# The Future of Cybersecurity Is the Quantum Random Number Generator

## Truly random numbers will provide an unbreakable tool set for cryptography

Illustration: Greg Mably

In 1882, a banker in Sacramento, Calif.,named Frank Miller developed an absolutely unbreakable encryption method. Nearly 140 years later, cryptographers have yet to come up with something better.

Miller had learned about cryptography while serving as a military investigator during the U.S. Civil War. Sometime later, he grew interested in telegraphy and especially the challenge of preventing fraud by wire—a problem that was frustrating many bankers at the time. As a contemporary, Robert Slater, the secretary of the French Atlantic Telegraph Co., wrote in his 1870 book Telegraphic Code, to Ensure Secresy [sic] in the Transmission of Telegrams, “Nothing then is easier for a dishonest cable operator than the commission of a fraud of gigantic extent.”

In his own book on telegraphic code, published in 1882, Miller proposed encrypting messages by shifting each letter in the message by a random number of places, resulting in a string of gibberish. For example, to encode the word HELP, you might shift the H by 5 so that it became an M, the E by 3 so that it became an H, the L by 2 so that it became an N, and the P by 4 so that it became a T. Even a meddlesome cable operator wouldn’t know what to make of MHNT unless he also had the list of random numbers, 5-3-2-4. For truly unbreakable encryption, each string of random numbers would encode only one message before being discarded.

About 35 years after Miller’s book, Bell Labs engineer Gilbert S. Vernam and U.S. Army Capt. Joseph Mauborgne came out with essentially the same idea, which they called the one-time pad. And ever since, cryptographers have tried to devise a way to generate and distribute the unique and truly random numbers that the technique requires. That, it turns out, is incredibly hard to do.

So instead, we’ve relied on less secure encryption methods, with the consequence that attackers who are sufficiently patient and knowledgeable can now crack into any encrypted data they want. And compared with Miller’s day, today we have more ways of connecting than the telegraph—through Internet of Things devices, wearable tech, and blockchain-dependent services, to name just a few—and they all need strong encryption. According to the 2017 “Cyber Incident & Breach Trends Report” [PDF] by the Online Trust Alliance, more than 150,000 businesses and government institutions were the victims of cybercrime last year. In just one of those attacks, on the consumer credit reporting company Equifax, hackers culled the personal information of nearly 148 million customers. “Surprising no one, 2017 marked another ‘worst year ever’ in personal data breaches and cyber incidents around the world,” the report concluded.

Fortunately, researchers have made good progress in recent years in developing technologies that can generate and distribute truly random numbers. By measuring the unpredictable attributes of subatomic particles, these devices can use the rules of quantum mechanics to encrypt messages. And that means we’re finally getting close to solving one of cryptography’s biggest puzzles and realizing the unbreakable encryption envisioned by Miller so many years ago.

You can’t beat one-time pads for security, if you use truly random numbers to shift the letters. Unfortunately, most one-time pads today use algorithms to generate pseudorandom numbers, like this example, which used numbers generated by Google.Illustration: Erik Vrielink

As any cryptographer knows, you need three ingredients to make a hackproof encryption method. First, you need an algorithm that converts your message into a string of meaningless characters. Second, you need a way to produce random numbers. And finally, you need the means to deliver the first two ingredients to the intended recipient without anyone else gaining access.

You cannot protect a message with the first ingredient alone, no matter how good the algorithm is. An encrypted message will be completely exposed to anyone who knows the algorithm used to secure it. That’s why we combine the algorithm with random numbers. Despite its relatively simple algorithm, the one-time pad becomes unbreakable with the addition of random numbers. To recover the original message, you need to know the specific sequence of random numbers the algorithm used to encrypt the message. Those random numbers are a cryptographic key, which unlocks the content of the encrypted message, but it’s useless for deciphering other messages, just as your house key opens your front door but not your neighbor’s. Your encryption system is thus only as strong as your cryptographic key is unpredictable.

Unfortunately, most sources of random numbers aren’t truly random. These pseudorandom-number generators use algorithms to produce sequences of numbers that look random. But again, if you know the underlying algorithm, they become completely predictable.

