Statistics show the frequency of floods and extreme rainfall events around the globe has increased by more than 50% this decade, and this figure is only growing. As residents of Brisbane, Australia we experience major floods regularly, the most recent of which in 2011 claimed 35 lives throughout our state. We have seen first-hand the dangers they cause, and how frequently people take uneducated risks that can lead to severe injury or even death. A recent survey found 1 in 3 people admitted to attempting to cross moving floodwaters without knowledge of its depth or speed.

We see here an opportunity to provide people with information on the flood status of their local waterways, alerting them to potential hazards, and deterring them from taking life-threatening risks. Our proposition: an IoT sensor network capable of measuring the water level, flow rate, and turbidity at points along local waterways. Aside from providing the public with access to this data via an app, the device detects when a flood event occurs and warns people in the local area.


Our IoT device “The FloodLight” uses a LASER-based sensor system to monitor the flood status of local waterways from a distance. Mounted on the river bank up to 5 meters above the water level, the FloodLight fires a series of red laser pulses diagonally (40deg) downwards into the water. The majority of the laser’s intensity is reflected away by the surface, but some is refracted into the water where it diffuses. Some of this scattered light is returned to the FloodLight, where it is measured by a photodiode. From analysing the laser light’s behaviour the water depth, turbidity, and flow rate of the water can be determined.

For the device to be fully functional in all lighting conditions, it is empirical that only the light from the laser is measured by the photodiode. This is achieved by a 16mm collimating lens which narrows the photodiode’s field of view to 5 degrees, overlaying this with a red colour filter and linear polariser to prevent unwanted surface reflections. To further eliminate ambient light, the laser is toggled at 1750 Hz using PWM, and the received signal (amplified) sent through an analogue bandpass filter that eliminates all lower frequencies, hence other light sources. The low pass aspect of the bandpass filter also reduces noise in the measurements.

The signal is then read by the ADC for digital analysis. From the intensity of light received per pulse (11 measurements, 40kHz sampling), averaged over each pulse cycle (17 pulses, 0.01s total duration), the turbidity of the water can be measured. From repeating this measurement every 10 seconds for a minute and measuring the variation in this data, the surface disturbance (indicating flow rate) can be measured. Water level is determined by Time of Flight (ToF), measuring the receive time with a Discriminator and interrupt pin. Measurements are taken hourly and transmitted via the Sigfox Wide Area Network, but if a flood event is detected, measurement frequency is increased to every 10 minutes.

The use of lasers for these measurements is not new technology. This is to our advantage as it has been previously tried, tested, and proven to work accurately. The FloodLight design was inspired by a remote water turbidity laser measurement used by aerial drones, but has been adapted to be able to also simultaneously measure water level and flow and fit the needs of an IoT flood monitoring system. This is our innovation, and to our knowledge the FloodLight is the first device of its kind.


We are currently in the process of developing prototypes to validate our design. Although this remains a work in progress, preliminary results have been promising.

During this phase the Thinxtra Xkit is used as the core of the design. A 5mW laser pointer has been dismantled and rigged to the PWM, and photodiode tested, but the details of the signal processing circuitry are still being developed. A 3D printed casing will be used to protect the components and correctly orientate and house the laser and lens/filter system.

The primary costs, excluding the development kit, are the lens/filter/polariser system. The accuracy of the data measured relies on the ability of this combination to filter out unwanted light, and the current set being tested uses high quality versions which add a significant cost to the overall device. The manufacture of alternatives is currently being investigated to find a more suitable compromise.

The final product however will look different. Instead of the development kit, a microcontroller will be used, the Sigfox compatibility will need to be added separately, a larger battery will be used, and it will likely be enclosed in a higher quality injection molded casing which meets environmental protection requirements.


In conclusion, we believe that the FloodLight IoT Sensor Network offers a novel, innovative, and most importantly viable method to monitor the flooding of local waterways and disseminate useful information and warnings to the general public to increase awareness of the dangers of floods and prevent injury and loss of life.


In our home city of Brisbane, Australia we have experienced the effects of flooding first-hand. In the 2011 floods that devastated Brisbane and surrounding areas, countless people were badly injured in the floodwaters, homes were washed away entirely, over a billion dollars’ worth of damage was sustained, and most tragically 35 people lost their lives. For a country that experiences severe flooding events regularly, there is still a large portion of the population that do not understand the extreme dangers posed by flooded waterways. Furthermore, there is a lack of accessible information for residents regarding flood severity, and the safety of their local waterways. Such information has the potential to save lives. This is always something that has concerned us, having watched the destructive effects of flooding not only in our own city, but around the globe. After learning about the Keysight IoT Innovation Challenge we saw this as an opportunity, as student engineers, to use our skills to develop an IoT solution that could have real world positive impacts for those living around us. A motivation for both of us when choosing to study engineering was the opportunity to develop skills that would allow us to create devices that could help people and tackle some of the biggest challenges our world is facing today. Through the development of our LASER-based flood monitoring network, we hope to live up to this dream; creating technology that can potentially save lives.


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