Smartphones, Earthquakes, Stealth Cars & Sniper Attacks
By Richard A. Lovett
Shortly before starting this column, I discovered the pedometer app on my smartphone. I’d never noticed before, because I’d been using a GPS-based app whose accuracy I’d been grumbling about to an acquaintance, who had been advising me to switch to the pedometer app. “It’s been collecting data on you all along, even if you didn’t know it,” he said.
I opened it, and to my amazement, he was right. All the way back to 2016, when I’d bought the phone, it had hour-by-hour data on how many steps I’d walked, the distance I’d covered, and how many fights of stairs I’d climbed.
Two years ago, I’d had a hip replacement.
Now, I could look back at my progress through rehab, when I’d been under strict instructions about how much walking I should do in order to hit the sweet spot for ideal healing. At the time, I’d tried measuring my rehab walks with the GPS app. But at the pace I was going, it had tended to shut off and decide I was immobile: a good description of how I’d felt, but not useful for my efforts to find that sweet spot. But here was this new app, which I’d had all along, showing me exactly what I’d done, every hour of every day. Collecting all the data, even though I’d had no clue it was doing so.
It was fun . . . and disconcerting.
“Big Brother is watching you,” George Orwell wrote in 1984. But in 1949, when Orwell published his dystopia, Big Brother did his watching through the then-newfangled invention of television. And, however prophetic he was on other things, Orwell missed it on this one. Big Brother isn’t in our living rooms, watching us on our TVs on Skype. He’s in our pockets, in a device that has the ability to monitor us day-by-day, hour-by-hour, step-by-step, whether we know it or not.
A lot has been written about the dangers of government agencies and corporations collecting such data for nefarious purposes. But this is Analog, not the Journal of Hopeless Dystopian Futures. So let’s set aside the evil-government warnings for the moment and look at the positive things that have resulted from this technology, including ones far more important than my ability to reexamine how well I managed to comply with my surgeon’s recommendations for rehabilitating my hip.
High on the list of such positives is scientific research.
The first example of this I ever saw involved earthquakes . . . and Twitter.
Tweets aren’t private unless you deliberately protect them. Anyone can see what you tweet, and if they have a big enough computer, they can tabulate what the entire Twitter world is saying, in real time.
There are a lot of potential applications of this, but a decade or so ago, a U.S. Geological Survey seismologist named Paul Earle realized that when an earthquake happens, people tend to tweet about it, quickly. Quickly enough that in some parts of the world, their tweets can beat the arrival of seismic waves to the nearest seismometers.
Monitoring the Twittersphere, he realized, could also help emergency managers organize disaster relief in the aftermath of earthquakes and other catastrophes, simply by revealing the regions where people are tweeting out key words indicating they’re in need of help. Big Brotherish? Maybe. Lifesaving? Very likely.
More recently, urban planners used a similar approach to deal a blow to the image of ride-hailing services such as Uber and Lyft as solutions to traffic congestion.1 They found that at least in San Francisco, these companies (technically known transportation network companies, or TNCs) have become major contributors to urban gridlock. “[In 2016] about 15% of vehicle trips in San Francisco were on TNCs,” says Gregory Erhardt, a transportation engineer at the University of Kentucky, Lexington.
“Initially, there was a lot of enthusiasm because here was this great, new, affordable, fast-mobility option,” adds Joe Castiglione of the San Francisco County Transportation Authority. But now, “people [have] the perception that congestion has gotten much worse, and there [are] TNCs everywhere.”
To test this perception, Castiglione and Erhardt started by turning to smartphones, or more precisely, the type of things smartphones are designed to do. They developed a program that pinged Uber and Lyft every second for about six weeks, tallying how many cars were on the streets looking for rides at any given time, and where they were—exactly as potential customers do every time they’re seeking rides. “What that showed,” Castiglione says, “is that TNCs definitely concentrate in the most congested parts of [the city] and that most trips are happening at the most congested times of day.”
To figure out how that affected traffic, they then turned more directly to smartphones (and their vehicular counterparts, GPS-based navigation systems), seeking to determine how much time people spent driving each of the city’s streets, both before 2010 (prior to the advent of ride-hailing services) and in 2016. Their conclusion: more than 60% of the city’s increase in gridlock was due to TNCs.
Why TNCs have this effect is complex. In San Francisco, at least, they appear to be providing primarily short rides, thereby competing with bus trips, bicycling, and even walking. The result is more cars on the road, not less. Also, there is no place for TNC drivers to park between rides. That forces them to cruise around in a process known as deadheading, in which, according to Elliot Martin, a research and development engineer at the University of California, Berkeley, it’s as though each of these vehicles is “storing itself on the road while waiting for its next ride.”
What to do about this is also complex. Perhaps all that’s needed is to give TNCs a legal place to park between rides. Or maybe the solution involves the imposition of congestion fees on cars of all types—a prospect many see as the wave of the future, whether we like it or not.
More interesting for this column is the manner in which this data was obtained. On its surface, it sounds like a massive invasion of privacy. But researchers see it more like the “psychohistory” of Isaac Asimov’s Foundation series, in which the things that matter aren’t so much the actions of specific individuals as their collective behavior. “For us, [individuals] are like particles in a gas, that move and interact with each other,” Chaoming Song, a computational social scientist at the University of Miami, Florida, told me a few years ago.
That said, there are situations in which individuals very much matter—ones in which our pocket Big Brothers can be harnessed not to spy on us, but to protect us.
