This is a guest post. The views expressed here are solely those of the authors and do not represent positions of IEEE Spectrum or the IEEE.
Universities, together with other operators of large spaces like shopping malls or airports, are currently facing a dilemma: how to return to business as fast as possible, while at the same time providing a safe environment?
Given that the behavior of individuals is the driving factor for viral spread, key to answering this question will be understanding and monitoring the dynamics of how people move and interact while on campus, whether indoors or outdoors.
Fortunately, universities already have the perfect tool in place: the campus-wide Wi-Fi network. These networks typically cover every indoor and most outdoor spaces on campus, and users are already registered. All that needs to be added is data analytics to monitor on-campus safety.
We, a team of researchers from the University of Melbourne and the startup Nexulogy, have developed the necessary algorithms that, when fed data already gathered by campus Wi-Fi networks, can help keep universities safe. We have already tested these algorithms successfully at several campuses and other large yet contained environments.
To date, little attention has been paid to using Wi-Fi networks to track social distancing. Countries like Australia have rolled out smartphone apps to support contact tracing, typically using Bluetooth to determine proximity. A recent Google/Apple collaboration, also using Bluetooth, led to a decentralized protocol for contact monitoring.
Yet the success of these apps mainly relies on people voluntarily downloading them. A study by the University of Oxford estimated that more than 70 percent of smartphone users in the United Kingdom would have to install the app for it to be effective. But adoption is not happening at anything near that scale; the Australian COVIDSafe app, for example, released in April 2020, has only been downloaded by 6 million people by mid-June 2020, or about 24 percent of the population.
Furthermore, this kind of Bluetooth-based tracking does not relate the contacts to a physical location, such as a classroom. This makes it hard to satisfy the requirements of running a safe campus. And data collected by the Bluetooth tracking apps is generally not readily available to the campus owners, so it doesn’t help make their own spaces safer.
Our Wi-Fi based algorithms provide the least privacy-intrusive monitoring mechanisms thus far, because they use only anonymous device addresses; no individual user names are necessary to understand crowd densities and proximity. In the case of a student or campus staff member reporting a positive coronavirus test, the device addresses determined to belong to someone at risk can be passed on authorities with the appropriate privacy clearance. Only then would names be matched to devices, with people at risk informed individually and privately.
Wi-Fi presents the best solution for universities for a couple of reasons: wireless coverage is already campus wide; it is a safe assumption that everyone on campus is carrying at least one Wi-Fi capable device; and virtually everyone living and working on campus registers their devices to have internet access. Such tracking is possible without onerous user-facing app downloads.
Often university executives already have the rights to use the information collected in the wireless system included as part of its Terms and Conditions. In the midst of this pandemic, they now also have a legal or, at least a moral obligation, to use such data to their best ability to improve safety and well-being of everyone on campus.
The process starts by collecting the time and symbolic location (also known as network access point) of Wi-Fi capable devices when they are first detected by the Wi-Fi infrastructure, for example, when a student enters the campus’ Wi-Fi environment, and then during regular intervals or when they change locations. Then, after consolidating any multiple devices of a single user, our algorithms calculate the number of occupants in a given area. That provides a quick insight into crowd density in any building or outdoor plaza.
Our algorithms can also reconstruct the journey of any user within the Wi-Fi infrastructure, and from that can derive exposure time to other users, spaces visited and transmission risk.
This information lets campus managers do several things. For one, our algorithms can easily identify high risk areas by time and location and flag areas where social distance limits are regularly exceeded. This helps campus managers focus resources on areas that may need more hand sanitizers or more frequent deep cleaning.
For another, imagine an infected staff member has been identified by public health authorities. Based on the health authorities’ request, the university can identify possible infection pathways by tracking the individual’s journeys through the campus. It is possible to backtrack the movement history since the start of the data collection, for days or even weeks if necessary.
To illustrate the methodology, we assumed one of us had a COVID infection and we reconstructed his journey and exposure to other university visitors during a recent visit to a busy university campus. In the heat map below, you can see that only the conference building showed a significant number of contacts (red bar) with an exposure time of, in this example, more than 30 minutes. While we only detected other individuals by Wi-Fi devices, the university executives could now notify people that potentially have been exposed to an infectious user.
This system isn’t without technical challenges. The biggest problem is noise in the system that needs to be removed before the data is useful.
For example, depending on the building layout and wireless network configurations, wireless counts inside specific zones can include passing outside traffic or static devices (like desktop computers). We have developed algorithms to eliminate both without requiring access to any user information.
Even though the identification and management of infection risks is limited to the area covered by the wireless infrastructure, the timely identification of a COVID event naturally benefits areas beyond the campus. Campuses, and even residential campuses in lockdown, have a daily influx of external visitors —staff, contractors, and family members. Those individuals could be identified via telecommunication providers if requested by health authorities.
In the case of someone on campus testing positive for the virus, people they came in contact with, their contact times and places they went to can be identified within minutes. This allows the initiation of necessary measures (e.g. COVID testing or decontamination) in a targeted, timely and cost-effective way.
