Using Azure IoT Hub to connect my home to the cloud

I’ve written about my hybrid local/cloud home automation architecture previously: in summary most of the moving parts and automation logic live on a series of Raspberry Pis on my home network, using a MQTT broker to communicate with each other. I bridge this “on-prem” system with the cloud in order to route incoming events, e.g. actions initiated via my Alexa Skills, from the cloud to my home, and to send outgoing events, e.g. notifications via Twilio or Twitter.

Historically this bridging was done using Octoblu, having a custom Octoblu MQTT connector running locally and an Octoblu flow running in the cloud for the various inbound and outbound event routing and actions. However, with Octoblu going away as a managed service hosted by Citrix, I, like other Octoblu users, needed to find an alternative solution. I decided to give Azure IoT Hub and its related services a try, partly to solve my immediate need and partly to get some experience with that service. Azure IoT isn’t really the kind of end-user/maker platform that Octoblu is, and there are some differences in concepts and architecture, however for my relatively simple use-case it was fairly straightforward to make Azure IoT Hub and Azure Functions do what I need them to do. Here’s how.

I started by creating an instance of an Azure IoT Hub, using the free tier (which allows up to about 8k messages per day), and within this manually creating a single device to represent my entire home environment (this is the same model I used with Octoblu).

After some experimentation I settled on using the Azure IoT Edge framework (V1, not the more recently released V2) to communicate with the IoT Hub. This framework is a renaming and evolution of the Azure IoT Gateway SDK and allows one or more devices to be connected via a single client service framework. It is possible to create standalone connectors to talk to IoT Hub in a similar manner to how Octoblu connectors work, but I decided to use the Edge framework to give me more flexibility in the future.

There are various ways to consume the IoT Edge/gateway framework; I chose to use the NPM packaged version, adding my own module and configuration. In this post I’ll refer to my instance of the framework as the “gateway”. The overall concept for the framework is that a number of modules can be linked together, with each module acting as either a message source, sink, or both. The set of modules and linkage are defined in a JSON configuration file. The modules typically include one or more use-case specific modules, e.g. to communicate with a physical device; a module to bidirectionally communicate with the Azure IoT Hub; and a mapping module to map between physical device identifiers and IoT Hub deviceNames and deviceKeys.

The requirements for my gateway were simple:

  1. Connect to the local MQTT broker, subscribe to a small number of MQTT topics and forward messages on them to Azure IoT Hub.
  2. Receive messages from Azure IoT Hub and publish them to the local MQTT broker.

To implement this I built a MQTT module for the Azure IoT Edge framework. I opted to forego the usual mapping module (it wouldn’t add value here) and instead have the MQTT module set the deviceName and deviceKey for IoT Hub directly, and perform its own inbound filtering. The configuration for the module pipeline is therefore very simple: messages from the IoT Hub module go to the MQTT module, and vice-versa.

The IoT Edge framework runs the node.js MQTT module in an in-process JavaScript interpreter, with the IoT Hub being a native code module that runs in the same process. Thus the whole gateway is run as a single program with the configuration supplied as its argument.

The gateway runs on a Pi with my specific deviceName and deviceKey, along with MQTT config, stored locally in a file “/home/pi/.iothub.json” that look like this:

{
  "iothub":{
    "deviceName":"MyMQTTBroker",
    "deviceKey":"<deviceKey for device as defined in Azure IoT Hub>",
    "hostname":"<my_iot_hub>.azure-devices.net"
  },
  "localmqtt":{
    "url":"mqtt://10.52.2.41",
    "protocol":"{\"protocolId\": \"MQIsdp\", \"protocolVersion\": 3}"
  }
}

The gateway can now happily send and receive messages from Azure IoT Hub but that isn’t very useful on its own. The next step was to setup inbound message routing from my Alexa Skills.

aziotIn the previous Octoblu implementation the Alexa Skills simply called an Octoblu Trigger (in effect a webhook) with a message containing a MQTT topic and message body. The Octoblu flow then sent this to the device representing my home environment and the connector running on a Pi picked it up and published it into the local MQTT broker. The Azure solution is essentially the same. I created an Azure Function (equivalent to an AWS Lambda function) using a JavaScript HTTP trigger template, that can be called with a topic and message body, this then calls the Azure IoT Hub (via a NPM library) to send a “cloud-to-device” (C2D) message to the MQTT gateway device – the gateway described above then picks this up and publishes it via the local broker just like the Octoblu connector did. I then updated my Alexa Skills’ Lambda Functions to POST to this Azure Function rather than to the Octoblu Trigger.

