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Inno2: Cloud-Based Wastewater Monitoring System

Oct 2020 to Jul 2021

Azure-hosted pipeline for NEA COVID-19 wastewater surveillance. 100+ Tastek industrial RTUs with SIM card slots, streaming directly over cellular to a single Azure VM running Mosquitto, the database, and the dashboard. Twilio WhatsApp alerts to the on-call engineers. Contributed to the Lee Hsien Loong IDM Smart Nation Award 2022. Straits Times feature.

01

Context

In 2020 the National Environment Agency rolled out wastewater surveillance as a community-level COVID-19 early warning system. Inno2 (the W2 Industrial Services Hub initiative I worked on) built the cloud and edge stack that powered the deployment.

We built it from scratch. By the time I rotated off the project, the platform was running across roughly 50 sites (migrant worker dormitories, residential blocks, and treatment facilities), with engineers visiting site cabinets daily to verify uptime. Deployment kept scaling past 360 sites after I left.

02

My role

Cloud and IoT engineer plus team lead on a five-person team. Joined as an SP-attached intern and stayed on as a W2 IoT Solutions consultant. Owned the Azure-side architecture, the integration between field RTUs, the broker, and the operations dashboard, and drove the team's day-to-day direction on what to build next.

03

Architecture

  • 100+ Tastek industrial RTUs in the field. Each unit had a SIM card slot, so the RTU dialed straight out to the cloud over cellular.
  • One Azure VM running everything: the Mosquitto MQTT broker, the database, and the web dashboard.
  • When load grew, we resized the VM to a larger SKU. Each resize required a brief stop and restart.
  • Twilio WhatsApp Business API for alerts. Engineers registered their numbers once, then offline-RTU and threshold-breach notifications landed on WhatsApp.
04

Outcomes

  • Contributed to the Lee Hsien Loong IDM Smart Nation Award 2022 received by W2.
  • Featured by The Straits Times when NEA announced the doubling of surveillance sites.
  • Stayed in regular production use, with the alerting flow catching dropouts before site visits.

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