPServer(80);
let httpClients = [];
setInterval(() => {
    httpClients.push(new HTTP(server.available(), handler));

    const connected = [];
    httpClients.forEach(client => {

        if(!client.isConnected()) {


    // Discard disconnected clients.
    httpClients = connected;
}, 0);

Here we see we have a function that gets repeatedly executed as fast as possible after giving the system some time to process other stuff. The function checks whether there is a new connection available, and if there is and it remains connected, it attempts to read data from it. This is done for all active connections, which are stored inside the httpClients array. All disconnected TCP connections are discarded after each loop (and collected by the garbage collector eventually). A new instance of theHTTP class is created for each new connection. The handler for HTTP requests is the handler function, which simply checks for one of two endpoints: the main page, and the endpoint that returns sensor data. The page variable is where the HTML file that serves as our main page is stored as a string.

Other functions from the main.js file:

// Get this from https://manage.auth0.com/#/apis
const secret = 'test';
const audience = '/get-sensor-data';
const issuer = 'https://speyrott.auth0.com/';


function validateJwt(headers) {
    try {
        const idx = headers.indexOf('ACCESS-TOKEN');
        if(idx === -1) {
            return false;

        const decoded = jwtVerifyAndDecode(headers[idx + 1], secret);

        return decoded.valid && 
               decoded.payload.aud == audience &&
               decoded.payload.iss == issuer;
    } catch(e) {
        return false;
    return false;

function sendSensorData(http, headers) {
    if(!validateJwt(headers)) {


The Web Page

The web page is really simple. Of course, you could make something much more pleasing from an aesthetic point of view. The page displays a text introduction, and then either a login button, or the report from the sensors in textual form. The web page periodically requests new sensor data using XMLHttpRequest. Let's see how it works.

DISCLAIMER: Since there is no SSL/TLS support on the Particle Photon, this page is NOT secure. All tokens are sent in the clear. Do not use this example outside of your local network, or even inside your local network if you need to keep tokens secure.

<!DOCTYPE html>

    <title>Local Sensors</title>
    <script src="https://cdn.auth0.com/js/auth0/8.8.0/auth0.min.js"></script>

    <p>Hello, this is your local sensors report</p>
    <p>BEWARE: this example should only be used on trusted networks! NO SSL/TLS IS IN PLACE, TOKENS TRAVEL IN THE CLEAR IN YOUR LOCAL NETWORK.</p>

    <button id="login-button" onclick="loginClicked()">Login</button>

    <div id="sensors-report">
            <span style="font-weight: bold">Movement: </span>
            <span id="sensor-movement"></span>
            <span style="font-weight: bold">Flame: </span>
            <span id="sensor-flame"></span>
            <span style="font-weight: bold">Humidity: </span>
            <span id="sensor-humidity"></span>
            <span style="font-weight: bold">Temperature: </span>
            <span id="sensor-temperature"></span>
            <span style="font-weight: bold">Gas: </span>
            <span id="sensor-gas"></span>
            <span style="font-weight: bold">
                Time since last alarm in milliseconds: 
            <span id="sensor-time-since-alarm"></span>

    <button id="logout-button" onclick="logoutClicked()">Logout</button>

    <!-- ... -->

The HTML page embeds a small script to do all its magic. The script checks whether the user has logged in previously and shows or hides the necessary elements according to that:

const accessToken = localStorage.getItem('access_token');

// (...)

if(accessToken) {
    loginButton.style.display = 'none';
    logoutButton.style.display = 'block';
    report.style.display = 'block';

    if(!refreshInterval) {
        setInterval(refresh, 2000);
} else {
    loginButton.style.display = 'block';
    logoutButton.style.display = 'none';
    report.style.display = 'none';

    if(refreshInterval) {

The refresh function gets the sensor data using the access token and then updates the DOM accordingly:

function refresh() {
    httpGet('/get-sensor-data', accessToken).then(data => {
        try {
            const sensors = JSON.parse(data);

            sensorMovement.innerHTML = sensors.data.movement ?
                'detected' : 'undetected';

            sensorFlame.innerHTML = sensors.data.flame ? 
                'detected' : 'undetected';

            sensorHumidity.innerHTML = 
                sensors.data.humidity.toString() + '%';

            sensorTemperature.innerHTML = 
                sensors.data.temperature.toString() + ' Celsius';

            sensorGas.innerHTML = sensors.data.gas.toString();

            sensorTimeSinceAlarm.innerHTML = 
                sensors.timeSinceLastAlarmMs.toString() + 'ms';
        } catch(e) {
    }).catch(status => {
        if(status === 401) {

The httpGet function is a simple wrapper around XMLHttpRequest:

function httpGet(url, accessToken) {
    return new Promise((resolve, reject) => {
        const request = new XMLHttpRequest();            

        request.onreadystatechange = () => { 
            if(request.readyState !== XMLHttpRequest.DONE) {

            if(request.status === 200) {
            } else {

        request.open("GET", url, true);
        request.setRequestHeader('ACCESS-TOKEN', accessToken);