The code for the Azure function is really just argument checking and plumbing to call into the library that in turn calls the Azure IoT Hub APIs. In order to get the necessary Node libraries into the function I defined a package.json and used the debug console to run “npm install” to populate the image (yeah, this isn’t pretty, I know) – see the docs for details on how to do this.

If you’re wondering why I’m using both AWS Lambda and Azure Functions the reason is that Alexa Smart Home skills (the ones that let you do “Alexa, turn on the kitchen lights”) can only use Lambda functions as backends, they cannot use general HTTPS endpoints like custom skills can. In a different project I have completely replaced an Alexa Skill’s Lambda function with an Azure function (which, like here, calls into IoT Hub) to reduce the number of moving parts.

So with all of this I can now control lights, TV, etc. via Alexa like I could previously, but now using Azure IoT rather than Octoblu to provide the cloud->on-prem message routing.

logicappThe final use-case to solve was the outbound message case, which was limited to sending alerts via Twitter (I had used Twilio before but stopped this some time back). My solution started with a simple Azure Logic App which is triggered by a HTTP request and then feeds into a “Post a Tweet” action. The Twitter “connection” for the logic app is created in a very similar manner to how it was done by Octoblu, requiring me to authenticate to Twitter and grant permission for the Logic App to access my account. I defined a message scheme for the HTTP request which allowed me to POST JSON messages to it and use the parsed fields (actually just the “message” field for now) in the tweet.

I then created a second Azure Function which is configured to be triggered by Event Hub messages using the embedded Event Hub in the IoT Hub (if that all sounds a bit complex, just use the function template and it’ll become clearer). In summary this function gets called for each device-to-cloud event (or batch of events) received by the IoT Hub. If the message has a topic of “Alert” then the body of the message is sent to the Logic App via its trigger URL (copy and paste from the Logic App designer UI). I added the “request” NPM module to the Function image using the same procedure as for the iot-hub libraries above.

The overall flow is thus:

  1. Something within my home environment publishes a message on the “Alert” topic.
  2. The Azure IoT gateway’s MQTT module is subscribed to the “Alert” topic, receives the published message, attaches the Azure IoT Hub deviceName and deviceKey and sends it as an event via the IoT Hub module which sends it via AMQP to Azure IoT Hub.
  3. Azure IoT Hub invokes the second Azure Function with the event.
  4. The Function pulls out the MQTT topic and payload from the event and calls the Logic App with them.
  5. The Logic App pulls out the message payload and send this as a tweet using the pre-authorised Twitter connector.

Although all of this seems quite complex, it’s actually fairly simple overall: the IoT hub acts an a point of connection, with the on-prem gateway forwarding events to and from it, and a pair of Azure Functions being used for device-to-cloud and cloud-to-device messages respectively.

simple

It was all going well until I discovered that the spin-up time for an Azure Function that’s been dormant for a while can be huge – well beyond the timeout of an Alexa Skill. This is partly caused by the time it takes for the function runtime to load in all the node modules from the slow backing store, and partly just slow spin-up of the (container?) environment that Azure Functions run within. A common practice is to ensure that functions are invoked sufficiently often that Azure doesn’t terminate them. I followed this practice by adapting my existing heartbeat service running on a Pi that publishes a heartbeat MQTT every 2 minutes to also have it call the first Azure function (the one that the Alexa Skills call) with a null argument; and to keep the second function alive I simply had the MQTT gateway subscribe the heartbeat topic thereby ensuring the event handler function ran at least once every 2 minutes as well.

 

Advertisements

A universal IR remote control using MQTT

In some previous hacking I created an add-on for the Kodi media player which allowed me to control Kodi and the TV it is connected to (by using an IR blaster) using messages published through my home MQTT broker. The original purpose for this hack was to enable voice control via an Amazon Echo Smart Home Skill.

alloffI’ve since added another use-case where a single push-button connected to a Raspberry Pi Zero W publishes a single MQTT message which my rules engine picks up and then publishes a number of messages to act as an “all off” button: it sends “off” messages to all lights in the room (these are a mixture of LightwaveRF and Philips Hue – both interfaced via services that subscribe to the local MQTT broker); a “pause” message to Kodi; and a TV “off” message to the TV.

