CRII: NeTS: Towards Predictive Communications for UAV based IoT Networks

Northern Arizona University, Flagstaff AZ

Investigators

Abstract

More than 2 million Unmanned Aerial Vehicles (UAVs) have shipped in 2016 creating a global market revenue of $4.5 billion, and is projected to increase further, with huge potential to transform modern living in future smart & connected communities. UAVs will significantly impact our daily life by enabling new applications, and simplifying existing applications including transportation, traffic control, remote health monitoring, surveillance, border patrolling, habitat monitoring, and precision agriculture. An important drawback to commercialize UAV-based solutions in these domains is networking inefficiency. Current communication protocols are extremely inefficient in accommodating dynamic topologies of networks of UAVs. This project seeks to address this issue and develop predictive communication strategies by anticipating network topology changes. The research will thus develop cognizant communication protocols for fully autonomous UAV networks by facilitating high-throughput information exchange and eliminating the need for multiple ground control stations. This project aims to develop novel tools for predictive communication for networks of flying objects with heterogeneous maneuverability levels. The key idea is to take preventive actions via communication protocols before anticipated network partitions and/or failures. The proposed methodology relies on developing a universal model to predict motion trajectories of surrounding network nodes. To enable motion profiling, driving force of each object is modeled as a hierarchical generative model with a hidden layer shared among objects of the same type. The use of a novel merge-and-split method provides flexibility in accommodating new object classes with unseen maneuverability levels and repealing intruding objects. Novel methods based on cyclic monoids in category theory will be used to design optimal measurement patterns for network topology prediction. Finally, a predictive routing algorithm will be developed by incorporating the predicted network topology into decision making when looking for the optimal path. A concrete characterization of the tradeoff between the routing optimality and prediction uncertainties will be developed based on random matrix concentration inequalities to pave the road for practical implementation. Graph snappers and hybrid routing techniques will be studied to integrate conventional and predictive routing algorithms to accelerate the routing algorithm execution for large-scale networks. 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.