We can also generate random numbers by measuring physical processes, like flipping a coin or the interference of radio communications on an electric current. One problem with this approach is that if the process is bound by the laws of classical physics, the measurements can be predicted. To be sure, it may take some doing to reverse engineer what’s being measured, but a cryptographer has to assume that somebody will eventually find a way to do so.

Many physical random number sources are also slow. One common method is to record the coordinates of mouse clicks or movements on a computer screen. KeePass, an open-source password manager, uses mouse jiggles to generate a master password. Think how much random clicking or jiggling it would entail just to encrypt every email you wanted to send.

What’s needed, then, is a source of true randomness that is fast enough and that any device can use. That’s where quantum mechanics comes in.

By their nature, subatomic particles like electrons and photons behave in ways that can’t be predicted. If you take two photons emitted by the same atom at different times but under the same conditions, they may exhibit different behaviors, and there’s no way to predict those behaviors ahead of time. That’s not to say any behavior is possible, but of the outcomes that are possible, we can’t predict which one we’ll get. That unpredictability is crucial for developing a random number generator.

### One-Way Functions

The most common example of a one-way function is the multiplication of two large prime numbers (typically thousands of digits long). Any computer can multiply two large primes in the blink of an eye, but even for the fastest, it’s very slow going to reverse the process, taking the answer and checking all the possible options until it finds the two initial numbers.Illustration: Erik Vrielink

In the 1990s, a team at the U.K. Ministry of Defence became the first to propose a way to use quantum mechanics for random number generation. Today, you can buy commercial quantum random number generators from companies like QuintessenceLabs and ID Quantique. QuintessenceLabs’ generators are based on quantum tunneling, which occurs when subatomic particles spontaneously pass through a barrier that according to classical physics they shouldn’t be able to cross. The ID Quantique generator tracks the distribution of individual photons as they hit a detector.

All of the available commercial generators are limited to specialized applications, such as encrypting classified military data or financial transactions. They’re much too large, or too slow, or too expensive for mass market use. Imagine instead having a tiny quantum random number generator installed in your phone, your laptop, or anything else that needs to communicate securely. Creating such cheap, compact, and quick quantum systems has been the focus of our group’s research at the Institute of Photonic Sciences, or ICFO, in Barcelona, for the past eight years.

One of the most promising approaches is based on a type of semiconductor laser called a distributed feedback laser diode. We start by oscillating the laser diode above and below its threshold level—that is, the energy level at which the stimulated emission of photons starts. For our laser diodes, the threshold is about 10 milliamperes. Each time the laser exceeds its threshold level, the laser will emit photons with a random phase, which means that the photons will be at an unpredictable point along their wavelength. Those random phases become the basis for the random numbers we use to generate a cryptographic key.

We’ve already built several devices that have helped confirm the “spooky action at a distance” principle in quantum mechanics, which is the idea that entangled particles can interact with one another instantaneously regardless of distance. Specifically, our devices provided an observer-independent method of verifying that the spooky action could occur, which is important when it comes to proving that the instantaneous interaction is actually occurring. We built those devices using fiber optic cable, and each was about the size of a shoebox. Now, using standard chip-fabrication techniques, we’ve integrated the components for our quantum random number source onto an indium phosphide chip measuring less than 2 by 5 millimeters. This chip can be installed directly into a phone or an IoT sensor.

### RSA Algorithms

Quside Technologies, a company spun off from our institute last year, is commercializing components using our technology. (One of us, Abellán, is now Quside’s CEO.) Quside’s latest generation of quantum sources can produce several gigabits of random numbers per second, which means one source should be enough for any current or emerging encryption need. And because they can be made using standard chip-fabrication techniques, it should be easy to manufacture them in large volumes.

What’s more, our chips are immune to nearby electronic interference. Generally speaking, any electronic device may be susceptible to thermal or electronic interference. White noise, for example, can interfere with the reception of radio signals. Quantum sources, being so tiny, are especially susceptible, so in most cases, their designers need to pay close attention to eliminate any effects that might corrupt the pure, inherent randomness from the quantum process. Our solution neatly avoids this problem simply because a photon’s phase is largely unaffected by electrical currents in the vicinity.