However, despite having this capability I still use separate traditional IR remote controls for the TV and Kodi, and a 433MHz control for the LightwaveRF lights. It seemed like a good idea to take advantage of the MQTT control to reduce the need for so many remote controls so I set about turning the Hama remote control I use for Kodi into a universal control for all the devices.

The strategy I used was to have the Hama remote control publish MQTT messages and add some rules to the broker’s rules engine to map these to the required MQTT messages(s) to control Kodi, the TV, or the lights. I chose to connect the Hama USB IR receiver to a new Raspberry Pi Zero W – I could have left this connected to the Pi 3 running Kodi and created a new add-on to talk to it but I have future plans that call for another Pi in this location and this seemed like it would be easier… – and set about building a small service to run on the Pi to relay received IR commands to the MQTT broker.

iroverview
Overview of the overall architecture

After a few false starts with LIRC I settled on consuming events from the remote control via the Linux event subsystem (same as handles keyboard and mouse). There are some node.js libraries to enable this but I found a much more complete library available for Python which, critically, implements the “grab” functionality to prevent “keystrokes” from the IR control also going to the Pi’s login console.

I’ve already implemented a few Python MQTT clients using the Paho library (including the Kodi add-on itself) so I recycled existing code and simply added an input listener to attach to the two event devices associated with the IR control (hard-coded for now) and, after a little processing of the event, publish an MQTT message for each button press. The Hama remote acts like a keyboard and some of the buttons include key modifiers: this means that a single button push could involve up to 6 events: e.g. key-down for left-shift, key-down for left-ctrl, key-down for ‘T’, followed by three key-up events in the reverse order. My code maintains a simple cache of the current state of the modifier keys so that when I get a key-down event for a primary key (e.g. ‘T’ in the above example) I can publish a MQTT message including the key and its active modifiers.

 for event in self.device.read_loop():
     if event.type == evdev.ecodes.EV_KEY:
         k = evdev.categorize(event)
         set_modifier(k.keycode, k.keystate)
         if not is_modifier(k.keycode) and not is_ignore(k.keycode):
             if k.keystate == 1:
                 msg = k.keycode + get_modifiers()
                 self.mqttclient.publish(self.topic, msg)

(The full code for the service can be found here.)

This results in MQTT messages of the form

IR/room2av KEY_VOLUMEUP
IR/room2av KEY_VOLUMEDOWN
IR/room2av KEY_LEFT
IR/room2av KEY_RIGHT
IR/room2av KEY_DOWN
IR/room2av KEY_UP
IR/room2av KEY_PAGEUP
IR/room2av KEY_PAGEDOWN
IR/room2av KEY_T_KEY_LEFTCTRL_KEY_LEFTSHIFT

The next step was to add a rule to the rules engine to handle these. The rules engine is a simple MQTT client that runs on the same Raspberry Pi as the MQTT broker; it listens to topics of interest and based on incoming messages and any relevant state (stored in Redis) publishes message(s) and updates state. In this case there is no state to worry about, it is simply a case of mapping incoming “IR/*” messages to outbound messages.

A (partial) example is:

function handle(topic, message, resources) {
  switch (topic) {
  case "IR/room2av":
    switch (message.toString()) {
    case "KEY_UP":
      resources.mqtt.publish("KODI/room2/KODI", "Action(Up)");
      break;
    case "KEY_DOWN":
      resources.mqtt.publish("KODI/room2/KODI", "Action(Down)");
      break;
    case "KEY_PAGEUP":
      resources.mqtt.publish("Light/room2/Lamp", "on");
      resources.mqtt.publish("Light/room2Ceiling", "on");
      break;
    case "KEY_PAGEDOWN":
      resources.mqtt.publish("Light/room2/Lamp", "off");
      resources.mqtt.publish("Light/room2Ceiling", "off");
      break;
    case "KEY_VOLUMEUP":
      resources.mqtt.publish("KODI/room2/TV", "VOL_p");
      break;
    case "KEY_VOLUMEDOWN":
      resources.mqtt.publish("KODI/room2/TV", "VOL+m");
      break;
...

