3 days ago
Watercare uses edge intelligence for smarter sewer monitoring in Auckland
Watercare is deploying 5,000 sewer level monitors across its Auckland wastewater network, utilising edge intelligence to detect issues such as blockages and pipe failures directly at the source.
This initiative, carried out in partnership with Kallipr, moves beyond conventional systems which rely largely on cloud-based processing. Instead, the monitors process data locally, enabling real-time responses to potential events and reducing reliance on external communication infrastructure.
Edge intelligence
Dave Moore, Smart Systems Manager at Watercare, described the importance of the localised approach. "What's unique here is where the intelligence lives. By shifting processing out to the edge, we're cutting down on cloud consumption and enabling the network to respond faster, closer to the source of the issue in near real time. It means less lag, less noise, and far more confidence in how we manage events as they unfold."
The local data processing is intended to lower communication overheads, extend the lifespan of monitoring solutions, and identify network issues earlier, reducing the risk of sewage spills and their associated environmental and financial costs.
Beyond monitoring
The sensors provided by Kallipr form part of Watercare's wider Smart Network Programme, valued at NZD $12 million. This programme focuses on integrating digital technologies and artificial intelligence into existing water infrastructure.
The devices are being deployed city-wide, including in regions prone to flooding such as Wairau. Each sensor, installed beneath manhole lids, autonomously monitors sewer conditions, adapting its sampling frequency when it detects anomalies via radar and TinyML-powered logic. The systems' long-life batteries further support extended deployment periods without frequent maintenance.
Gerhard Loots, Chief Executive Officer of Kallipr, outlined the broader implications for the utility sector. "Many utilities are still focused on collecting more data but the next leap forward is creating intelligence in the field. This deployment shows how utilities can evolve from data-heavy, cloud-reliant systems to a model that's faster, leaner, and built for real-world complexity. Watercare is leading with a future-ready approach that others can follow."
Technology details
The core of the deployment is Kallipr's Spectra radar sensor, which is designed to withstand the challenging conditions found in sewers, including exposure to corrosive gases, submersion, potential animal interference, and unreliable network signals. Spectra records data at regular 15-minute intervals, but can increase its activity if local sensors anticipate overflow or blockage situations.
These devices use low-power LTE networks - including NB-IoT and Cat-M1 standards - to transmit information, while onboard TinyML algorithms support local adaptation and ongoing battery efficiency. Connectivity challenges in underground settings are mitigated by dual SIM capability, with the devices using edge logic to choose the strongest available network. This ensures stable, continuous communication regardless of external conditions.
Device management is centralised through Kallipr Kloud Fleet, a platform built on Microsoft Azure, which allows remote monitoring and control of the distributed monitoring units.
Wider impact
Edge-based processing reduces the amount of bandwidth required, energy consumption, and load on cloud systems, helping to trim operational expenses and enhance environmental outcomes. According to Watercare, each avoided spill mitigates contamination risks, minimises unnecessary travel to field sites, and prevents excess emissions.
The deployment of this network in Auckland is reflective of a global move toward decentralised utility management. Watercare's approach demonstrates that efficiency and responsiveness can be improved by locating decision-making as close as possible to operational events. This strategy is intended to drive cost efficiency, resilience, and the long-term adaptability of critical infrastructure networks.
