CRII: CPS: Building Highly-efficient and Low-power Edge Computing with Data-driven Learning and Control

The Internet of Things (IoT) is described as networks of small physical devices, embedded with sensors, software, and other technologies, that easily exchange data with other devices and systems over the Internet. The convergence of traditional technologies from wireless networking, control systems, and automation with miniaturization and low-powered devices contributed to the development of IoT, spurred on by strong demand and rapid growth in smart home automation and smart cities.

Affordable interoperable IoT systems are increasingly ubiquitous in daily life. These IoT devices, working closely together, orchestrate a range of tasks, increasingly used for such activities as programmable personalized control of heating, cooling, and security in homes and offices. As these IoT devices become more capable, more computationally demanding tasks can be performed by these devices singly or in combination as a local distributed network bringing computing closer to the location where needed to improve responsiveness, i.e., at the edges of the Internet.

The challenge is to ensure the highly capable, timely performance, seamless collective operation of IoT devices with edge computing and even cloud services as an efficient purposeful system.

This project studies the relationships between system resource utilization and energy efficiency in various edge and IoT systems in order to better understand how to optimize the key performance parameters of edge computing systems. This project explores mitigating the inefficiency in edge systems through a data-driven approach. Specifically, the primary research directions include: (1) analyzing the power inefficiency in different edge systems and develop a data-driven energy-aware framework for runtime edge and IoT applications, (2) tailoring the edge runtime framework including parts of data and control planes to reveal hidden dependencies, and (3) scaling and evaluating this framework and methodology in high-fidelity realistic test scenarios.

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.