How IoT’s Buddy could help solve Flint’s water crisis

IoT company Buddy have a goal, to use IoT for the greater good. They’ve created a water quality demo that showcases how Buddy integrates with distributed devices and sensors as part of an initiative they are setting up called IoT for Impact.

The result is quality, real-time data, that could be used to preemptively alert health officials of system abnormalities – like those that impacted Flint, Michigan, recently – that could then be addressed immediately.

I spoke to CEO and co-founder David McLauchlan, about the initiative. He explained:

To some, IoT data is seen as superfluous.  Events with disastrous impact are surfacing all around us and coming to a head. The contaminated drinking water in Flint, poisoned water from fracking in New York and Pennsylvania, continued e. coli and listeria break outs in Massachusetts, or air quality issues in Oregon — these problems could have been predicted or in some way contained if IoT technology had been implemented.

If sensors were placed in common water filters used in homes, the Flint water crisis could have been discovered and remediated. This could extend to solve other environmental problems, for example:

If air quality sensors were as inexpensive and easy to install as LED garden lights, how many homeowners would be willing to place them in their yard and contribute to an early warning system for abnormal levels of pollution? And if micro-thermometers could be packaged with meat and produce from the time of harvest to the time of consumption, how quickly would noxious foods and their origins be identified and mitigated?

McLauchlan believes there’s a real opportunity for IoT data to bring value to communities by predicting these events. He wants to start a conversation that will engage and ideally move other IoT and data solution providers into taking action. Fundamentally, he believes that people and positive social impact should be at the center of IoT value creation.

The Buddy demo uses water turbidity as a proxy for the overall water quality.  A variety of other water quality characteristics could be measured using this same design.  In their demo, data is captured from a turbidity sensor and displayed real-time on a dashboard.  If the turbidity level crosses a certain threshold (for example, 100 NTU), the sensor alerts by changing the color of smart lightbulbs in two separate locations.

The demo can be broken down into the following key steps:

1. Turbidity sensor is placed in water sample
2. Data is collected, processed, and streamed real-time to Splunk
3. As the sensor detects key thresholds, light bulbs change color indicating a threshold was met
4. Data is compared against a data-set of water quality data provided by USGS