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Alerts about forest fires shouldn't depend on pets smelling smoke. We need smart infrastructure, and that needs zero-power sensors
The Windy Fire, shown here near California Hot Springs, Calif., on 27 September 2021, was first spotted on 9 September and burned through some 100,000 acres (40,000 hectares), including parts of the Sequoia National Forest. A sensor network [superimposed] might have provided an earlier warning.
Why is the dog barking, you wonder, as you wake up? You notice the smell of smoke, and when you try to turn on your bedside light you discover that the power is out. Then you see it out your window: a wall of orange flame, crawling up a nearby hillside. You roust your family and run to the car. Your lives have just been saved by a Stone Age warning system: your dog.
This has been the experience of hundreds of Californians. In the case of the 2017 Tubbs Fire, the 2018 Camp Fire, and the 2020 August Complex Fires, high winds blasted flames through populated areas in the early morning hours while residents were sleeping. Too many did not make it out of their beds, let alone their homes. In our always-on, sensor-laden, Internet-connected world, shouldn't technology have done better?
Technology was on duty miles downwind, where, as the battle against these fires went on for days, even weeks, many residents of the Bay Area and Sacramento River Delta region turned to air-quality sensor networks, particularly AirNow, maintained by the U.S. government, and PurpleAir, created via crowd-sourcing of commercial sensors. The data from these two broad sensor networks helped residents decide whether to wear an N95 particle mask when going outside, whether it was safe to exercise or to let children play outdoors, how long to keep the air filters running inside the house, and how far to drive to escape.
These particular networks use sensor units mounted on buildings to stream data via Wi-Fi to Web-based mapping programs. Just a few hundred sensor units distributed over the larger Bay Area were enough to identify significant local differences in the spread of smoke. For instance, the sensors showed that the topography of the Santa Cruz Mountains protected downwind coastal towns from smoke, while the Sacramento River Delta suffered far more as smoke stagnated in its wide, low areas.
It's great that sensors tracked smoke in these areas. But why weren't they on the job where they were really needed, where these wildfires started, to issue an alert before the fires spread?
The main reason is access to power. Sensors that mount on buildings can just plug into a wall outlet. A sensor system that could detect a fire started in a forest does not have that luxury.
Could it use batteries instead, at least one per sensor node?
A resident of Vacaville, Calif., was one of many Northern Californians forced to flee the LNU Lightning Complex fires in August 2020, after an unusual series of thunderstorms sparked nearly 400 blazes.Philip Pacheco/Bloomberg/Getty Images
Pause for a moment to look at the smoke detector in the room where you're sitting and think about the last time you changed its battery. Kind of a pain, wasn't it? A sensor network that could monitor an entire forest, or a gas pipeline, or any critical infrastructure, would need thousands or even millions of sensors—and batteries. Just thinking about the crew of people needed to tramp around to change all those batteries is exhausting, and to actually do it would be prohibitively expensive and impractical.
If we had a sensor network that rarely—or never—drew power, imagine how many important places and things could be monitored, how many lives could be saved. Consider bridges and dams that could report on their structural integrity. Or think about city streets that could report storm flooding, or downed power lines that could identify the exact location of the break and possible risk of fire.
Before we talk about how we might create such a zero-power monitoring system, let's review the basic components of a distributed sensor network. Besides the power source and the sensors themselves, each node in the network requires a computer (in the form of a microprocessor or a microcontroller chip) and a radio. Typically, the computer is in control: It accumulates sensor data at specific intervals and processes the data. Then it turns on the radio to transmit the data. If the power source is limited in capacity, such as a battery, or in availability, such as a solar panel, the computer also monitors and manages power consumption.
When we talk about managing power consumption here, we usually focus on the power used by the radio. A radio can be very power hungry; the farther a radio signal needs to reach, the more power it must draw.
For those PurpleAir and the other building-mounted sensors mentioned, the radio signal needs to reach just several meters, to a base station, potentially using a low-energy radio protocol like Bluetooth Low Energy or Zigbee, or to an Internet router using Wi-Fi. Out in the forest, though, that's not the case. Even with mesh networking—a protocol that allows messages to be passed in short hops from node to node on the way back to home base—a large-area network might require each node to transmit over kilometers. To reach such long distances, each radio could need watts, versus only the milliwatts of power available in Bluetooth Low Energy.