Another good quantum source for random numbers is light-emitting diodes. In 2015, researchers at the Vienna University of Technology demonstrated the first such compact random number generator. It consists of a silicon-based LED that emits photons in the near infrared and a single-photon detector. Its random number generation was linked to when the photons arrive at the detector. The lab prototype generated random numbers at a rate of a few megabits per second.

Illustration: Greg Mably

A year later, our group in Barcelona demonstrated the chip-based quantum source we mentioned before, that is capable of producing gigabits of random numbers per second using distributed feedback lasers. As a bonus, our sources are built from off-the-shelf components and rely on standard optical communication and manufacturing techniques.

Meanwhile, researchers at SK Telecom [PDF], one of the largest telecom providers in South Korea, have demonstrated a random number generator chip that uses a smartphone camera to detect the fluctuations in an LED’s light intensity. The design was based on a patent from ID Quantique. The prototype, unveiled in 2016, measured 5 by 5 mm; since then SK Telecom has announced plans for a commercial version that’s about the same size—that is, small enough to fit inside your smartphone.

Other researchers are investigating quantum random number generators based on single-photon detection arrays. The arrays can detect the small variations as a light source fluctuates and should provide even better detection of quantum fluctuations than a traditional camera can.

Having an encryption algorithm paired with truly random numbers isn’t enough. You still need a secure way to send your message along with the cryptographic key to the recipient.

For encrypting and decrypting keys, the standard protocol for many years has been the RSA algorithm. Developed in 1977 by cryptographers Ron Rivest and Adi Shamir and computer scientist Leonard Adleman, it hinges on a mathematical trick known as a one-way function—that’s any calculation that is very easy to solve in one direction but extremely hard to solve in reverse. A classic example—and the one that Rivest, Shamir, and Adleman used—is to multiply two large prime numbers, typically 1,024 or even 2,048 bits in length. It’s of course very easy to multiply the numbers together, but it’s very hard to factor the result back to the original prime numbers.

RSA and similar algorithms give every network user two keys: a public key (known to everyone) and a private key (known only to the user). To send information, you encrypt it using the recipient’s public key. The recipient then decrypts the information using her private key. The algorithms have worked remarkably well for more than four decades because it’s extremely hard to crack the private key, even knowing the public key.

Flipping a Coin: Quside’s random number generator is fully integrated into a chip that’s a fraction of a coin’s size. It’s faster than flipping a coin, too, and it can generate gigabits of random numbers every second.Photo: Optica

The algorithms aren’t perfect, however. One of the main problems is that they take a long time to encrypt and decrypt a relatively small amount of data. For that reason, we use these algorithms to encrypt keys but not messages. The other big problem is that the algorithms are crackable, at least in theory. Right now, the only methods to crack the code take too long, provided a mathematical breakthrough doesn’t make RSA and similar algorithms easily solvable. For any practical attack, not even today’s supercomputers are up to the task.

Using a clever 20-year-old algorithm, a quantum computer, however, could easily calculate prime number factors by exploiting the quantum property of superposition to drastically decrease the computation time needed to find the correct factors. Today’s quantum computers aren’t powerful enough to handle an RSA-level hack. But it’s only a matter of time, and when that day comes, our current cybersecurity infrastructure will become obsolete.

Ideally, we should be able to exchange cryptographic keys that cannot be cracked before quantum computers or mathematical breakthroughs catch us by surprise. One possibility is to use a technology called quantum key distribution. Much like generating truly random numbers, quantum key distribution relies on the unpredictable nature of quantum mechanics, in this case to distribute unique keys between two users without any third party being able to listen in. One of the most common methods is to encode the cryptographic key into the orientation of a photon and send that photon to the other person. To achieve full security, we need to combine quantum key distribution with one-time pads to encrypt our messages, which will still require extremely fast random number generators.

We believe these quantum random number generators will be able to provide all the random numbers we’ll ever need. We’ll also have to continually check that our quantum sources are free from defect and interference and are producing numbers that are truly random. At our lab, we’ve developed a method for determining how confident we can be in a source’s true randomness. Our “randomness metrology” begins with establishing both the physical process that the source uses and the precision of the source’s measurements. We can use that information to set a boundary on how much of the randomness is arising purely from the quantum process.

