The emergence of the Internet of Things (IoT) enables many new applications ranging from augmented reality and self-driving cars, to surveillance and cashier-less retail stores. These applications continuously collect video streams from IoT devices, such as sensors, cameras, and radars. They aim to understand the video content to make intelligent decisions, by running sophisticated video analytics tasks, such as counting people and recognizing license plates in the video streams. These video analytics tasks often run a collection of computing resources including IoT devices, edge clusters near the devices and the remote cloud, connected through networks with dynamic bandwidth and latency. This project will enable a high-performance video analytics framework that can support a variety of IoT applications in real-time, with high accuracy, and at scale.
The key idea of this project is to enable video analytics for IoT devices by joint optimizations across application, computing, and networking. Today’s solutions often focus on separated optimization, which leads to inaccurate answers to analytical queries, inefficient use of computing resources, and performance degrades when network condition changes. This project's video analytics framework will (1) leverage both network layer information and physical information to tune the parameters in video analytics, in order to optimize task accuracy, instead of network bandwidth, latency or quality of experience, (2) allocate computing resources for analytics tasks to meet multi-dimensional task-level service-level objectives with distributed time tracking and runtime scheduling, and (3) redesign video analytics and encoding algorithms by considering the network and computing constraints. This project will build and test representative video analytics applications on top of the system to demonstrate its capability. The project will facilitate the interactions between the machine learning research community and the systems/networking research community, and result in novel algorithms and efficient networked systems for video analytics. The project will also engage underrepresented groups and undergraduates in research.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