One way to conserve power is by programming the computer to sample and transmit on fixed time intervals, say once per hour. Or it might continuously monitor the sensor's output data and transmit data only when something interesting happens, such as when a prescribed sensor threshold level has been exceeded. But in either case the computer must always be running, which means it will eventually drain the battery.
The ideal sensor warning system, like that pet dog guarding a home at night, would normally remain asleep; however, a certain threshold of noise or smell will cause it to wake up and start barking a warning.
A much better way to conserve battery power would be to use none of it at all until the system actually had important data to transmit. The system would remain in an ultralow-power sleep mode, or even an open-circuit mode, with no current flowing, until the sensor itself detected an important signal.
In this vision, the sensor is in control, not the computer. The sensor would trigger the computer to power up, process the data, and transmit it. And then, with transmission complete and the triggering stimulus gone, the system would shut down and return to a sleep or fully powered-off state. Sleep mode, or something close to it, already appears in virtually every modern IC—particularly those intended for use in mobile devices, where conserving battery life is critical.
The ideal sensor warning system, like that pet dog guarding a home at night, would normally remain asleep; however, a certain threshold of noise or smell will cause it to wake up and start barking a warning.
The sensor equivalent of a sleeping dog is called an event-driven sensor. In its most common form, it uses an incoming stimulus, at some minimum threshold value, to move and close a mechanical switch, which in turn activates an electronic circuit. Once the switch closes, the circuit draws power from the battery and then performs more power-intensive duties like data processing and radio transmission.
Using microelectromechanical systems (MEMS) technology, we can make such event-driven sensors on silicon chips that are only millimeters in size. Tiny forces can actuate them and thus power electronic circuits embedded within the silicon.
At Northeastern University, in Boston, Matteo Rinaldi's group has demonstrated an event-driven sensor that could help detect a forest fire by reacting to the infrared light emitted from a hot object. On its surface, the sensor has an array of nanoscale metal squares that selectively absorb light from specific wavelengths, causing the sensor to heat up. At a predetermined temperature threshold, the absorbed heat will deform a metal finger that mechanically closes an electrical switch. The mechanism is similar to that used in older home thermostats, albeit at a much smaller scale. Once the stimulus is removed, the metal finger reverts to its original shape and the switch opens.
This sensor from Northeastern University researcher Matteo Rinaldi sleeps in an ultralow-power mode until infrared light, like that from a fire or hot object, wakes it up. A warning system using this type of sensor could go a decade without a battery change.Matthew Modoono/Northeastern University
By changing the geometry of the absorber and the mechanical switch, you could customize this sensor to respond to different wavelengths and light intensities. It could therefore be used in a sensor network to watch for the heat signature created by a forest fire, or in a security application to look for the hot exhaust from a certain vehicle type passing by. During its inactive state, it draws nearly zero power, having a leakage current of only nanoamperes. This sensor could last for years on its original battery while waiting for a triggering event.
At the University of Texas at Dallas, Siavash Pourkamali's group has taken a different approach. They developed an event-driven DC accelerometer that can detect change in tilt. This could be used as a security device, to set off an alarm if an object is moved, or as a package shipping monitor, to determine if a package is upended during transport. Deployed in a sensor network, it could also detect small angle changes in large structures, such as fences, pipelines, roadways, or bridges, indicating potentially troublesome deformation or cracking.
The idea behind this motion event-triggered sensor isn't new. A hundred years ago, centimeter-scale tilt switches used a conductive blob of mercury rolling along a glass tube to close an electric circuit. The MEMS version, of course, is only a few millimeters in size, and instead of mercury, it uses a suspended block of silicon. When the angle changes, the displaced block closes an electrical circuit. This sensor can be customized to designated tilt thresholds, and it consumes no power while waiting for the triggering motion.
Both of these event-driven sensors still require a battery to power up the rest of the system after a triggering event occurs. The awakened computer must then process the sensor data and begin radio transmission according to its programmed instructions.
With parsimonious use, the battery could last for years, but at some point it will run out. The ultimate dream, therefore, would be to have no batteries at all.