Now that we’ve taken the first steps in developing quantum random number generators that are small enough, cheap enough, and fast enough for widespread, everyday use, the next step will be to install and test them in computers, smartphones, and IoT devices. With true random number generators, we can produce unpredictable cryptographic keys, and if we combine those keys with a secure method to distribute them, no longer will we have to worry about the computational or mathematical skills of an enemy—even the most capable attacker is powerless against true unpredictability. Nearly a century and a half after Frank Miller proposed his one-time pad, unbreakable security could finally be within our grasp.

This article appears in the July 2018 print issue as “The Future of Cybersecurity Is Quantum.”

Carlos Abellán is CEO of the quantum cryptography startup Quside, in Barcelona. Valerio Pruneri is a cofounder of Quside and the Corning Inc. chair and leader of the optoelectronics group at the Institute of Photonic Sciences, also in Barcelona.

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## No More Invasive Surgery—This Pacemaker Dissolves Instead

### Temporary pacemakers are often vital but dangerous to remove when their jobs are done

The transient pacemaker, developed at Northwestern University in Evanston, Ill., harmlessly dissolves in the patient's body over time.

Northwestern University

After having cardiovascular surgery, many patients require a temporary pacemaker to help stabilize their heart rate. The device consists of a pulse generator, one or more insulated wires, and an electrode at the end of each wire.

The pulse generator—a metal case that contains electronic circuitry with a small computer and a battery—regulates the impulses sent to the heart. The wire is connected to the pulse generator on one end while the electrode is placed inside one of the heart’s chambers.

But there are several issues with temporary pacemakers: The generator limits the patient’s mobility, and the wires must be surgically removed, which can cause complications such as infection, dislodgment, torn or damaged tissues, bleeding, and blood clots.

Researchers have found that a dissolving pacemaker can make a real difference for patients. Last year a team of scientists at Northwestern University in Evanston, Ill., developed such a device, which will allow patients to live without being tethered to external hardware. The dissolving pacemaker is 250 microns thick, weighs less than half a gram, and dissolves in the patient’s body, eliminating the need for surgical removal.

In May, the researchers introduced an upgraded, smart version of the pacemaker. It connects to a network of soft, flexible wearable sensors the team developed. These sensors monitor various physiological functions to help determine when to pace the heart and at what rate. The pacing system is completely autonomous.

The device also releases an anti-inflammatory drug while it dissolves.

“When you implant any kind of foreign hardware into the human body, cells attack that object,” explains Igor Efimov, a member of the development team and a professor of biomedical engineering at the university. He says that releasing the anti-inflammatory drug will stop the body from rejecting the pacemaker.

The device isn’t available for human use yet, but Efimov expects it to be available to patients in less than five years.

The pacemaker’s outer layer is made of a bioresorbable polyurethane. The material is thin, soft, and flexible, which were important factors to consider during development because the device is placed on the surface of a patient’s heart, says John Rogers, who led the project. The IEEE Fellow is a professor of materials science and engineering, biomedical engineering, and neurological surgery.

Encased in the bioresorbable polyurethane are the electronics. On one side there is a receiver antenna and on the other side there is a transmission coil. They are connected via a serpentine radiofrequency diode.

A small wireless patch is attached to the patient’s chest, directly above the pacemaker. Electrodes are located on the side of the patch that is in direct contact with the person’s skin. Inside the device there is a small battery and a transmission coil. The electrodes record an electrocardiogram (ECG) on the patient and helps regulate impulses sent to the heart.

The transmission coils in the patch and the pacemaker couple wirelessly. An oscillating magnetic field produced by the battery induces current in the patch’s coil. The current is then passed into the coil in the pacemaker to power the device and control the heart’s pacing rate.

If the pacemaker detects that the patient’s heart rate dropped below a certain rate, it activates the transmission coil in the patch. This powers the pacemaker, allowing it to bring the patient’s heart rate back up to a healthy level.

A small wireless patch that is attached to the patient’s chest records an ECG, which is then sent to a mobile app and available for the patient's doctor.Northwestern University

The receiver antenna inside the pacemaker collects data on the patient’s heart rate and sends it to a mobile app using near-field communication protocols—the same technology used in smartphones and RFID tags. The data is monitored by the patient’s doctor.

