In environments where it is vital for customers to be aware of service information, such as in restaurants, amusement parks or for transportation routes, accurate congestion information can provide real-time data about aspects like service availability and queue length.

Yet because congestion information can quickly become outdated, interruptions in queuing systems often cause users to seek other options. While such choices may serve them better individually, this behavior can make the entire system inefficient, said Ness Shroff, senior author of the study and a professor of computer science and engineering at The Ohio State University.

“If information is outdated and thus everybody’s joining what appears to be the shortest path, you’re going to create congestion over that path,” said Shroff. This bottleneck can lead to gaps in fresh information for future customers to access and use, and impede overall service progress over time.

To better regulate this information learning, researchers have developed a way to incentivize people to choose less popular service alternatives. The proposed method is a side-payment mechanism that would periodically charge customers who contribute to overcrowding by making “selfish” choices and reward others for exploring alternative avenues.

In experiments using real-world datasets, the team found that this system was adept at balancing congestion with addressing user needs via alternative routes, resulting in steady performance. According to Shroff, adding incentivized settings to mobile queuing platforms goes a long way to making these complex systems work more sensibly for everyone.

“We calculate when the public value of fresh information is worth the congestion it takes to get it, and then build incentives that steer individual choices towards that balance,” he said. “Giving incentives for people to try out different routes might in fact create better opportunities for all.”

The study was published in the journal IEEE/ACM Transactions on Networking.

According to the study, this team’s work is the first to examine how human choice can impact system outcomes. Hongbo Li, lead author of the study and a postdoctoral scholar at the AI-EDGE Institute at Ohio State, calls this phenomenon human-in-loop learning (HILL), noting that leveraging it can provide researchers with new insights into the growing class of service systems that rely on decentralized, customer-driven data.

“Designing a mechanism to change a user’s decision to be both consistent with social welfare and long-term utility can be difficult,” he said. “It has to be done in a way that doesn’t directly hurt their service benefit.”

A promising use-case scenario could look like this: A user visiting a car-charging station might be rewarded for choosing a less crowded location farther away, but penalized for visiting a closer station that is already at risk of becoming overloaded. Although both visits generate useful information for the operating system, the former is more valuable because curbing congestion helps reduce system inefficiencies, said Li.

“In testing, we saw that even average use saves costs and energy,” he said. “This means our approach is amazingly good for the social optimum.”

Besides keeping these systems more accurate, this team’s mechanism would also limit expenses by using the money earned from those penalized to pay out rewards. With millions of people relying on queuing systems to navigate their day-to-day lives, these meaningful findings could inform future network design for a wide number of technologies and industries, the researchers say.

To advance the work, the team next aims to test how well their system works when people make different, unexpected choices regarding prices, risks and personal convenience.

“Our next step may be to develop mechanisms that are more robust to heterogeneous users and to test them experimentally in different scenarios,” said Li. “It’s important to consider human behavior in engineering, and our goal was to show that.”

Other co-authors include Lingjie Duan from the Singapore University of Technology and Design.

Using machine learning tools to create a digital twin, or a virtual copy, of an electronic circuit that exhibits chaotic behavior, researchers found that they were successful at predicting how it would behave and using that information to control it.

Many everyday devices, like thermostats and cruise control, utilize linear controllers – which use simple rules to direct a system to a desired value. Thermostats, for example, employ such rules to determine how much to heat or cool a space based on the difference between the current and desired temperatures.

Yet because of how straightforward these algorithms are, they struggle to control systems that display complex behavior, like chaos.

As a result, advanced devices like self-driving cars and aircraft often rely on machine learning-based controllers, which use intricate networks to learn the optimal control algorithm needed to best operate. However, these algorithms have significant drawbacks, the most demanding of which is that they can be extremely challenging and computationally expensive to implement. 

Now, having access to an efficient digital twin is likely to have a sweeping impact on how scientists develop future autonomous technologies, said Robert Kent, lead author of the study and a graduate student in physics at The Ohio State University. 

“The problem with most machine learning-based controllers is that they use a lot of energy or power and they take a long time to evaluate,” said Kent. “Developing traditional controllers for them has also been difficult because chaotic systems are extremely sensitive to small changes.”

These issues, he said, are critical in situations where milliseconds can make a difference between life and death, such as when self-driving vehicles must decide to brake to prevent an accident.

The study was published recently in Nature Communications.