Here we can see how buttons pushes from this one IR remote are routed to multiple devices:

  • the “up” and “down” navigation buttons result in messages being sent to Kodi (the message content is simply passed to Kodi as a “builtin” command via the xbmc.executebuiltin(…) API available to add-ons);
  • the “+” and “-” channel buttons (which map to PAGEUP and PAGEDOWN keycodes) have been abused to turn the lights on and off – note the two separate messages being sent, these actually end up going to LightwaveRF and Philips Hue devices respectively; and
  • the “+” and “-” volume buttons send IR commands to the TV (this happens to be via the Kodi add-on but is distinct from the Kodi control) – the “VOL_p” and “VOL+m” being the names of the IR codes in the TV’s LIRC config file.

A major gotcha here is that when controlling a device such as the TV with an IR blaster, there will be an overlap between the blast of IR from the Hama device and from the IR blaster connected to the Kodi Pi, and the TV will find it difficult to isolate the IR intended for it. To avoid this I’ve had to put tape over the TV’s IR receiver and IR blaster which is glued to it such that IR from the Hama control can’t get through.

The end result is that I can now use a single IR remote control to navigate and control Kodi, turn the TV on and off and adjust its volume, and control the lights in the room. Because everything is MQTT under the hood, and I’ve got plumbing to route messages pretty much anywhere I want, there is no reason why that IR remote control can’t do other things too. For example it could turn off all the lights in the entire house, or turn off a TV in another room (e.g. if I’ve forgotten to do so when I left that room), or even to cause an action externally via my Azure IoT gateway (more on this in a future blog post). And because the rules engine can use state and other inputs to decide what to do, the action of the IR remote control could even be “contextual”, doing different things depending on circumstances.

 

Hybrid local/cloud style home automation

When I first started automating items in my home I used a local (“on-prem” if you like) point solution for security lighting. This had a connection to an internet service (Google Calendar) but only in a “pull” manner, and only for that specific use-case. As time went on I wanted to enable a broader set of use-cases, some of which required other internet-connected services in both pull and push directions. I experimented with using Octoblu, with the local LightwaveRF controller being connected to it – all the automation therefore was being performed in the cloud. As I looked at adding additional devices (PIR detectors, reed switches, IR blasters, and so on) I encountered a number of failures and delays caused by this reliance on my domestic internet connection and the (at the time) patchy reliability of Octoblu (it’s far more solid now).

My solution was to use a “hybrid” model with a local message broker and set of services running inside the home, and a connection to Octoblu for cases when external services were involved. Overall this means that truly local operations, such as PIRs turning on lights depending on the position of various reed switches, can be kept within my home network with no reliance on my internet connection or any external service. In cases where external stimuli are used (e.g. for connecting Amazon Echo’s smart home stuff) or external actions are required (e.g. sending a text message using Twilio) the bridge between the two worlds allows a message to be forwarded in either direction.

The on-prem piece

homeautopiThe local system is based on a MQTT broker running on a Raspberry Pi. This provides a publish-subscribe messaging system that various services connect to. The broker and many of the services run on the same Raspberry Pi however the broker also listens on a TCP port to allow other Raspberry Pis (and potentially other devices) in the home to join the party. I also have a Redis key-value-pair (KVP) store which is used to store state such as presence information or whether it’s day or night.

As an illustration of how the system is used: the LightwaveRF radio-controlled lighting system is connected via a small node.js service that subscribes to lighting messages (e.g. “Light/Kitchen/Main=on“) and sends the appropriate command to the local LightwaveRF hub which then emits the 433MHz radio command. When I later added a Philips Hue system alongside the LightwaveRF one I created a similar node.js service that also subscribes to lighting messages and makes calls to the Hue hub’s API as necessary. In both cases the services ignore messages about lights they don’t control. This all means other services, such as the PIR detector, or the rules engine, can simply publish a lighting control message without worrying about whether it’s a LightwaveRF, Hue, or (perhaps in the future) other system managed locally or via a cloud service.

