EMERGENCY COMMUNICATIONSmart Algorithms Prevent Digital Traffic Jams—Especially When Public Emergencies Strike
In critical moments of public emergencies like earthquakes or extreme weather, network slowdowns can block urgent alerts, delay emergency updates, or disrupt public transit coordination.
Smartphones and wearables collect real-time data—like traffic flow, air quality, and crowd density—that fuel smart-city services. But when too many devices offload data at once, networks slow down and batteries drain quickly. In critical moments of public emergencies like earthquakes or extreme weather—these slowdowns can block urgent alerts, delay emergency updates, or disrupt public transit coordination. Without fast, reliable data flow, cities risk losing their real-time edge when it matters most.
To tackle this digital traffic jam, researchers from Jilin University and the University of North Carolina developed an energy-efficient strategy that significantly reduces processing delays and power use during peak hours. By keeping devices responsive and conserving energy, their method helps ensure seamless services like navigation updates, pollution alerts, and other urban applications—even when demand surges.
“Our goal was to keep everyone’s devices up and running—even when the digital rush hour hits—without draining the battery,” says Prof. Jing Deng, lead researcher on the project.
Clever Algorithms Power Greener Data Routing
The researchers developed a two-step smart control system that works like a smart traffic and delivery controller for urban data. First, to avoid digital traffic jams, the system monitors how busy each mobile device and nearby server is, then decides how much data each device should send and when—just like a city’s traffic lights adjusting in real time to keep cars flowing smoothly. This process is powered by a technique called Lyapunov optimization. Next, once the system knows what data needs to be sent, it sorts different types—like videos, images, or text—and directs them to the most suitable servers, similar to how a logistics center assigns packages to the right delivery trucks for faster delivery. This second step uses a method called the Kuhn–Munkres algorithm. Together, these coordinated strategies reduce energy use, cut delays, and keep smart-city services running reliably even during peak loads.
Beating the Benchmarks: Major Gains in Speed and Energy Savings
In head-to-head tests with established methods, the new approach struck the best balance between energy efficiency and system stability. While some strategies slightly lowered energy use, they caused server queues to swell and risked system slowdowns. The proposed method kept queue lengths under control and maintained steady performance, even under heavy data loads—demonstrating superior reliability and smarter trade-offs for real-world smart-city systems.
As smart cities rely more heavily on real-time data from countless connected devices, keeping these systems stable, efficient, and responsive is no small feat. This research offers a practical, future-ready solution—using intelligent data-routing strategies that not only reduce energy consumption and delays, but also maintain system reliability when it matters most. By balancing performance with stability and outperforming conventional methods under real-world conditions, the approach lays a solid foundation for smarter, greener, and more resilient urban data networks.
