Our research explores system-level design of next-generation intelligent wireless systems and advanced sensing technologies, spanning both algorithm design as well as system prototyping. A key area of our work involves creating intelligent sensing platforms that integrate diverse sensor modalities to address complex real-world challenges. Our approach bridges the gap between theoretical innovation and robust system prototyping, driving advancements in connected and context-aware environments.
The rapid advancement in aerial robotics has unlocked vast potential for Unmanned Aerial Vehicles (UAVs) to provide a wide range of "on-demand" services. Our research focuses on leveraging UAVs to create lightweight, flexible, and portable wireless networks that can be deployed quickly and efficiently. These networks are particularly valuable in environments where existing infrastructure is absent, damaged, or insufficient, offering immediate and scalable solutions. UAV networks hold immense promise in disaster relief efforts, enabling first responders to establish communication and support in critical situations.
We focus on using RF-based sensing technologies, such as RFID, ultrawideband, and millimeter waves, to solve challenges in location tracking, smart environments, surveillance, and communication. By combining these sensing tools with mobile platforms like robots and drones, we can create scalable and efficient systems that work well in tough situations, like during emergencies. We are also working on making these sensing solutions lightweight and practical for use on IoT and edge devices, making them more accessible and easier to deploy.
Spectrum databases track available RF spectrum, but inaccuracies often lead to inefficient usage. To improve this, we enhance these databases with real-time spectrum measurements. To make spectrum sensing scalable and widely accessible, we are developing advanced algorithms that enable smarter, more efficient data collection, such as optimal wardriving strategies. Additionally, we are exploring generative AI tools to predict radio signal maps based on terrain and wireless deployments, improving spectrum management in diverse environments.