Compact enough to fit on an inexpensive computer chip capable of balancing on your fingertip and able to run without an internet connection, the team’s digital twin was built to optimize a controller’s efficiency and performance, which researchers found resulted in a reduction of power consumption. It achieves this quite easily, mainly because it was trained using a type of machine learning approach called reservoir computing. 

“The great thing about the machine learning architecture we used is that it’s very good at learning the behavior of systems that evolve in time,” Kent said. “It’s inspired by how connections spark in the human brain.”

Although similarly sized computer chips have been used in devices like smart fridges, according to the study, this novel computing ability makes the new model especially well-equipped to handle dynamic systems such as self-driving vehicles as well as heart monitors, which must be able to quickly adapt to a patient’s heartbeat.   

“Big machine learning models have to consume lots of power to crunch data and come out with the right parameters, whereas our model and training is so extremely simple that you could have systems learning on the fly,” he said. 

To test this theory, researchers directed their model to complete complex control tasks and compared its results to those from previous control techniques. The study revealed that their approach achieved a higher accuracy at the tasks than its linear counterpart and is significantly less computationally complex than a previous machine learning-based controller. 

“The increase in accuracy was pretty significant in some cases,” said Kent. Though the outcome showed that their algorithm does require more energy than a linear controller to operate, this tradeoff means that when it is powered up, the team’s model lasts longer and is considerably more efficient than current machine learning-based controllers on the market. 

“People will find good use out of it just based on how efficient it is,” Kent said. “You can implement it on pretty much any platform and it’s very simple to understand.” The algorithm was recently made available to scientists. 

Outside of inspiring potential advances in engineering, there’s also an equally important economic and environmental incentive for creating more power-friendly algorithms, said Kent. 

As society becomes more dependent on computers and AI for nearly all aspects of daily life, demand for data centers is soaring, leading many experts to worry over digital systems’ enormous power appetite and what future industries will need to do to keep up with it. 

And because building these data centers as well as large-scale computing experiments can generate a large carbon footprint, scientists are looking for ways to curb carbon emissions from this technology. 

To advance their results, future work will likely be steered toward training the model to explore other applications like quantum information processing, Kent said. In the meantime, he expects that these new elements will reach far into the scientific community. 

“Not enough people know about these types of algorithms in the industry and engineering, and one of the big goals of this project is to get more people to learn about them,” said Kent. “This work is a great first step toward reaching that potential.”

This study was supported by the U.S. Air Force’s Office of Scientific Research. Other Ohio State co-authors include Wendson A.S. Barbosa and Daniel J. Gauthier. 

Spintronic memories, like those used in some high-tech computers and satellites, use magnetic states generated by an electron’s intrinsic angular momentum to store and read information. Depending on its physical motion, an electron’s spin produces a magnetic current. Known as the “spin Hall effect,” this has key applications for magnetic materials across many different fields, ranging from low power electronics to fundamental quantum mechanics. 

More recently, scientists have found that electrons are also capable of generating electricity through a second kind of movement: orbital angular momentum, similar to how Earth revolves around the sun. This is known as the “orbital Hall effect,” said Roland Kawakami, co-author of the study and a professor in physics at The Ohio State University. 

Theorists predicted that by using light transition metals – materials that have weak spin Hall currents – magnetic currents generated by the orbital Hall effect would be easier to spot flowing alongside them. Until now, directly detecting such a thing has been a challenge, but the study, led by Igor Lyalin, a graduate student in physics, and published today in the journal Physical Review Letters, showed a method to observe the effect.

“Over the decades, there’s been a continuous discovery of various Hall effects,‘’ said Kawakami. “But the idea of these orbital currents is really a brand new one. The difficulty is that they are mixed with spin currents in typical heavy metals and it’s difficult to tell them apart.”

Instead, Kawakami’s team demonstrated the orbital Hall effect by reflecting polarized light, in this case, a laser, onto various thin films of the light metal chromium to probe the metal’s atoms for a potential build-up of orbital angular momentum. After nearly a year of painstaking measurements, researchers were able to detect a clear magneto-optical signal which showed that electrons gathered at one end of the film exhibited strong orbital Hall effect characteristics. 

This successful detection could have huge consequences for future spintronics applications, said Kawakami.