The process of dissolution starts immediately after the device is implanted. The bioresorbable polyurethane is the first material to dissolve.

Each device will be designed and made for individual patients. If a surgeon needs a patient’s pacemaker to last for two weeks, the team will design the device so it maintains its integrity for the required time period, Rogers says.

While it is possible to measure how fast a patient’s heart is beating solely based on ECG results, Rogers says, outside factors such as physical activity, the patient’s blood type, and their resting heart rate can affect the heart.

So Rogers and his team developed a network of sensors that monitor the patient’s body temperature, oxygen levels, respiration, physical activity, muscle tone, and the heart’s electrical activity.

There are currently three units: one for the chest, forehead, and neck. The pacemaker’s system automatically analyzes the data collected by these sensors. If it detects abnormal cardiac rhythms, the pacemaker will change the impulse of the heart rate.

“If normal activity is regained, then it stops pacing,” Efimov explains. "This is important because if you stimulate the heart when it’s unnecessary, then you risk inducing arrhythmia.”

## iRobot CEO Colin Angle on Data Privacy and Robots in the Home

### In light of Amazon's recent acquisition, we revisit our 7 September 2017 Q&A with iRobot's CEO

iRobot CEO Colin Angle.
Photo: iRobot

Editor's note: Last week, Amazon announced that it was acquiring iRobot for \$1.7 billion, prompting questions about how iRobot's camera-equipped robot vacuums will protect the data that they collect about your home. In September of 2017, we spoke with iRobot CEO Colin Angle about iRobot's approach to data privacy, directly addressing many similar concerns. "The views expressed in the Q&A from 2017 remain true," iRobot told us. "Over the past several years, iRobot has continued to do more to strengthen, and clearly define, its stance on privacy and security. It’s important to note that iRobot takes product security and customer privacy very seriously. We know our customers invite us into their most personal spaces—their homes—because they trust that our products will help them do more. We take that trust seriously."

Story from 7 September 2017 follows:

About a month ago, iRobot CEO Colin Angle mentioned something about sharing Roomba mapping data in an interview with Reuters. It got turned into a data privacy kerfuffle in a way that iRobot did not intend and (probably) did not deserve, as evidenced by their immediate clarification that iRobot will not sell your data or share it without your consent.

Data privacy is important, of course, especially for devices that live in your home with you. But as robots get more capable, the amount of data that they collect will increase, and sharing that data in a useful, thoughtful, and considerate way could make smart homes way smarter. To understand how iRobot is going to make this happen, we spoke with Angle about keeping your data safe, integrating robots with the future smart home, and robots that can get you a beer.

Were you expecting such a strong reaction on data privacy when you spoke with Reuters?

Colin Angle: We were all a little surprised, but it gave us an opportunity to talk a little more explicitly about our plans on that front. In order for your house to work smarter, the house needs to understand itself. If you want to be able to say, “Turn on the lights in the kitchen," then the home needs to be able to understand what the kitchen is, and what lights are in the kitchen. And if you want that to work with a third-party device, you need a trusted, customer-in-control mechanism to allow that to happen. So, it's not about selling data, it's about usefully linking together different devices to make your home actually smart. The interesting part is that the limiting factor in making your home intelligent isn't AI, it's context. And that's what I was talking about to Reuters.

What kinds of data can my Roomba 980 collect about my home?

Angle: The robot uses its sensors [including its camera] to understand where it is and create visual landmarks, things that are visually distinctive that it can recognize again. As the robot explores the home as a vacuum, it knows where it is relative to where it started, and it creates a 2D map of the home. None of the images ever leave the robot; the only map information that leaves the robot would be if the customer says, “I would like to see where the robot went," and then the map is processed into a prettier form and sent up to the cloud and to your phone. If you don't want to see it, it stays on the robot and never leaves the robot.

Do you think that there's a perception that these maps contain much more private information about our homes than they really do?

Angle: I think that if you look at [the map], you know exactly what it is. In the future, we'd like it to have more detail, so that you could give more sophisticated commands to the robot, from “Could you vacuum my kitchen?" in which case the robot needs to know where the kitchen is, to [in the future], “Go to the kitchen and get me a beer." In that case, the robot needs to know where the kitchen is, where the refrigerator is, what a beer is, and how to grab it. We're at a very benign point right now, and we're trying to establish a foundation of trust with our customers about how they have control over their data. Over time, when we want our homes to be smarter, you'll be able to allow your robot to better understand your home, so it can do things that you would like it to do, in a trusted fashion.