And last but not least there's Auth0 authentication:

// Get this from https://manage.auth0.com/#/clients
const auth0Client = new window.auth0.WebAuth({
    domain: "speyrott.auth0.com",
    clientID: "5OzskonPwTAikfl1pIexAZYPuJN65WmK"

function parseHash() {
    const re = /access_token=(.*?)&/;
    const match = re.exec(window.location);
    if(match) {
        localStorage.setItem('access_token', match[1]);
        document.location.href = '/';

function loginClicked() {
        audience: '/get-sensor-data',
        scope: 'read:sensors',
        responseType: 'token id_token',
        redirectUri: ''

function logoutClicked() {


By using the auth0.js library, authentication and authorization are just a matter of calling auth0Client.authorize. This will send the user to the Auth0 login page. After the user is authenticated, the authorization server will redirect the user back to our sensor site with the right access token for our API. This is what our parseHash function does: it gets the token from the URL and stores it in local storage.

To use Auth0 you will first need to perform a couple of simple steps, which we describe below.

Setting Up Auth0

To use Auth0 to authenticate, authorize and get an access token for our API we need to perform two steps: first we need to create a client (this identifies our client application to the authorization server), and second, we need to create an API endpoint so that we can request access tokens for it. If you haven't signed up for Auth0, sign up now. You can use the free tier for this example!

Create a Client

  1. Go to the Auth0 dashboard and select Clients.
  2. Click on Create Client.
  3. Choose a name and put it in the text field near the top.
  4. Select Regular Web Applications.
  5. Select Settings.
  6. You will now see your Auth0 Domain and Auth0 Client ID on the screen. Take note of these and set them in both index.html and main.js.
  7. Go down and find the Allowed Callback URLs field. Set the IP address of your Particle Photon there as an URL. Example: You should make sure the Particle Photon gets assigned the same IP address always (most modern WiFi routers keep track of this).

Create an API Endpoint

  1. Go to the Auth0 dashboard and select APIs.
  2. Click on Create API.
  3. Choose a name. In the Identifier field put /get-sensor-data. This is what the access token will carry in the aud (audience) claim. For the algorithm pick HS256. Click on Create.
  4. Go to Settings and take note of the signing secret. Set it in main.js.

That's it! Once you have done this Auth0 is ready to use.

Finishing Touches

To be able to run this example another minor change was required with regards to post 2. We had to slightly increase the heap size for JerryScript. Fortunately, there still is some free RAM in the Photon. Heap size is now set to 42KB, you can see this in Makefile.particle. That's right, this whole example uses less than 42KB of RAM, and its running on JavaScript!

Another change that was necessary was to enable typed arrays and regular expressions in JerryScript. Typed arrays are used by the JWT libraries, and regular expressions by the HTTP parser. Even so, RAM use remains really low! You can see these changes in custom.profile, the JerryScript profile used for this example.

Let's see it in action!

Get the full code for this example. If you need help flashing the compiled firmware, refer to the previous post.


In our previous post, we managed to get something useful running on JavaScript, but we didn't really develop a full fledged application. For this post, however, we upped the ante and managed to run our own web server doing most of the work using only JavaScript. We handled connections, HTTP parsing, request dispatching, and JWT validation with HS256 (HMAC + SHA256). We also integrated a Node library (http-parser-js) and wrote all of our code using ECMAScript 2015 with modules. The result is over 1000 lines of JavaScript, or around 15KiB of minified JavaScript. This all runs on a 120MHz ARM CPU and uses less than 42KB of RAM!

The development experience was not without trouble. JerryScript remains rough around the edges for now. Some constructs are not handled correctly (for example, the common immediately invoked function expression appears to fail), typed arrays remain incomplete, and using polyfills by adding or changing methods in a prototype does not work as expected. These are all common patterns or tools in the JavaScript world, and there may be more small differences in behavior between it and the more powerful V8 or SpiderMonkey implementations.

So now that we have gone over the experience of developing something bigger using JavaScript, has it changed our opinion from the last post that JavaScript only makes sense for microcontrollers if you don't need to fall back to C often? To be honest, no, it has not changed. Using existing libraries has been tremendously helpful, but we still had to debug them and find out the very specific differences between JerryScript and other more common JavaScript engines. This took some time. We also estimate that the performance of doing HTTP parsing and JWT decoding and verification on an interpreter is much slower than doing them in C code. For our case, it has not resulted in problems, but bigger codebases may struggle to be performant. Things may be different using a different JavaScript engine. What we have seen so far is very good, but not entirely production ready. Using JavaScript through JerryScript on the Particle Photon remains an interesting option for smaller teams or hobbyists.

In our next post we will take a look at the ESP8266 (finally) and Espruino, a different firmware that comes with an integrated JavaScript interpreter and most of its API already exposed through it. We will see if a different development environment results in a more "production-ready" experience. Until then, hack on!

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iot ,javascript ,microcontrollers ,web server ,tutorial

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