NSF Award
2221875
With the advances in the technology, unmanned aerial vehicles (UAVs) have become lighter, cheaper, and easier to deploy. Combined with their mobility, autonomy and flexibility, UAVs are now poised to be used in a variety of real-world scenarios (such as delivery of medical supplies, disaster relief, emergency search and rescue, environment monitoring, and remote sensing), and are expected to be an integral part of next-generation wireless networks. At the same time, integration of autonomous UAVs into next-generation wireless networks while maintaining the reliability, safety, and connectivity is a critical undertaking, and many challenges and questions remain. Several key questions in this context are the following. How would airspace constraints (e.g., safety and collision avoidance) interact with network connectivity requirements? As the number of UAVs grows and centralized control becomes prohibitive, can decentralized autonomous decision-making be achieved for mobility and connectivity management in aerial networks? Can decentralized/distributed resource allocation policies be developed and how would these policies impact mobility and connectivity management? What is the sensitivity of decision-making policies to adversarial attacks? Can robust learning and secure decision-making be achieved in aerial networks in the presence of adversarial attacks? <br/><br/>Hence, there exists a critical knowledge gap, and this project addresses this gap via interconnected thrusts. Specifically, the primary objective in this project is to design and analyze robust, intelligent, and decentralized decision-making policies in UAV-enabled wireless networks. To realize the vision of autonomous aerial networks, the critical considerations of mobility and connectivity management are addressed jointly with dynamic resource allocation in the challenging but practically more relevant setting of decentralized operation. In this setting, both connectivity and control constraints such as collision avoidance requirements and kinematic limitations in UAV path planning are taken into account. Decentralized and autonomous decision-making strategies are to be developed via distributed learning with only local observation/sensing. Robustness in decision-making is to be established via sensitivity analysis to adversarial attacks and the development of defensive mechanisms. Overall, the project seeks to establish a strong analytical and algorithmic framework by combining tools from wireless networking, optimization, control, deep reinforcement learning, and adversarial learning. The outcomes of this research will support applications such as disaster relief and remote sensing. Integration of this research project with education will be accomplished through newly introduced courses, and involving undergraduates in the PIs' research program.<br/><br/>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.