Other services that publish and/or subscribe to MQTT messages include:

  • A Bluetooth Low Energy (BLE) advertisement monitor – this is used to determine the presence of absence of various things
  • A Google calendar interface that can update local KVP state, or generate messages, based on scheduled events
  • A GPIO service that enables things like PIRs and push buttons to be attached to Raspberry Pis
  • A timer service that other services and rules can use to provide timeouts, e.g. to turn off PIR-controlled lights after a set delay
  • Plug-ins for the Kodi media player to control Kodi itself as well as IR emitters for nearby TVs and other devices (stay tuned for a blog post on this)

homeautoruleA core part of the whole system is the rules engine. This is a service that subscribes to MQTT messages, implements rules, and publishes MQTT messages as necessary. It doesn’t directly interface to any device. An example of a rule is managing lighting for the basement stairs: inputs to the rule are two PIRs (via the GPIO service), the value of the day/night KVP, and the timeout message from a named timer (from the timer service). If a PIR triggers and it is night (from the KVP) then a lighting message (“Light/Stairs/Basement=on“) is published; when the PIR stops detecting, a timer control message is published (“Timer/basement_stairs_lighting/reset=<time in 60s>“) to start the timer; receipt of the timer’s timeout message causes the rule to emit an “off” lighting message.

The Octoblu bridge

homeautodiagram

So, what if I want to control stuff in the home from somewhere else? Or if I need to send a message to a cloud-connected device or service? That’s where the Octoblu connector comes in. I built a basic connector that runs as a node.js service on the main MQTT Raspberry Pi. This subscribes to a subset of MQTT topics and has the ability to publish arbitrary messages into the on-prem system. It also acts as an Octoblu connector, maintaining a bidirectional connection into Octoblu. This allows my entire home automation world to appear as a thing in Octoblu so I can send messages to it, which leads to MQTT published messages, and receive messages from it, which came from MQTT subscribed messages.

As an example, I can remote-control a light using a button on a web page by connecting an Octoblu “trigger” (a thing that can respond to a HTTPS POST and then emit a message into the Octoblu platform) to the home automation thing, and ensure the message payload includes the right MQTT topic (e.g. “Light/Kitchen/Main“) and message (e.g. “on“). The connector routes this message into my home as a published MQTT message that the LightwaveRF or Hue service will act on. This is the core mechanism I used when connecting my home lighting up to Amazon Echo using an Alexa smart home skill adapter.

In the other direction an on-prem service, such as the rules engine, can send a message to Octoblu just by publishing to a suitable topic (currently this means it has a “Octoblu/” prefix) via the local MQTT broker. An example of this is a security feature that sends me a text message if a particular combination of stimuli are seen. The rules engine publishes a message “Octoblu/alert/<detail>=<status>“, the Octoblu connector receives this via its MQTT subscription and sends it into Octoblu. The Octoblu flow then decides how to process this message which may end up with a call to the Twilio thing to send a text message.

Other examples of using the Octoblu connection include:

  • Sending events for sunrise and sunset, based on pulling data from a weather API (saves having to drill walls to install a light sensor)
  • Routing messages from Alexa skills handlers (these run in the cloud as AWS Lambda functions so I need a way to route the message back across my firewall)

In closing

I’ve ended up with a hybrid-cloud style of IoT management for my home automation, not by design, but by evolution. I find that being able to combine local automation – with its low latency and lack of reliance on external connectivity and services, with a powerful cloud-based automation platform able to send and receive from a variety of cloud/internet services, is a best-of-both-worlds solution.

homeautooctoblu

Using LightwaveRF switches to control other stuff

img_9028I’ve been using LightwaveRF‘s range of wirelessly controlled lighting and power outlets for some time and I’ve built up a collection of switches and remote controls for them. These switches broadcast control messages, formed of a command (e.g. on/off) and a unique identifier for the transmitter, using a simple on-off keying modulation on 433.92MHz with each light fitting or other device being “paired” to one or more transmitters. In reality the pairing is actually just telling the device which command(s) to respond too – the transmitter units can’t receive anything which prevents a bidirectional pairing. This all runs in parallel with the LightwaveRF network bridge which is really just an IP-controlled 433.92Mhz transmitter.

