Our lab develops intelligent, connected field instruments for real-time monitoring of Aedes mosquitoes. We design species-selective acoustic and visual lures that attract male Aedes by mimicking female flight tones. Low-cost optical sensors identify insects by their wingbeat frequency. Edge AI running on low-power microcontrollers classifies them directly on the device, and long-range LoRa networks transmit compact metadata for city-scale population mapping. Our first system, MAST-CloudNet, captured 49 Aedes against 1 in a silent control during outdoor field trials, reaching 83.1% target selectivity, and has been accepted in IEEE Transactions on Instrumentation and Measurement. Its successor, MAAS-IoT, has already recorded live male Aedes wingbeats with a working optical detector prototype, and on-device classification, LoRa networking and a field-ready enclosure are now in development. This interdisciplinary work, conducted with the Department of Zoology, University of Dhaka, is building toward autonomous, solar-powered surveillance networks for early dengue warning.
Smart Vector Surveillance for Dengue Early Warning
