Through our Internet of Things business, we help local authorities monitor the health of their sewer systems and address maintenance issues before they occur.

Monitoring systems are available for vacuum, pressure and gravity sewer networks. Our solutions in production include the largest vacuum sewer monitoring system in Australia and New Zealand.

Our monitoring systems are based on low-energy, low-cost, single-use devices that we design, manufacture and install throughout a sewer network. 

Each device:

  • connects to a central database through a low-power wide-area network built on the industry-leading LoRaWAN protocol
  • sends an alert to the database if it detects any unusual activity
  • lasts approximately ten years on a single battery
  • is secure, using the AES-128 encryption standard for data transmission.

Finding and fixing faults before they occur

  • Each type of sewer network has a range of faults which, if not addressed quickly, can have significant impacts on communities, including sewage leaking onto client premises and community areas. Our monitoring systems help to identify these faults ahead of time.
  • For vacuum sewers, we monitor the opening and closing of valves between chambers. A single valve fault can result in a critical loss of vacuum for the whole network.
  • For pressure sewers, we monitor correct operation of pumps. Failure of a pump to switch off when the water level is low, or excessive strain on pumps during heavy rain, can make pumps burn out and the network break down.
  • For gravity sewers, we monitor water levels using a dual float system. Anomalies in water levels can indicate that the network is struggling to cope with heavy rainfall or other unexpected demands.

Whatever the sewer type, faults have traditionally been extremely difficult to diagnose and rectify. Often, a fault at one point in the network can trigger a chain reaction where the problem is manifested far from its root cause.

By combining full coverage with real-time data, our sewer monitoring solutions make guesswork a thing of the past. We know where and when a fault occurs so maintenance crews can get to it quickly. Now, we're going even further: with [predictive modelling], we can use machine learning to predict faults before they happen.