With my home automation system now growing to include Philips Hue and various homemade devices I’d like the LightwaveRF switches and remote controls to be able to control more than just LightwaveRF devices which means taking them our of their constrainted world of 433.92MHz. To do this I am building a set of 433.92MHz receivers to forward the command messages to my MQTT broker. This means I can extend my existing set of automation rules to react to the LWRF switch commands by, for example, sending a command to the Hue Hub to control a Hue light. I can also build up more complex operations with other rules such as only performing certain actions outside of daylight hours, or controlling multiple devices at the same. This also allows for more interesting control of the LightwaveRF devices themselves because I can now insert rules between the transmitter and receiver simply by not pairing the device with the transmitter and instead routing everything through the MQTT world and the LightwaveRF network bridge.

img_9021The prototype is based on three main components:

  1. A 433.92MHz receiver – I’m using a cheap one from Amazon. This has a 17.3cm wire antenna soldered on to it.
  2. An Arduino Uno R3 to handle the demodulation of the on-off keying and the protocol decoding.
  3. An existing Raspberry Pi to forward the decoded messages from the Arduino to the MQTT broker.

Why the separate Arduino? That’s because I don’t then have to worry about responsiveness of the decoding software if it was to be running on a Raspberry Pi alongside other stuff – some of which may also be latency sensitive.

There is plenty of material online (see the resources below) about the LightwaveRF protocol. I found that none of the existing code (at least what I could find) worked in the way I wanted so I implemented a decoder from scratch using the various online protocol resources as guides.The decoder, running on the Arduino, is formed of three main pieces. Firstly a demodulator that looks for state changes on the data coming from the 433.92MHz receiver. This then feeds the demodulated bits into the main protocol decoder which looks for the message start bit, each byte’s start bit, and a valid pattern for the byte (which is a form of 4b8b encoding). The final piece kicks in once the transmission has ceased for 25ms: the buffered received data is de-duplicated and for each unique message a MQTT message is sent over the serial port (via USB).

The format of the MQTT message is topic=”LWRF433/<unitID>/<channel>”, message=”<command 0=on,1=off>|<level>”, where:

  • <unitID> is the unique 3 byte identifier for the switch unit
  • <channel> is the channel number, the meaning of which varies across the different type of switches
  • <command> and <level> tell the light what to do and vary depending on whether the controlling switch is a simple switch, dimmer, or mood switch.

lwrfmqtt

The initial prototype is connected to an existing Raspberry Pi running LibreELEC/Kodi as a media centre. The Arduino is connected to a USB port on this Pi. The function of the Pi is simply to forward MQTT messages received over the USB serial port from the Arduino to the house MQTT broker. To do this in the LibreELEC/Kodi environment is a little trickier than in a general Linux/Raspbian environment due to the locked-down, read-only nature of the LibreELEC distribution. However the Kodi add-on mechanism provides a reliable way to do this.

20161009_170439I created a Kodi add-on formed of two main parts. Firstly a Python script that acts as the main forwarding loop between the USB serial port and the MQTT broker. This is based on code from a previous, albeit standalone, proxy doing largely the same thing – it includes a device scanning function to watch for Arduino devices appearing and to fire up proxy threads for each one – this was originally done to avoid static configuration and to allow for hot-plugging of new Arduino devices (this was in a far more complex, multi-Arduino system, compared to this simple case).

The second part of the Kodi add-on is really just boilerplate and plumbing to kick off this script.

So putting it all together this is how it works:

  1. A button is pushed on a LightwaveRF switch, this sends a message (actually a burst of several) on 433.92MHz.
  2. The 433.92MHz receiver module receives these messages and passes the raw data to the Arduino.
  3. The software on the Arduino decodes the messages, de-duplicates and sends one or more MQTT messages over the USB serial port.
  4. The proxy script running on the Kodi Pi forwards the MQTT message(s) to the MQTT broker.
  5. Part of my MQTT rules engine is subscribed to LWRF433 messages and receives the message(s). After debouncing, the relevant rule is invoked, e.g. sending a separate MQTT message to control a Hue light.