As impossible as that may sound, battery-free sensors already exist. We can create them by using a commonplace technology: radio frequency identification. An RFID tag can be a passive electronic device, with no power source of its own. Instead, it draws power inductively from an external device, called a reader. The reader emits electromagnetic energy across a distance, which couples to the RFID tag's antenna and generates a transient electric current within the RFID tag's circuit. This temporary coupling of the reader and tag enables small bits of information to be transmitted, such as a serial number or an account balance. A typical use of RFID in this manner is electronic toll collection; the passive RFID tag resides on the car's windshield, and the car drives under a reader mounted to an overhead gantry.
Getting to zero-power sensors is well worth the effort and expense; deploying them to warn of wildfires would alone justify the R&D investment.
RFID technology can be used to return a sensor reading, instead of just a tag number. Indeed, it has already been used for years in implanted medical sensors, such as the CardioMEMS system. In that system, a glass-based MEMS capacitive pressure sensor within an aortic aneurysm stent allows a cardiologist to check for stent leakage by placing a reader against the patient's torso.
But there's a lot more that can be done with RFID-style powering and readout.
At Tsinghua University, in Beijing, Zheng You's group developed an acoustic-wave sensor that can passively detect temperature change with precision. This device relies on the fact that the center frequency of a piezoelectric structure shifts with variations in temperature, and small frequency shifts can be easily detected by the RFID reader's circuitry.
With the addition of a chemically selective absorbing coating to the piezoelectric surface, the sensor could measure the concentration of a gas. As the coating absorbs the target gas molecules, the mass resting on the piezoelectric material would increase, again shifting the resonant frequency.
Any sensor that can convert a physical phenomenon into a change in resonant frequency could be read by RFID and therefore operated without a battery. In this case, the challenge involves getting the reader close enough to each and every sensor in the network. It's hard to imagine doing this for a forest-fire detection system. Putting a larger antenna on the sensor, as well as on the reader, would certainly help, but even in the best case we're looking at a few meters, as in electronic tollbooths.
Still, with a transmission range on the order of meters, a large-area sensor network composed of battery-free, passive sensors could be read using a drone, flying in a pattern over the network to gather the data. Eric Yeatman's group at Imperial College London has been developing the hardware platform needed for such drone-based data collection. Drones would navigate to each sensor-node location, power up the node, then collect data. To provide ample power, the sensor network incorporates supercapacitors that charge up via inductive wireless power transfer. Drones would work best for sensor networks having clear air space, for example, those on farms, aqueducts, pipelines, bridges, or dams.
In November 2018, the Camp Fire, burning in California's Butte County, sent thick clouds of smoke [top] into the San Francisco Bay area, where a network of sensors monitored by PurpleAir identified dangerous levels of airborne particulates [bottom]. The fire ultimately covered more than 150,000 acres (60,000 hectares), destroying 18,000 structures and claiming at least 85 lives.Top: David Little/The Mercury News/Getty Images; Bottom: PurpleAir
A large-area sensor network would have been very useful in managing the Oroville Dam in California in February 2017, when a controlled release of excess rainwater caused the dam's spillway to fail. The resulting cascade of water eroded the dam's foundation, potentially compromising the dam's integrity. Local authorities ordered more than 180,000 nearby residents to leave until more detailed inspections could determine that the dam was safe. Had a large-area structural-monitoring sensor network been in place at the time, those authorities could have gathered data to determine the state of the dam and make a timely and informed decision on whether evacuation would truly be needed. (Ultimately, the feared collapse did not occur.)
Likewise, the 2018 Morandi bridge collapse in Genoa, Italy, was caused by a combination of aging infrastructure and severe weather. The disaster, which resulted in 43 deaths, might have been prevented if the weakening of the span could have been detected in good time by an installed sensor network, instead of by sporadic and sparse inspections.
Are event-driven or zero-power sensors ready to detect the outbreak of a wildfire in a remote area? We're not quite there yet, but we are getting closer. All the essential pieces of such a large-area sensor network exist in various states of technical maturity; several more years of development and product integration will bring them to reality. Perhaps the more difficult challenge will be to motivate regional and federal governments to purchase and deploy such networks where they can be most useful or to enable a crowd-sourced sensor network, similar to PurpleAir.
