SWATT: Synchronized Wide-area Sensing and Autonomous Target Tracking

Monitoring large land areas is a human-intensive process. Sensor networks offer the promise of automation. Modern sensor networks alone typically only address the problem of event detection; they do not not address having to analyze what is detected. Furthermore, despite advances in low-power, long-range embedded sensing as well as high-power, high-fidelity camera or UAS sensing, no one type of sensing system alone can be low-cost, widely deployed, and high-fidelity.We propose a hybrid network of low-power and high-power sensors that, together, offer wide-area coverage. We couple these with a mechanism for online machine learning, allowing the the network to react to events autonomously. We design, deploy and evaluate a proof-of-concept system that is able to detect, learn about and track a vehicle in real time based on this combination of multi-modal sensing and machine learning.