 

The initial use-case is to use the LightwaveRF remote control to turn off the room lights, pause whatever the media centre is playing, and turn off the TV, all with a single button push. To do this I created a rule that on receipt of the appropriate LWRF command via MQTT sends out two more MQTT messages, one to turn off the TV and one to pause Kodi. Both of these are handled by a separate add-on running on the same Kodi Pi, but that piece of functionality is completely separate to the LWRF add-on described above and is the subject of a future blog. The room lights turn off directly in response to the LightwaveRF 433MHz command without any MQTT infrastructure being used.

lwrfrule

Resources:

Getting started with home automation

20160908_195003I’ve always had an interest in electronics and software, and in connecting the two together. I guess it started in my pre-teen years when I figured out I could use the cassette motor relay on my family’s BBC Micro to turn LEDs and buzzers on and off, and it grew as I learned to use the BBC’s 8 bit “user port” with more sophisticated electronics. I’ve used a variety of technologies and gadgets over the years, including various microcontrollers (PIC in particular) accessed via serial (RS232 or RS423), Elexol’s USBIO24 GPIO devices and, more recently, various Arduino and Raspberry Pi models. I’ve built a number of different things such as controls and displays for my flight simulator, computer control and feedback interfaces for my model railway, and miscellaneous gadgets of varying levels of utility. But until recently an area I’ve not explored is home automation – a topic well aligned with my experience and interests. That changed about 4 years ago when the problem of needing landlord permission to run cables and drill holes disappeared and I became a homeowner.

Like most people who venture into home automation my starting point (and mostly still the extent of my experience) was lighting. The automation of lights is both relatively straightforward and safe – there are no moving items under control of my software so no bug can lead to overheated motors or physical damage.

20160908_185431A decade ago I’d probably have started by buying a bunch of relays or triacs and working back from that (in fact I still have a device much like this that I fear to plug in) however I decided that it was time to leave the mains voltage to the professionals and concentrate on the low voltage stuff and the software. When I looked around for suitable hardware I was particularly impressed by the LightwaveRF range. The core of the range is a set of dimmers and relays in various form-factors including inline (which for the light switch types mean they need a leakage current through the lights to power them – something I’m not keen on) and plug-in units. All the dimmers and relays are controlled by 433MHz RF, either locally from handheld remotes or wall-mounted buttons, or via the network using an Ethernet to RF bridge (called a “Wi-Fi Link” despite having no Wi-Fi capability!).

20160908_185332The “Wi-Fi link” is designed to be used with LightwaveRF’s cloud service to enable control of the lights from mobile apps or via REST APIs. However, it can also be used directly by sending suitably formatted UDP packets to it.

When I first started using LightwaveRF my priority was to use the system to replace timer-based security lights simulating occupation while away from home. I initially started with Paul Clarke’s excellent “lightwaverf” tool which can drive the Wi-Fi Link from either command line invocations or from events in a Google calendar. I ran this tool on an early-model Raspberry Pi sitting in the cellar. I coupled this with an IFTTT recipe that runs daily and populates a dedicated Google calendar with a sunset event which was the basis for triggering the switching on of some of house lights.

20160908_185404This was all fine until Google withdrew the ability to share a calendar privately as an XML download. This was the motivation to build a more flexible system that would enable more use-cases for automatic light control (these will be the subject of future blogs). I looked at using Octoblu, an IoT automation platform acquired by Citrix. It didn’t have support for LightwaveRF but I did end up building a simple gateway to allow me to use the two technologies together. More importantly it was, at the time, not particularly stable (it’s much better now) so I ended up building a local MQTT-based system with a bunch of individual services connected via the MQTT broker (all running on the same Raspberry Pi as the previous system). One of these services is the LightwaveRF service which subscribes to messages about light controls and sends the appropriate UDP commands to the LightwaveRF Wi-Fi Link device (turns out there’s a NPM library for this). Another service is the calendar service which uses the Google calendar APIs to regularly pull down the same automation events as I was using with the previous system, but this time using my Google account and an API key, thus avoiding the problem of the missing private XML calendar. This service then issues lighting control command via messages published to the MQTT broker. By adopting the same calendar format as used previously I was able to continue to use the IFTTT recipe to populate the sunset events and enable/disable the security lights based on my travel plans

I’ve since added more services to the MQTT environment enabling more lighting automation use-cases – keep an eye out for more blog posts on these in the future.