Unmanned aerial vehicle-aided networks have been widely recognized by both cellular and internet industry and academia as an emerging technique to enhance current networking infrastructure. The research objective of this project is to design, analyze, and evaluate AirEdge, which is an innovative robust 3D airborne computing and networking system that exploits a swarm of aerial mobile radio access points and edge servers carried or deployed by unmanned aerial vehicles. The unique feature of AirEdge is to enable fast-deployable highly efficient on-demand edge computing and networking services. AirEdge will enable a series of applications in the areas of disaster rescue, public safety, anti-terrorism, battlefield assistance, and mobile entertainment. For example, AirEdge can be rapidly deployed to the area impacted by the disaster and allows first responders to locate and identify injured people using face recognition and provide their corresponding health information for first aids. This project also fosters interdisciplinary research and provides a unique training program for undergraduate and graduate students.
This project aims to realize AirEdge through the communication-motion co-design principles for 3D airborne networking and communication-computation co-design principles to enable reliable and energy-efficient airborne edge computing. Toward this end, two fundamental research problems are investigated: 1) how to dynamically establish edge computing networks to enable flexible edge computing and 2) how to integrate dynamic computing resource deployments with the communication network provided by unmanned aerial vehicle (UAVs) to enable low-latency and high-performance edge computing on resource-constrained computing platforms. To address these problems, 1) a new edge-assisted optimal motion control scheme is designed to exploit the abundant computation power of the ground edge server and high-fidelity ray-tracing simulations to perform site-specific Air-to-Ground channel modeling; 2) a new energy-efficient motion planning strategy is developed for the UAV swarm with an objective to simultaneously enhance the area spectral efficiency of the entire serving site and satisfy the time-varying data rate requirements of the edge computing applications; 3) a novel multi-agent actor-critic (MA-AC) reinforcement learning method is developed to realize a more adaptive and robust model-free control scheme under the uncertainties of the deployment environment; 4) a new context-aware adaptive edge computing deployment solution is designed to optimally integrate the airborne edge computing with the airborne communication network; 5) a novel dynamic edge analytics framework is engineered for the airborne communication and computing. The framework leverages approximate computing to mitigate the tradeoff between computation quality, service latency, and energy efficiency in AirEdge.
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.
