Radio city


October 19, 2017

If you ask Bob Iannucci to describe a radio, he is likely to take his cell phone out of his pocket and hold it up to you.

“When you look at this, you see a cell phone,” he says. “When I look at it, I see 8 antenna and 11 radios.”

Iannucci is the director of the CyLab Mobility Research Center at Carnegie Mellon University, and resident at CMU’s Silicon Valley campus. His cell phone exemplifies how prevalent radio technology is in our daily lives. If it weren’t for radios, we wouldn’t type on Bluetooth keyboards, wouldn’t engage in wireless video chats, wouldn’t be guided safely to our destinations by GPS mapping. These technologies illustrate what radios do for us now. But what will they do for us in the future?

What's radio got to do with it?

In essence, a radio is just a means for remote communication. It’s a device that can transmit and receive electromagnetic waves of radio frequency that carry information, such as audio and data. The information is captured in these radio waves by altering the waves’ amplitude, frequency, and phase (a process called modulation). The waves are then transmitted through the air to an electrical conductor, where they are demodulated to recover the original information.

This is how music can travel from a radio station’s antenna to your car’s stereo, or how your voice travels from one cell phone to another, and even how GPS data is extracted from satellites circling the earth. Bluetooth is radio. Your Wifi and LTE receivers are radios, too. But according to Iannucci, the future of radio technology will take us far beyond these applications to a whole new computing platform that will be just as revolutionary as the smart phone.

“We’ve created an experimental, mostly software-defined testbed for advanced wireless research, called CROSSMobile,” says Iannucci. “We’re using it to study critical elements that will make up the next-generation communication and computing platform. We start with today’s wireless networks and study how they can be improved. But rather than just considering how to stream videos faster or how to improve person-to-person messaging, we are converging on a vision of how communications and computation will come together to create a wholly new computing platform.”

Iannucci’s platform is based on the idea that communication—not only between people, but between devices—is the key to next-generation computing technologies. Platforms of the past – mainframes, minicomputers, and PCs – largely focused on computation. But the mobile revolution brought about a shift in thinking. Computation and communication became equals. Looking ahead, however, we may come to see communication dominate computation in emerging platforms. Consider the so-called smart city. From machine sensing and predicting traffic patterns, to generating wireless emergency alerts and aerial drone surveying; smart cities will be integrated with millions of devices, talking back and forth constantly on so many radios while aggregating and processing shreds of information to generate insights. And at the center of it all will be the next-generation wireless network.

“Together with my colleagues ECE Associate Professor Anthony Rowe and Assistant Professor Swarun Kumar, we’re putting traffic sensors into the city environment, and these sensors need robust, low-power wireless communication capabilities,” says Iannucci. “We’ve built mechanisms to allow these sensors to self-adapt to the challenging radio conditions of the smart city – it’s the ‘can you hear me now’ question for billions of city-embedded Internet of Things devices. Taking a cue from Carnegie Mellon’s past, we are approaching the understanding of networks for smart cities from a systems point of view. What’s emerging is the need to stop thinking about networks and start thinking about platforms. This work brings together past research in distributed systems, real-time computation, wireless networking and low-power embedded computing into a new model that seeks to radically simplify the programming task for such an ensemble.”

This initial traffic sensing research is funded by the University Transportation Center’s Technologies for Safe and Efficient Transportation (TSET) Initiative, and Iannucci is currently working closely with faculty at the Pittsburgh campus to deploy this sensing technology around the city.

Broadband and the CMU network

Today, we take the power of parallel computing for granted. Google, Amazon, and the rest of the tech world’s biggest companies use parallel computing platforms, or huge data centers filled with computers that are linked together to provide seemingly unlimited storage and computing power. But as little as 30 years ago, some of the first, groundbreaking work in high-performance parallel computing was done here at Carnegie Mellon. The C.mmp and CM* systems projects provided proof that computers could be harnessed in parallel to do large, complex tasks, leading to the parallel data centers that now power much of the world around us.

Once again, Carnegie Mellon is leading the charge toward the next generation platform—this time, an open, intelligent, flexible, spectrum-collaborative communication network that merges communication and computation all the way to the smallest sensor devices. Carnegie Mellon seeks to make this research relevant and applicable by conducting studies of the key problems in a real-world context. Integrating the CROSSMobile project with CMU’s Crowdsourced Smart City project weaves together low-power, in-the-city sensing and embedded computing with the network itself. In essence, Iannucci and his team imagine that future networks, together with their associated sensing devices, will emerge as the next major computing platform. But bringing this vision to reality requires solving a programming problem. To the programmer, the cloud, the network, and the devices together must be made to look like a coherent ensemble rather than so many separate yet interconnected subsystems. Iannucci calls this concept “edgeless computing.”

Changing the nature of networks

“If we think about smart cities and say, smart intersections, as cars come into the intersection, we want make sure that they don’t collide or hit pedestrians,” says Iannucci. “The sensors in the intersection at that time—in cars, in the pavement, in the signaling systems, and in the smartphones of pedestrians near the intersection—can be federated, and what they tell us at that instant can help avoid accidents and improve efficiency; but only if the wireless network does its job right.”

Connecting sensed data—such as where the cars are on the road—with time on a millisecond scale relies on mechanisms that don’t exist in today’s networks and sensor devices. Adding the mechanisms is possible, but effectively coordinating them is an enormous challenge for the programmer. Currently, Iannucci is working on new approaches that would relieve many of these complexities. Making the creation of smart city ‘apps’ about as easy as creating smartphone apps will be essential to bring about the same level of societal and economic impact the smartphone has had. This is what Iannucci is talking about when he talks about a new kind of computing platform.

In the smart intersection, the lower the network’s latency—or the time it takes for data to be transported over a network—the fewer accidents will occur. With current technological capacity, all communication networks go through the cloud. If a smart traffic light must turn red because two cars are about to collide, a signal would have to go from the light’s sensor, up through the cloud, then back down to the light. This could take up to 2000 milliseconds. To prevent an accident, the latency needs to be more like 2 milliseconds.

“We are trying to change the nature of networks of IoT devices that will allow programmers to create apps that, at the same time, minimize latency, achieve time synchronization and minimize power—especially important for install-and-forget smart city devices. Our work goes beyond the so-called edge of the network to create an integrated system for apps that encompasses cloud, network and device: edgeless computing.” Iannucci says. “Edgelessness – breaking down the assumption that the network has, or should have, an edge is essential to programmable, low-latency distributed computing in the smart city and beyond.”