Zizmos Continues Its Quest to Create an IoT Earthquake-Warning Network

Earthquake-warning startup Zizmos has been surviving on contest prizes and reality TV opportunities. Next up, Kickstarter

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A simulation of Zizmos' earthquake early warning system shows the progression of a temblor in the San Francisco Bay Area
Photo: Zizmos

Update 20 September 2017:

A few smartphone users in the Mexico City area were running the Zizmos app, described below, when this week’s magnitude-7.1 earthquake struck, Zizmos founder Battalgazi Yildirim reports, but not enough to issue a warning, although Zizmos did register the shaking.

Yildirim says he’d like to be able to get 50 fixed sensors installed in Mexico City—enough to reliably give warnings of aftershocks. The design, however, is still at the prototype stage, so each costs about $500 to build. He only has 10 on hand to donate, and would need funding to produce 40 more and local volunteers to install them.

Meanwhile, since the Mexico City earthquake, he says, another 5,000 smartphone users around the world have started running the app.

I first met Battalgazi Yildirim two years ago. He had posted a request in my local online community: His startup, Zizmos, wanted volunteers willing to mount a sensor package inside their homes, preferably on a bearing wall, to test whether a network of cheap packages of electronics, based on the Android phone design and his algorithms, could give early warnings of earthquakes. He wasn’t looking to do long-term prediction, just 15 or 30 seconds—enough to allow people to grab their kids and move to the safest spot in their house.

Yildirim funded that first trial—which eventually involved 100 sensors—with an NSF research grant of $150,000. Like many first design attempts, it didn’t work out so well. It turned out, Yildirim told me last week, that the Android platform had a fatal flaw—it couldn’t pull in data from an external sensor and simultaneously recharge. The alpha testers might have been willing to deal with keeping the gadgets charged, but this approach wasn’t going to appeal to the masses. And Yildirim’s idea is going to need mass adoption to work; it relies on large numbers of low-cost sensors that report possible earthquake vibrations to the cloud, then eliminates false alarms by comparing the data with neighboring sensors.

The good news, however, Yildirim said, was that the internal sensors on phones were getting better and better—maybe, he thought, he and cofounder Greg Stillman could just design an app instead of dedicated hardware. He entered the Verizon Powerful Answers competition and his proposal won a grand prize--$1 million. The award also came with a lot of help, formal and informal, he says, from Verizon’s business team.

With enough money to carry Zizmos for a while, Yildirim hired contractors to work on apps for Android and iPhone and also went back to his original idea of producing a wall-mounted Internet of Things gadget. “The system works better,” he says, “if we gather data from a fixed device for which we have full control of the hardware and software.”

The free app is out and has had nearly 100,000 downloads so far. It works as an earthquake sensor only when it is plugged into power, is connected to WiFi, and is stationary, say, sitting on a bedside table.

“It’s mostly looking at frequency,” Yildirim says. “When an earthquake originates some distance away, the high frequencies quickly die off; instead you are just getting low frequency vibrations. If a truck passes by or you bump the table the phone is sitting on, you get a broader range of frequencies.”

If the app triggers on a possible earthquake, it sends its data to the cloud. The cloud software knows the locations of nearby sensors. If enough are in the same neighborhood, it analyzes data from all of them. “If it is an earthquake wave, multiple sensors will trigger in sequence,” Yildirim said.

The system is currently using the data for research only. It isn’t sending out actual warnings to users. “We are detecting earthquakes,” Yildirim said, particularly on a small network in Northern California. But the users worldwide are currently distributed too sparsely to be of much use.

In the meantime, the app has some utility for those who have downloaded it: It gives earthquake reports based on data from government agencies around the world and allows users to customize those alerts for specific geographic areas, both local, and where friends and family live.

Next out of the gate, Yildirim says, will be that IoT package. He’s got 25 prototypes installed and tested, and Zizmos hardware engineering lead Mark Johnston is working on the industrial design for the final product, which Yildirim says will be small and sleek enough that it wouldn’t look out of place sitting next to a Nest thermostat on the wall. Zizmos plans on launching a Kickstarter campaign in a month or two in hopes of generating enough preorders to guarantee that particular geographies, like California or Oklahoma, will have critical mass—before shipping. He estimates that, for California, that will mean at least 6,000 sensors installed around the state.

Meanwhile, more prize money funding is trickling in—Zizmos scored additional prize money last month (but has to keep quiet until a formal announcement is made). Yildirim is hopeful that some additional grant applications will bring in funding, and says his idea has gotten some traction from reality TV producers. The company was scheduled to be featured on Planet of the Apps but the episode was preempted and Yildirim has heard no rescheduling news. He was also a finalist on America’s Greatest Maker, with $1 million at stake, but the show has been cancelled.

“Competitions like this product,” he says, “and they don’t require giving up equity. But you can’t run a company forever on prize money.”

The long-term plan is turning Zizmos into a venture-funded company that turns a profit—not from purchasers of the hardware, but from structural engineers and insurance companies interested in how certain types of buildings respond to earthquakes in different areas. They would find value in the data, using it to provide more granular risk assessments. 

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