“The concept of spintronics has been around for about 25 years or so, and while it’s been really good for various memory applications, now people are trying to go further,” he said. “Now, one of the field’s biggest goals is to reduce the amount of energy consumed because that’s the limiting factor for jacking up performance.”

Lowering the total amount of energy needed for future magnetic materials to operate well could potentially enable lower power consumption, higher speeds and higher reliability, as well as help to extend the technology’s lifespan. Utilizing orbital currents instead of spin currents could possibly save both time and money in the long term, said Kawakami. 

Noting that this research opens up a way to learn more about how these strange physics phenomena arise in other kinds of metals, the researchers say they want to continue delving into the complex connection between spin Hall effects and orbital Hall effects.

Co-authors were Sanaz Alikhah and Peter M. Oppeneer of Uppsala University and Marco Berritta of both Uppsala University and the University of Exeter. This work was supported by the National Science Foundation, the Swedish Research Council, the Swedish National Infrastructure for Computing and the K. and A. Wallenberg Foundation.

The research revolves around Bluetooth Low Energy (BLE), a type of Bluetooth that uses less energy when compared to Bluetooth Classic (an earlier generation of Bluetooth). On smartwatches and smartphones, billions of people rely on this type of wireless communication for all types of activities, ranging from entertainment and sports to retail and health care.  

Yet due to a design flaw in Bluetooth’s protocol, users’ privacy could be at risk, said Yue Zhang, lead author of the study and a postdoctoral researcher in computer science and engineering at The Ohio State University. Zhang recently presented the findings at the ACM Conference on Computer and Communications Security (ACM CCS 2022). The study also received a “best paper” honorable mention at the conference. 

Zhang and his adviser, Zhiqiang Lin, professor of computer science and engineering at Ohio State, proved the threat by testing over 50 market-available Bluetooth devices as well as four BLE development boards. They reported the flaw to major stakeholders in the Bluetooth industry, including Bluetooth Special Interest Group (SIG) (the organization that oversees the development of Bluetooth standards), hardware vendors such as Texas Instruments and Nordic, and operating systems providers such as Google, Apple and Microsoft. Google rated their findings as a high-severity design flaw and gave the researchers a bug bounty award. 

But the good news is that Zhang and Lin also developed a potential solution to the problem that they successfully tested.

Bluetooth devices have what are called MAC addresses – a string of random numbers that uniquely identify them on a network. About once every 20 milliseconds an idle BLE device sends out a signal advertising its MAC address to other nearby devices that it could connect with. 

The study identifies a flaw that could allow attackers to observe how these devices interact with the network, and then either passively or actively collect and analyze the data to break a user’s privacy.  

“This is a new finding that nobody has ever noticed before,” said Zhang. “We show that by broadcasting a MAC address to the device’s location, an attacker may not physically be able to see you, but they would know that you’re in the area.”

One of the reasons researchers are concerned about such a scenario is because a captured MAC address could be deployed in what is called a replay attack, which may allow the attacker to monitor the user’s behaviors, track where the user has been in the past or even figure out the real-time location of the user.  

“Bluetooth SIG was certainly made aware of the MAC address tracking threat, and to protect devices from being tracked by bad actors, a solution called MAC address randomization has been used since 2010,” said Lin.

Later in 2014, Bluetooth introduced a new feature called the “allowlist” which only allows approved devices to be connected, and prevents private devices from accessing unknown ones. But according to the study, this allowlist feature actually introduces a side channel for device tracking. 

Zhang and Lin proved the new tracking threat is real by creating a novel attack strategy they called Bluetooth Address Tracking (BAT). The researchers used a customized smartphone to hack into more than 50 Bluetooth gadgets – most of them their own devices – and showed that by using BAT attacks, an attacker could still link and replay a victim’s data, even with frequent MAC randomization. 

As of yet, BAT attacks are undefeated, but the team did create a prototype of a defensive countermeasure. Called Securing Address for BLE (SABLE), their solution involves adding an unpredictable sequence number, essentially a timestamp, to the randomized address to ensure that each MAC address can only be used once to prevent the replay attack. The study noted it was successfully able to stop attackers from linking up to the victim’s devices. 

The results of their experiment showed that SABLE only slightly affects battery consumption and overall device performance, but Lin hopes to use the new attack and its countermeasure to raise awareness in the community. “The lesson learned from this study is that when you add new features to existing designs, you should revisit previous assumptions to check whether they still hold.”

This work was supported by the National Science Foundation.