With funding from the National Science Foundation, a new project from the University of Maine’s Wireless Sensor Networks (WiSe-Net) lab uses AI and machine learning to make soil monitoring more efficient. Working with researchers from the University of New Hampshire and University of Vermont, WiSe-Net developed a network of AI-powered sensors that learn over time how to better use energy while monitoring soil moisture and processing the resultant data. That means it will adjust as needed to take advantage of available wireless network resources.
“AI can learn from the environment, predict the wireless link quality and incoming solar energy to efficiently use limited energy, and make a robust low-cost network run longer and more reliably,” said Ali Abedi, professor of electrical and computer engineering at the University of Maine and principal investigator on the project.
The technology can also be used for other types of sensors, using the same AI methods to measure things like snow depth.
“Real-time monitoring of different variables requires different sampling rates and power levels,” Abedi said. “An AI agent can learn these and adjust the data collection and transmission frequency accordingly rather than sampling and sending every single data point, which is not as efficient.”
