An IoT hass farm for supervisory, control & data acquisition systems.
This project intends to combine cheap CPUs and open source microservices to develop an architecture for distributed supervisory and control systems.
The SCADA architecture has functional levels layered as a pyramid that have been used to evolve solutions for home/office/industrial automation. Since the '70s, the SCADA concept has pioneered the ability to perform supervisory operations over a variety of other proprietary devices.
As shown in the diagram, the lowest level interfaces with the process, maybe a home/office/factory for example. The higher levels communicate to gather information from lower levels, analyze the data and evaluate the decisions to be made, eventually sending down commands to adjust the process behavior.
Today, IoT solutions face similar challenges concerning the development of home automation systems. Usually, a computer solution may grow adding more CPU power to one single computer. This option is commonly restricted by proprietary technology eventually associated to limited vendors. Another option is to grow the system as a distributed architecture, based on microservices running on several CPUs, tied through APIs.
The proposed architecture intends to inspire starters for distributed IoT architectures based on Raspberry Pi hardware and Home Assistant (HA) software. The system is installed on a Linux environment with Docker containers launching several microservices on each node. Then, different types of IoT devices are designed to cooperate as a single system. Although each node has its own configuration, they also share common Home Assistant files.
The picture describes a typical process that pumps water from well and fills a big water storage placed on a high tower. The plumbing connects to water points located on the near buildings, equipped with secondary water tanks that are filled by gravity and should also be monitored.
In order to accomplish the job, different types of IoT devices, or nodes, should cooperate together as a team. The process infrastructure is expected to provide AC power source and LAN to connect the nodes. Each node is expected to have its own Home Assistant configuration, automation and user interface scripts. Nodes also share common Home Assistant files, like customization, groups and secrets. A couple questions arise when we start designing the system, concerning how many wires are necessary to bring each node to life:
- How big should be the CPU for each node?
- How each node connects to power and communication?
The table below lists the components used at the Water Process: tank, super and pump.
| Node | CPU | Power/LAN | Extra | Photo |
|---|---|---|---|---|
| _tank | RPi Zero-W with Wifi. | AC/Wifi | Samba |  |
| _tank2 | RPi 3B+ with TP Ethernet RJ-45. | AC/TP | Samba, InfluxDB, Grafana |  |
| _super | RPi 2B with TP Ethernet RJ-45 | AC/TP | Samba, MQTT |  |
| _pump | RPi 3B+ with PoE TP Ethernet RJ-45 also provides power | PoE | Samba, A/D converter ads1x15 |  |
Since several tanks may exist in a wide area, the proposed distributed architecture dedicates a node to get data from each tank, including water level, temperature, humidity, and luminance.
The picture shows the level sensor located at the top of the tank, pointing down towards water. The distance to the water allows calculating the water level and consequently the volume stored in the tank. The sonar technology has been frequently used for this task, the HC-SR04 is a popular example. In this project, a different option replaces sound with light.
The component chosen here is the STMicroelectronics VL53L1X "time of flight sensor", equipped with an invisible laser to measure distances with millimeter resolution. Please check the full specs on the product page and the available breakout shown below.
The tanks nodes themselves are not enough to calculate the total water in the system since they only handle local information about each tank. As shown in the diagram below, a second level is created for a supervisor node that collects information from all tanks.
MQTT streaming generated at each tank node publishes the variables that should be monitored. A preliminary user interface is located at the tank supervisor, showing information from all tanks. This system may be easily scaled for many tanks and is smart enough to handle simple decisions.
The Raspberry Pi Zero-W is a cheap and powerful choice to measure the water level and other environmental data from water tanks. The source code from an available Python 2 library should be upgraded to Python 3 in order to create a new "Time of Flight" platform for Home Assistant.
A Raspberry Pi 2B powers the supervisor node. For this project, in order to make comparisons with tank data, the supervisor also has sensors to collect the ambient temperature, humidity, and luminance.
This node gets info from pumps to inform how much water is being moved to the tanks. We know that a lot of electromagnetic interference exists near pumps, and definitely this is not healthful to computer CPUs. In order to keep node connections far from this wild environment, power and communication will be provided by the Power over Ethernet technology. A RPI PoE HAT providing 5V DC/2.5A from the RJ-45 network port (PoE IEEE 802.3af) will be used to power the Raspberry 3B+ CPU.
A hall-effect sensor generates an analog voltage proportional to each pump power current. A analog to digital converter will keep track of analog values in order to evaluate the pumps performance.
The running containers at tank and supervisor nodes are shown below, using the docker ps with an option for pretty-printing the command output.
The tank node has three microservices running: the Hass.io supervisor, the Homeassistant software and the Samba add-on that allows network access to configuration files.
$ ssh pi@cast
pi@cast's password:
Linux cast 4.14.71+ #1145 Fri Sep 21 15:06:38 BST 2018 armv6l
...
Last login: Fri Mar 29 22:21:27 2019 from 192.168.20.113
pi@cast:~ $ docker ps --format "table {{.ID}}\t{{.Image}}\t{{.Status}}"
CONTAINER ID IMAGE STATUS
d2ff42ec7739 homeassistant/armhf-hassio-supervisor Up 2 hours
22ef93868dbf homeassistant/raspberrypi-homeassistant Up 3 days
7f761d6a8dbd homeassistant/armhf-addon-samba Up 3 days
The Hass.io setup for tank node is shown below:
The supervisor node has four microservices running: the Hass.io supervisor, the Homeassistant software, the MQTT server, and the Samba add-on that allows network access to configuration files.
$ ssh pi@lava
pi@lava's password:
Linux lava 4.14.71-v7+ #1145 SMP Fri Sep 21 15:38:35 BST 2018 armv7l
...
Last login: Tue Mar 26 22:08:38 2019 from 192.168.20.113
pi@lava:~ $ docker ps --format "table {{.ID}}\t{{.Image}}\t{{.Status}}"
CONTAINER ID IMAGE STATUS
4f058070fb47 homeassistant/raspberrypi3-homeassistant Up 3 days
bff95bb5169c homeassistant/armhf-addon-samba Up 4 days
ce8ab55c04ef homeassistant/armv7-addon-mosquitto Up 4 days
d3ae6c4ceeff homeassistant/armhf-hassio-supervisor Up 4 days
The Hass.io setup for supervisor node is shown below:
The supervisor panel shown below contains water level and environmental data collected from a single tank. The delta temperature is calculated for each tank, based on the difference between data collected from it and the "normal" temperature registered by supervisor sensors. This way, an alarm for unexpected temperature in the tank may be raised, demonstrating the typical behavior from higher levels nodes in the SCADA architecture.
Further details about this project are published separately for each node. Please check articles in the following order:
Note: This issue is already solved, please update to the latest version (HA 0.96.5 or greater).
After updating HA to 0.95.4, the BH1750 and HTU21D sensors failed to install. Looks like ha container is trying to build wheels on the fly but gcc support is missing. In this same system works fine a Time of Flight VL53L1X laser distance detector that also uses I2C. Rolling back to HA version 0.92.2 everything works fine. Follow these instructions to change version. More details in the closed issue at Homeassistant repo.
Have fun with your IoT initiative!
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