Since the analytics can happen in the cloud, the software can easily be updated to reflect on new or refined medical knowledge or health regulations, say a new exposure time threshold or physical distancing guidelines.
Privacy is paramount in this process. Understanding population densities and crowd management is done anonymously. Only in the case of someone on campus reporting a confirmed case of the coronavirus do authorities with the necessary privacy clearance need to connect the devices and the individuals. Our algorithm operates separately from the identification and notification process.
As universities around the world are eager to welcome students back to the campus, proactive plans need to be in place to ensure the safety and wellbeing of everyone. Wi-Fi is available and ready to help.
Jan Dethlefs is a data scientist and Simon Wei is a data engineer, both at Nexulogy in Melbourne, Australia. Stephan Winter is a professor and Martin Tomko is a senior lecturer, both in the Department of Infrastructure Engineering at the University of Melbourne, where they work with geospatial data analytics.
THE INSTITUTE Every year on 14 October the IEEE Standards Association (IEEE SA) joins the international community in celebrating the importance of standards development and honoring the collaboration of individuals and organizations across the globe that drive technological innovation.
This year’s World Standards Day theme is “Raising the World’s Standards for the Protection of the Planet.” During the past century, large-scale industrial activities, rapid population growth, urbanization, and inequality have negatively impacted the Earth, our lives, and the well-being of future generations. The concept of sustainable development has become more important. Global standards play a key role in supporting the environmental, social, and economic sustainability of our planet and human society.
IEEE SA has several standards and projects—as well as communities and resources—working on sustainable development.
IEEE SA is conducting a video contest, seeking entries that answer the question: How do standards protect the planet? All formats will be considered, including self-recorded and animated works, documentaries, and music videos.
Each video must be online and include a reference to an IEEE standard and its relevance to the theme. It must be original content, run between 15 and 60 seconds in length, be in English or include English subtitles, and include the URL where the video may be found.
Other eligibility requirements and contest rules can be found here.
Up to three videos will be selected, and each winner will receive a US $500 prize. The deadline is 15 September 2020.
Electronic waste could get recycled into strong, protective coatings for steel, a new study finds.
Recycling typically converts large quantities of items made of a single material, such as aluminum cans or glass bottles, into more of the same. However, this approach is not feasible for complex garbage such as electronic waste, or e-waste, because it contains many different materials that cannot be easily separated.
Still, there are many reasons to recycle e-waste. For example, there is a growing amount of it—the United Nations found that people generated 44.7 million metric tons of e-waste globally in 2016, and expected that to grow to 52.2 million metric tons by 2021. In addition, precious metals are often scattered within e-waste, although this fact can at times lead to appalling scenarios involving child workers scavenging amidst toxic waste.
"We've developed a throwaway mentality, where we use something until it's worn out or we don't need it or want it any more, and we get rid of it," says study senior author Veena Sahajwalla, a materials scientist and founding director of the Center for Sustainable Materials Research and Technology at the University of New South Wales in Sydney, Australia. "That would be fine if we had unlimited resources and unlimited space for disposal, but we don't."
Previous research showed the careful use of heat could selectively break and reform chemical bonds in e-waste to form new environmentally friendly materials. For instance, mixes of glass and plastic could find use in valuable silicon-loaded ceramics.
"It is very exciting that these waste materials have lot of valuable elements that could be reformed into brand-new products," Sahajwalla says. "To take just one example, some types of e-waste like printed circuit boards contain between 10% and 20% copper, while copper ore only contains up to 3%."
In the new study, researchers investigated the properties of copper and silica compounds often found in old printed circuit boards and computer monitors. They suspected that after these substances were extracted from e-waste, they could get combined to create a durable new hybrid material potentially useful for protecting metal surfaces against corrosion and wear.
First the researchers heated glass and plastic powder from old computer monitor screens and shells to 1,500 degrees C, generating silicon carbide wires 10 to 50 nanometers (billionths of a meter) in diameter. They next combined these ceramic nanowires with copper recovered from ground-up circuit boards, placed the mix on a steel surface, and then heated it up to 1,000 degrees C. This melted the copper to form a thin film 1 micron thick atop the steel. (The scientists noted this width could get adjusted to range from a few nanometers to a few hundred microns.)
This structural bonding of different elements creates new properties that are superior to the parent materials. "Say, for example, the metal structure has a good toughness but a poor hardness. In contrast, a ceramic has a high hardness but it's very brittle," Sahajwalla says. "Combining these two structures together successfully by the judicious choice of temperature after understanding the the raw material can create a completely new hybrid material that has a ceramic-like hardness and metal-like toughness. And surprisingly, all this could be done from waste sources, which can prevent these resources going to a landfill."
The scientists found the micron-thick hybrid layer increased the surface hardness of the steel by about 125%. In addition, microscope images revealed that when this hybrid layer was struck with a nano-sized indenter, it remained firmly bonded to the steel without cracking or chipping.
"For a long time, we have relied on mining to provide the raw materials we need, and we’ve thrown much of our waste into landfill," Sahajwalla says. "In the future, we may be mining those same landfill sites for our resources."
Sahajwalla and her colleague Rumana Hossain detailed their findings online July 13 in the journal ACS Omega.