"Robots are viewed as creatures in the home. That's both exciting and a little scary at the same time, because people anthropomorphize and attribute much more intelligence to them than they do to a smart speaker."

Fundamentally, would the type of information that this sort of robot would be sharing with third parties be any more invasive than an Amazon Echo or Google Home?

Angle: Robots have this inherent explosive bit of interest, because they're viewed as creatures in your home. That's both exciting and a little scary at the same time, because people anthropomorphize and attribute much more intelligence to them than they do to a smart speaker. The amount of information that one of these robots collect is, in many ways, much less, but because it moves, it really captures people's imagination.

Why do you think people seem to be more concerned about the idea of robots sharing their data?

Angle: I think it's the idea that you'd have a “robot spy" in your home. Your home is your sanctuary, and people rightfully want their privacy. If we have something gathering data in their home, we're beyond the point where a company can exploit their customers by stealthily gathering data and selling it to other people. The things you buy and place in your home are there to benefit you, not some third party. That was the fear that was unleashed by this idea of gathering and selling data unbeknownst to the customer. At iRobot, we've said, “Look, we're not going to do this, we're not going to sell your data." We don't even remember your map unless you tell us we can. Our very explicit strategy is building this trusted relationship with our customers, so that they feel good about the benefits that Roomba has.

How could robots like Roomba eventually come to understand more about our homes to enable more sophisticated functionality?

Angle: We're in the land of R&D here, not Roomba products, but certainly there exists object-recognition technology that can determine what a refrigerator is, what a television is, what a table is. It would be pretty straightforward to say, if the room contains a refrigerator and an oven, it's probably the kitchen. If a room has a bed, it's probably a bedroom. You'll never be 100 percent right, but rooms have purposes, and we're certainly on a path where just by observing, a robot could identify a room.

What else do you think a robot like a Roomba could ultimately understand about your home?

Angle: We're working on a number of things, some of which we're happy to talk about and some of which less so at this point in time. But, why should your thermostat be on the wall? Why is one convenient place on the wall of one room the right place to measure temperature from, as opposed to where you like to sit? When you get into home control, your sensor location can be critically tied to the operation of your home. The opportunity to have the robot carry sensors with it around the home would allow the expansion from a point reading to a 2D or 3D map of those readings. As a result, the customer has a lot more control over [for example] how loud the stereo system is at a specific point, or what the temperature is at a specific point. You could also detect anomalies in the home if things are not working the way the customer would like them to work. Those are some simple examples of why moving a sensor around would matter.

“There's a pretty sizeable market for webcams in the home. People are interested in security and intruder detection, and also in how their pets are doing. But invariably, what you want to see is not what your camera is pointing at. That's something where a robot makes a lot more sense."

Another good example would be, there's actually a pretty sizeable market for webcams in the home. People are interested in security and intruder detection, and also in how their pets are doing. But invariably, what you want to see is not what your camera is currently pointing at. Some people fill up their homes with cameras, or you put a camera on a robot, and it moves to where you want to look. That's something where a robot makes a lot more sense, and it's interesting, if I want to have privacy in our home and yet still have a camera I can use, it's actually a great idea to put one on a robot, because when the robot isn't in the room with you, it can't see you. So, the metaphor is a lot closer to if you had a butler in your home— when they're not around, you can have your privacy back. This is a metaphor that I think works really well as we try to architect smart homes that are both aware of themselves, and yet afford privacy.

So a mobile robot equipped with sensors and mapping technology to be able to understand your home in this way could act like a smart home manager?

Angle: A spatial information organizer. There's a hub with a chunk of software that controls everything, and that's not necessarily the same thing as what the robot would do. What Apple and Amazon and various smart home companies are doing are trying to build hubs where everything connects to them, but in order for these hubs to be actually smart, they need what I call spatial content: They need to understand what's a room, and what's in a room for the entire home. Ultimately, the home itself is turning into a robot, and if the robot's not aware of itself, it can't do the right things.