Getting to zero-power sensors is well worth the effort and expense; deploying them to warn of wildfires would alone justify the R&D investment. Wildfires have already caused such huge losses and continue to threaten lives, property, habitat, and the long-term health of the millions breathing in smoke.
Imagine a future fire season in California. A lightning strike sets a tree ablaze, far from any houses, and the fire grows. But long before even a faint smell of smoke can wake your dog, the sensors in the forest wake up and alert a fire-monitoring station. At last, there is enough time and information to model the development of the fire, and to issue early evacuation warnings to the phones of everyone in the fire's path.
What would be wrong with Satelite sensing IR over a large area, then sending info to a central alert system. A second path might be placing IR detectors on Cell towers, high trees, or mountains. Power could be centralized or connected to a source. Even batteries would be manageable. The challenge would be to produce high resolution wide area coverage long distance IR detectors or cameras. Seems like a simpler methd
Made in bulk for the first time, this new carbon allotrope is the semiconductor graphene isn't
Prachi Patel is a freelance journalist based in Pittsburgh. She writes about energy, biotechnology, materials science, nanotechnology, and computing.
Researchers have found a way to make graphyne, a long-theoreized carbon material, in bulk quantities. Like its cousin graphene, graphyne is a single layer of carbon atoms but arranged differently.
Since graphene’s discovery 18 years ago—leading to a Nobel Prize in Physics in 2010—the versatile material has been investigated for hundreds of applications. These include strong composite materials, high-capacity battery electrodes, transparent conductive coatings for displays and solar cells, supersmall and ultrafast transistors, and printable electronics.
While graphene is finding its way into sports equipment and car tires for its mechanical strength, though, its highly touted electronic applications have been slower to materialize. One reason is that bulk graphene is not a semiconductor. To make it semiconductive, which is crucial for transistors, it must be produced in the form of nanoribbons with the right dimensional ratios.
There’s another one-dimensional form of carbon related to graphene that scientists first predicted back in 1987, that is a semiconductor without needing to be cut into certain shapes and sizes. But this material, graphyne, has proven nearly impossible to make in more than microscopic quantities.
Now, researchers at the University of Colorado in Boulder have reported a method to produce graphyne in bulk. “By using our method we can make bulk powder samples,” says Wei Zhang, a professor of chemistry at University of Colorado Boulder. “We find multilayer sheets of graphyne made of 20 to 30 layers. We are pretty confident we can use different exfoliation methods to gather a few layers or even a single layer.”
Graphite, diamond, fullerenes, and graphene are all carbon allotropes, and their diverse properties arise from the combination and arrangement of multiple types of bonds between their carbon atoms. So while the 3D cubic lattice of carbon atoms in diamond make it exceptionally hard, graphene’s single layer of carbon atoms in a hexagonal lattice make it extremely conductive.
Graphyne is similar to graphene in that it’s an atom-thick sheet of carbon atoms. But instead of a hexagonal lattice, it can take on different structures of spaced-apart rings connected via triple bonds between carbon atoms.
The material’s unique conducting, semiconducting, and optical properties could make it even more exciting for electronic applications than graphene. Graphyne's intrinsic electron mobility could, in theory, be 50 percent higher than graphene. In some graphynes, electrons can be conducted only in one direction. And the material has other exciting properties such as ion mobility, which is important for battery electrodes.
Zhang, Yingjie Zhao of Qingdao University of Science and Technology, in China, and their colleagues made graphyne using a method called alkyne metathesis. This is a catalyst-triggered organic reaction in which chemical bonds between carbon atoms in hydrocarbon molecules can crack open and reform to reach a more stable structure.
The process is complicated and slow. But it produces enough graphyne for scientists to be able to study the material’s properties in depth and evaluate its uses for potential applications. “It will take at least a couple years to have some fundamental understanding of the material,” says Zhang. “Then it will be in good shape for people to take it to a higher level, which is targeting specific semiconducting or battery applications.”
He and his colleagues plan to investigate ways to produce the material in much larger quantities. Being able to use solution-based chemical reactions would be critical for making graphyne at industrially relevant scales, he says.
