This project aims to create a modified mesh network of small sensors, placed in water sources across developing areas, in order to effectively monitor the water quality in the region.
Firstly I'm going to talk about the sensors themselves.
These are small low power devices located at a water source. The electrode is submerged allowing the device to monitor water purity. This is carried out using a current sense circuit that measures the conductance of the water. The raw data is captured and sent over long range Wi-Fi connection to the next node, finally reaching the monitoring station. Readings are taken at regular intervals set through the RTC, allowing the node to be in deep sleep when not in use. This should allow for a long battery life. The block diagram can be seen below.
The SoC in question is the ESP8266. I have decided to choose this device for 3 main reasons:
- No MCU - The SoC can run custom LUA scripts that will utilise the on-board GPIO and ADC to read the sensor data
- Range - With line of sight the module was tested to just under 5km using a large enough antenna, a standard duck antenna yields a range of around 500m, either way this is not a SoC dependant factor.
- Cost - This SoC is low cost, at around $2 per system.
- Support - This module is rather 'hot' right now and has its own forum and site dedicated to supporting it, more than most.
Now moving on to the network itself, I have produced a graphic outlining the operation.
I think the diagram speaks for itself really. Obviously in remote Africa an internet connection isn't exactly common, however employing this method of networking, we can utilise a chain of point to point connections to transmit data over large distances, with all information ending up at a central monitoring station, whereby an internet connection can be established.
This will allow us to effectively monitor large remote areas for water purity and analyse data over long periods of time. All processing will be done by the monitoring station, the sensor will just transmit it's raw data. Obviously it will be linked to the cloud allowing multiple stations to contribute to a wider overall picture of the water quality in the region.
Thanks for reading, please leave any comments below!