AI-Edge Logo

A multi-organizational team led by The Ohio State University has been selected by the National Science Foundation as the nation’s designated AI Institute for Networking Research. Our team spans 11 different universities, four leading companies, and three of our nation’s premier DoD labs spanning world class experts in network theory, network systems, and AI/ML.

Main Figure Diagram, depicting how AI is built distributively and ties to wireless networks

double-arrowInstitute Vision

Research:

Networking and AI are two of the most transformative IT technologies --- helping to better people’s lives, contributing to national economic competitiveness, national security, and national defense. The Institute will exploit the synergies between networking and AI to design the next generation of edge networks (6G and beyond) that are highly efficient, reliable, robust, and secure. A new distributed intelligence plane will be developed to ensure that these networks are self-healing, adaptive, and self-optimized. The future of AI is distributed because AI will increasingly be implemented across a diverse set of edge devices. These intelligent and adaptive networks will in turn unleash the power of collaboration to solve long-standing distributed AI challenges, making AI more efficient, interactive, and privacy preserving. The Institute will develop the key underlying technologies for distributed and networked intelligence to enable a host of future transformative applications such as intelligent transportation, remote healthcare, distributed robotics, and smart aerospace. 

Education and Workforce Development:

It is a national priority to educate students, professionals, and practitioners in AI and networks, and substantially grow and diversify the workforce. The Institute will develop novel, efficient, and modular ways of creating and delivering education content and curricula at scale, and to spearhead a program that helps build a large diverse workforce in AI and networks spanning K-12 to university students and faculty.

double-arrowResearch Thrusts

The research plan of the Institute is organized around 8 thrusts that span two broad synergistic themes: AI for Networks (Thrusts 1—4) and Networks for AI (Thrusts 5-8).

AI for Networks

The astonishing success of AI provides a unique opportunity to design distributed intelligent efficient, self-healing, secure, and adaptive next generation edge networks. While  the  preliminary  successes  of  AI  for  networks  have  been  promising,  developing AI/ML algorithms to networking with minimal or no human oversight poses many important research questions will be explored systematically and in depth.

AI Chip and Wireless Tower

Re-engineering the Physics/Constraints

Re-engineer the physical fabric for6G+ wireless communications through AI, thus treating the fabric itself as a controllable entity.

AI Chip and Flow Chart

AI Based Network Resource Allocation

Develop new AI techniques for the design and control of these next generation networks taking into account practical resource constraints.

AI Chips with phones, cars, laptops, and drones, all interconnected

Multi-agent Network Control

Develop multi-agent AI techniques for distributed intelligence and control across possibly non-cooperative, network entities.

AI Chips and security locks

AI Powered Network Security

Develop new AI tools and techniques to guarantee that the network is secure, intrusion free, and highly robust.

Networks for AI

With the dramatic increases in processing power at the edge devices, we expect that the future of AI will be distributed as devices will use AI to make decision in their local environments connected by the network. We will develop intelligent and adaptive networks that will unleash the power of collaboration to solve long-standing distributed AI challenges, making AI more efficient, interactive, and privacy preserving.

AI Chips around a rotating gear

Network Aware AI Operation

Develop distributed AI that will seamlessly adapt its operation by taking into account computation, communication and data constraints.

AI Chips in a network with cars, cameras, drones, and robots.

Network Operations for Distributed AI

Re-engineer networks by adaptively allocating communication, computing, and storage resources for serving the needs of distributed AI applications.

Human and AI Chip interacting

Humans, AI and Network Research at the Interface

Develop new collaborative methods across humans-AI-networks to make systems more efficient than either human or machines by themselves.

AI Chip with a lock and a crossed out eye

Security and Privacy of Network Users

Design and control the networks such that they are privacy-aware and can be optimized to facilitate protection from information leakage and attacks.

double-arrowUse Cases

Diagram showing Use Cases of Ubiquitous Sensing, Machines + Humans + Mobility, and Programmable and Virtualized 6g+ Networks. Logos of partners including IBM, AT and T, Qualcomm, Army Research Laboratory, Air Force Research Laboratory, Microsoft Reasearch, and the U.S. Naval Research Laboratory

Use Inspired Research: The focus of the AI Institute will be on edge networks, which will constitute the majority of the growth of future networks. This edge includes all devices connected through the radio as well as data centers and cloud computing systems that are not in the core of the Internet. A critical component of the Institute is to shorten the time-scale of interactions between Foundations and use case research across multiple disciplines. This will result in a virtuous cycle that will have a cascading impact dramatically accelerating time it takes from research to implementation and technology transfer. The research tasks will be enhanced and fleshed out by exploring three wireless edge use cases in depth:

  1. Ubiquitous Sensing/Networking: This use case focuses on AI-driven sensing and networking, and consists of POWDER and COSMOS PAWR platforms being deployed in Salt Lake City and New York City over metropolitan areas of tens of miles in diameter. These networks will not merely carry information, but also generate, deliver, and process data from pervasively deployed multimodal sensors, enabling AI agents to become cognizant of the environment, necessary for optimized actuation within the wireless infrastructure.
  2. Human Machine Mobility: This use case focuses on extreme mobility settings. Based on a  6G+ system with terrestrial vehicular and unmanned aerial system (UAS) mounted systems being deployed at the AERPAW PAWR platform in Raleigh, NC the platform will be a distributed wireless infrastructure where mobile elements complement resource provisioning by fixed towers. By including mobility at various scales these systems can coordinate to create dynamic MIMO platforms to bridge the vast tracts of land that continue to impede rural broadband.
  3. Programmable/virtualized 6G networks: The focus here will be on open, agile, and modular radio access network architectures rooted in the principles of softwarization, virtualization, interoperability, and of separation of the data plane from control functionalities. We will demonstrate key outcomes in the Colloseum, the world's largest RF emulator. By instantiating multiple virtual network slices tailored to accommodate diverse network services, tenants, and traffic on-demand, this will lead to an unprecedented ability to control the entire network infrastructure end-to-end by AI.

These use cases are important in their own right and connect the key research thrusts and their validation to specific experimental platforms. The Institute will work its industry and DoD partners to facilitate translation and adoption.