“A robot needs to understand what's a room, and what's in a room, for the entire home. Ultimately, the home itself is turning into a robot, and if the robot's not aware of itself, it can't do the right things."

So, if you wanted to walk into a room and have the lights turn on and the heat come up, and if you started watching television and then left the room and wanted the television to turn off in the room you'd left and turn on in the room you'd gone to, all of those types of experiences where the home is seamlessly reacting to you require an understanding of rooms and what's in each room. You can brute force that with lots of cameras and custom programming for your home, but I don't believe that installations like this can be successful or scale. The solution where you own a Roomba anyway, and it just gives you all this information enabling your home to be smart, that's an exciting vision of how we're actually going to get smart homes.

Angle: We're getting there. In order for manipulation in the home to make sense, you need to know where you are, right? What's the point of being able to get something if you don't know where it is. So this idea that we need these maps that have information embedded in them about where stuff is and the ability to actually segment objects—there's a hierarchy of understanding. You need to know where a room is as a first step. You need to identify where objects are—that's recognition of larger objects. Then you need to be able to open a door, say, and now you're processing larger objects to find handles that you can reach out and grab. The ability to do all of these things exists in research labs, and to an increasing degree in manufacturing facilities. We're past the land of invention of the proof of principle, and into the land of, could we reduce this to a consumer price point that would make sense to people in the home. We're well on the way—we will definitely see this kind of robot in, I would say, five to 10 years, we'll have robots that can go and get you a beer. I don't think it's going to be a lot shorter than that, because we have a few steps to go, but it's less invention and more engineering.

We should note that we spoke with Angle just before Neato announced their new D7 robot vacuum, which adds persistent, actionable maps, arguably the first step towards a better understanding of the home. Since Roombas already have the ability to make a similar sort of map, based on the sorts of things that Angle spoke about in our interview we're expecting to see iRobot add a substantial amount of intelligence and functionality to the Roomba in the very near future.

## How 6G Promises to Build a Sustainable Future

### Wednesday, 22 June 2022, 2pm ET

Keysight
Keysight

As 5G evolves into 6G networks, it will be critical that it adopt the most energy-efficient technologies to reduce carbon emissions and our dependence on non-renewable resources.

In terms of increased sustainability, 6G will need to aim directly at lessening its overall environmental impact, including water consumption, raw material sourcing, and waste handling. But it is also important to consider the indirect impact of 6G networks can have on sustainability by conserving resources and minimizing waste in either existing use-cases or novel use-cases.

Areas that this webinar will examine in how 6G can take on a key role in a sustainable future include:

• Sensor networks (e.g., smart ag, fleet and building management) – IoT, low power, and inherently backscatter compatible 6G
• Smart grids and grid-interactive data centers
• Micro grids, where devices can share and trade low levels of energy to prevent the need for batteries
• Zero-energy devices – a new opportunity in 6G

Speakers:

Onel Alcaraz López, Assistant Professor, 6G Flagship, University of Oulu

• Onel L. A. López received the B.Sc. (1st class honors, 2013), M.Sc. (2017) and D.Sc. (with distinction, 2020) degree in Electrical Engineering from the Central University of Las Villas (Cuba), the Federal University of Paraná (Brazil) and the University of Oulu (Finland), respectively. From 2013-2015 he served as a specialist in telematics at the Cuban telecommunications company (ETECSA). He is a collaborator to the 2016 Research Award given by the Cuban Academy of Sciences, a co-recipient of the 2019 IEEE European Conference on Networks and Communications (EuCNC) Best Student Paper Award, and the recipient of the 2020 best doctoral thesis award granted by Academic Engineers and Architects in Finland TEK and Tekniska Föreningen i Finland TFiF in 2021. He is co-author of the book entitled "Wireless RF Energy Transfer in the massive IoT era: towards sustainable zero-energy networks'', Wiley, Dec 2021. He currently holds an Assistant Professorship (tenure track) in sustainable wireless communications engineering in the Centre for Wireless Communications (CWC), Oulu, Finland. His research interests include sustainable IoT, energy harvesting, wireless RF energy transfer, wireless connectivity, machine-type communications, and cellular-enabled positioning systems.