One example comes from military research, where scientists and engineers are closing in on using soldiers’ smartphones to detect the location of an enemy sniper from a single shot. The basic methodology dates back to World War I, when William Lawrence Bragg (who’d previously won the 1915 Nobel Prize in Physics for his work in X-ray diffraction) developed a technique called “sound ranging,” in which the difference in arrival times of sound waves at two different locations could be used to pinpoint the location of enemy artillery.
Bragg’s method, however, relied on microphones separated by as much as two kilometers, and required several minutes to process results. The new method, presented at a 2019 meeting of the Acoustical Society of America (ASA), uses microphones built into individual soldiers’ tactical headsets and the computing power of their smartphones to determine the range and direction of a sniper attack, and does it within half a second.
“At the beginning of an ambush, the most important thing is to know where the shooting is coming from,” says Sébastien Hengy, a combat acoustics researcher at the French-German Research Institute of Saint-Louis, Saint-Louis, France. “And they need this information very fast.”
The soldiers’ headsets, called Tactical
Communication and Protective Systems (TCAPS), are designed both to keep soldiers in communication with each other and in acoustical touch with their environment, while also protecting their hearing from the noise of their own gunfire. To do this, they include microphones, positioned near each ear.
When a sniper shot passes nearby, Hengy says, it produces two types of sound waves. The first, generated by the projectile, is a supersonic shockwave that fans out from the bullet as it passes. The second is the muzzle wave, generated by the explosion of powder in the barrel of the gun that fired it. The difference in arrival time between these two sounds is a measure of the distance from which the shot was fired. The difference in time between when the supersonic shockwave is detected by the right-ear microphone and the left-ear microphone is a clue to the direction from which the shot originated. Compasses in the soldiers’ headsets refine the process by revealing the orientation of the soldier’s head at the time of the shot.
All of this data is sent to the smartphone, whose processor is more than sophisticated enough to handle the calculations. The answer is then relayed back to the soldier in terms of a warning sound designed to instantly direct his or her attention in the proper direction. It’s even possible to combine data from several soldiers’ headsets to produce an even more accurate calculation. “The more sensors you have, the better the performance will be,” Hengy says.
Nor are soldiers the only ones who can benefit from such technologies. In the U.S. and Europe, regulators are starting to require ultra-quiet electric and hybrid vehicles to emit warning sounds so they won’t sneak up on pedestrians, especially those who are blind or vision impaired. These warning sounds must be emitted whenever the vehicles are moving too slowly to generate enough tire noise to otherwise be easily audible, generally at speeds below twenty to thirty kilometers per hour.
It sounds like a good idea, but if dozens of cars are doing this at the same time, on a major urban street, the result won’t be warnings, it will be cacophony. Especially if the vehicles aren’t all using the same warning sounds. “If you mix tones, you get disharmony,” says Klaus Genuit, founder of HEAD acoustics GmbH, a German company that provides sound and vibration analysis to the automobile industry (among other clients). Using musical ensembles as an analogy, he adds, “if you have one violin, it may be good, but if you have two violins, they must be tuned together.” Also, he says, warning sounds must be designed with an eye (or ear) to their purpose, which isn’t simply to make noise, but to encourage people to take heed, when needed. “An overload of warning signals reduces their effectiveness,” he says.
All of this has Rene Weinandy, head of Noise Abatement in Transportation for the German Environment Agency, wondering if these types of warning signals are even necessary. Noise, he says, is an environmental poison, known to cause health risks, ranging from high blood pressure and coronary artery disease to depression, and learning disabilities in children.2 “Is it really wise to increase the noticeability of electric vehicles by making them spray poison?” he asked at the ASA meeting.
Perhaps, he and Genuit suggested, these cars could generate radio signals that could be sent to handicapped pedestrians in the danger zone—signals that could be detected by their smartphones, delivering the alert directly to those who need it, and only to those who want it.3 These pedestrians’ phones could also carry apps designed to broadcast warnings back to cars, letting them know there’s a vulnerable person nearby.
Will all of this work? Who knows? Can the type of data being generated by such systems be abused? For sure.
* * *
My phone knows how many steps I take per day. It probably knows where I was when I took them. When I drive, it knows where I parked my car, and guesses my destination and gives me a traffic report each time I get in the car, based on the time of day and my travel history. I’m enough of a creature of habit that it’s usually right. That’s all a little freaky.
But it also tells me that yesterday I walked the third-longest distance since my hip replacement. It shows that I really was meticulous about trying to find that sweet spot in my rehab routine.
A few weeks ago, a friend asked if I could feel a difference between the surgical hip and the other one. “No,” I said, startled. I’d assumed that that was the way it normally was.
“Your surgeon was good,” my friend said.
I’d always known that. But the trove of data from the Big Brother in my pocket also says I worked hard to be the world’s best patient.
So, I wonder, if someone was collecting data on the walking patterns of people my age, would I really care if my data went into their database? For that matter, would I really feel that my privacy was being invaded if my phone suddenly screamed “RUN! YOU’RE ABOUT TO BE HIT BY A CAR!”
The threats of Orwellian dystopia are real. But so are the benefits of the new technologies. Maybe the true task for those of us in science fiction is to look for ways to balance the one against the other.
1 Do transportation network companies decrease or increase congestion?
Gregory D. Erhardt et al, Science Advances, Vol. 5, no. 5, eaau2670 (08 May 2019), DOI: 10.1126/sciadv.aau2670.
2 How traffic noise may contribute to heart disease. David Railton, Medical News Today, 12 February 2018.
3 Signal-detecting devices could also be built into these people’s canes, walkers, or wheelchairs, eliminating the need to have a smartphone available and always turned on.
Copyright © 2019 Richard A. Lovett