It’s just the beginning for graphyne though, and for now, just being able to make this long-hypothesized material in sufficient quantities is an exciting first step. “Fullerenes were discovered in the 1980s, then nanotubes in the early '90s, then graphene in 2004,” Zhang says. “From discovery of a new carbon allotrope to its intensive study to first application, the timeline is becoming shorter. I’m already receiving calls from venture capitalists around the world. But I tell them it’s a little bit early.”
It’s a lot of progress over just one year
One year ago, we wrote about some “high-tech” warehouse robots from Amazon that appeared to be anything but. It was confusing, honestly, to see not just hardware that looked dated but concepts about how robots should work in warehouses that seemed dated as well. Obviously we’d expected a company like Amazon to be at the forefront of developing robotic technology to make their fulfillment centers safer and more efficient. So it’s a bit of a relief that Amazon has just announced several new robotics projects that rely on sophisticated autonomy to do useful, valuable warehouse tasks.
The highlight of the announcement is Proteus, which is like one of Amazon’s Kiva shelf-transporting robots that’s smart enough (and safe enough) to transition from a highly structured environment to a moderately structured environment, an enormous challenge for any mobile robot.
I assume that moving these GoCarts around is a significant task within Amazon’s warehouse, because last year, one of the robots that Amazon introduced (and that we were most skeptical of) was designed to do exactly that. It was called Scooter, and it was this massive mobile system that required manual loading and could move only a few carts to the same place at the same time, which seemed like a super weird approach for Amazon, as I explained at the time:
From what I can make out from the limited information available, Proteus shows that Amazon is not, in fact,behind the curve with autonomous mobile robots (AMRs) and has actually been doing what makes sense all along, while for some reason occasionally showing us videos of other robots like Scooter and Bert in order to (I guess?) keep their actually useful platforms secret.
Anyway, Proteus looks to be a combination of one of Amazon’s newer Kiva mobile bases, along with the sensing and intelligence that allow AMRs to operate in semi structured warehouse environments alongside moderately trained humans. Its autonomy seems to be enabled by a combination of stereo-vision sensors and several planar lidars at the front and sides, a good combination for both safety and effective indoor localization in environments with a bunch of reliably static features.
I’m particularly impressed with the emphasis on human-robot interaction with Proteus, which often seems to be a secondary concern for robots designed for work in industry. The “eyes” are expressive in a minimalist sort of way, and while the front of the robot is very functional in appearance, the arrangement of the sensors and light bar also manages to give it a sort of endearingly serious face. That green light that the robot projects in front of itself also seems to be designed for human interaction—I haven’t seen any sensors that use light like that, but it seems like an effective way of letting a human know that the robot is active and moving. Overall, I think it’s cute, although very much not in a “let’s try to make this robot look cute” way, which is good.
What we’re not seeing with Proteus is all of the software infrastructure required to make it work effectively. Don’t get me wrong—making this hardware cost effective and reliable enough that Amazon can scale to however many robots it wants to scale to (likely a frighteningly large number) is a huge achievement. But there’s also all that fleet-management stuff that gets much more complicated once you have robots autonomously moving things around an active warehouse full of fragile humans who need to be both collaborated with and avoided.
Proteus is certainly the star of the show here, but Amazon did also introduce a couple of new robotic systems. One is Cardinal:
The video of Cardinal looks to be a rendering, so I'm not going to spend too much time on it.
There’s also a new system for transferring pods from containers to adorable little container-hauling robots, designed to minimize the number of times that humans have to reach up or down or sideways:
It’s amazing to look at this kind of thing and realize the amount of effort that Amazon is putting in to maximize the efficiency of absolutely everything surrounding the (so far) very hard-to-replace humans in their fulfillment centers. There’s still nothing that can do a better job than our combination of eyes, brains, and hands when it comes to rapidly and reliably picking random things out of things and putting them into other things, but the sooner Amazon can solve that problem, the sooner the humans that those eyes and brains and hands belong to will be able to direct their attention to more creative and fulfilling tasks. Or that’s the idea, anyway.
Amazon says it expects Proteus to start off moving carts around in specific areas, with the hope that it’ll eventually automate cart movements in its warehouses as much as possible. And Cardinal is still in prototype form, but Amazon hopes that it’ll be deployed in fulfillment centers by next year.
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