The city offered up public infrastructure to a private company, SIGFOX, in October in order to establish a network dedicated to IoT solutions. Officials in the city Department of Technology were quick to point out at the time that the city didn’t have any specific uses in mind for the network, but wanted to foster the creativity of its tech-centric environs to find beneficial applications.
So the city hosted a 48-hour hackathon from Nov. 20-22 in an effort to develop ideas. On Nov. 23, the department announced the winners:
- Audio Argus, which aims to use sound sensors to gather noise that can then be used in predictive analytics.
- WaterSaver and DryWater, two solutions meant to save water in the drought-stricken state by using sensors to determine when sprinklers should be turned on instead of running them on fixed schedules.
- Better Bike, a concept that revolves around putting GPS trackers on bicycles to analyze patterns of movement.
The projects are all in early stages, he said, but could offer broad benefits to the city in the future. For instance, Audio Argus might be useful for detecting when certain machines like fleet vehicles and medical devices are close to breaking down.
“With audio sensors, maybe you can predict mechanical breakdown because those things make certain sounds and sound signatures,” Gamiño said.
Better Bike might also one day lead to better-informed city planning. San Francisco is aiming for zero traffic deaths by 2024, so Gamiño said efforts like Better Bike would be poised to contribute to safer urban design.
“That could be used for all sorts of different things,” he said. “It might influence how traffic patterns are designed for bikes to increase safety.”
Applications for the city’s IoT network don’t necessarily have to be for municipal purposes, he said, noting that they could also run for commercial uses. The SIGFOX network also might not be the only network the city uses for IoT solutions.
Ultimately, he said, the future is wide open.
“We are very much at a frontier,” he said. “What I’ve often said is that it’d be like trying to predict the ultimate killer app of the Internet 30 years ago. Thirty years ago you wouldn’t have been able to predict Airbnb or Uber, or else you’d be $50 billion [richer], right?”