The public may be divided over climate change issues, but the Pentagon and national security community are not. Secretary of Defense Lloyd Austin has said climate change is making the world more unsafe, citing accelerating security issues like pandemic and stability, mass migration, conflict over resources and natural disasters.
Nadya Bliss, executive director of Arizona State University’s Global Security Initiative, recently co-authored a white paper for the Computing Community Consortium that highlighted the role of computing research in addressing climate change-induced challenges.
The consortium catalyzes the computing research community with debate and discussion.
“When you have boots on the ground, you're not going to be stressing about partisan politics,” Bliss said. “You're going to be like, it's hotter in our areas of deployment. We need to protect our troops.”
Bliss and her co-authors examined six key areas where these challenges will arise: energy, environmental justice, transportation, infrastructure, agriculture, and environmental monitoring and forecasting. They identified specific ways in which computing research could help, using devices and architectures, software, algorithms/AI/robotics and sociotechnical computing.
Question: You've talked about monitoring energy in the environment. Where do you see gaps in these areas? The government and a lot of the private sector has sensors all over the Earth. What's not being covered? And what are we missing from them that computing research could fill in?
Answer: There's a bunch of different layers in various energy systems and how they interact with climate and weather patterns. Often those things are not connected. So if you think about it from a systemic perspective, a particular power company may have a clear monitoring of the energy needs of the community that it's monitoring. But if it's not connected to a state next door, what is happening? What kind of things are happening that potentially would cause people to either up or reduce their energy consumption and optimize accordingly?
If we think about the blackout in Texas this year, it was a complex sort of a confluence of a very specialized measurement infrastructure and then optimized for efficiency as opposed to resiliency, right? A lot of our systems tend to be optimized for efficiency versus resiliency. You can essentially create a system that's so complex and complicated and multi-layered that all you're doing is trying to figure out how to do analysis instead of making systems that are actually helping you with decisions. Acceptance of the fact that a lot of different facets of these challenges are interconnected and implementing for those interconnections could benefit society at large.
Q: Transportation: If all these elements — traffic, goods, people, pollution, energy availability and needs, etc. — are monitored, what could be improved in the name of climate crisis?
A: If we have a clear sense of various distribution patterns, we can absolutely increase efficiency of transportation networks — for example, if we could understand where we need to have electrical vehicle charging stations based on traffic patterns. Another thing that we can understand is a flow of how people go to certain places, right? Minimize congestion. It's well-documented that when non-electric cars are stalling, they are very wasteful with their gas consumption. If you could dynamically reroute traffic based on what is happening on the road, you potentially could reduce that congestion. This is something that is a very computationally intensive problem.
Q: Infrastructure: Any kind of sensor that could monitor the state of a bridge or road (and a database to manage the information) is easy to understand. How would AI/robotics/algorithms assist that?
A: You can have very cheap sensors embedded in roads. So low-power, specialized computing chips that are basically sensing the load on the roads that could communicate broadly and say, well, this traffic area is really congested, but this one isn't. So how do we do this? Some of this I imagine is already being done, but you can optimize traffic lights to adjust to traffic patterns. And these are just some examples of optimizations, but I think broadly there's a lot of opportunity for making a lot of those systems efficient.
Q: Finally, agriculture. Modern farms are swarming with tech, measuring things like moisture content of soils, etc. Tell me how tech-like systems for deployment and control of autonomous vehicles (e.g., UAVs, or unmanned aerial vehicles) and algorithms that leverage rich sensor data, together with real-time information about economic factors and transportation networks for planning and risk assessment, could help improve farming.
A: There could be optimization of things that are predictive like weather and climate patterns. I am definitely not an agriculture expert, but it's a similar set of things, right? It's embedded sensing connected to broad multilayer modeling that could allow you to make decisions and optimize for inefficiencies that potentially lead to a savings in energy.
Q: What impact does your collaborative research bring for the ASU community?
A: One of the things I really appreciate about the (Computing Community Consortium) is working with my colleagues across the country. I learned a ton from each and every one of them. It also gives me an opportunity to have ASU be at the table for these conversations, as we're shaping and informing the national agenda for computing research. I get to bring to bear all of the experiences and expertise that we have here at ASU to that discussion.
Top image by Comfreak/Pixabay
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