The goal of this program is to study the effects of a dynamic mobile communication environment on the computational performance of certain classes of decentralized algorithms in the presence of volatile wireless links and time-varying network topologies. These are algorithms that are inherently critical for various high-level distributed autonomy tasks, for instance online real-time machine learning, control, planning, localization and mapping in collaborative multi-agent platforms such as teams of Unmanned Aerial Vehicles (UAVs), or other autonomous robotic systems. The main focus will be on algorithms for decentralized numerical optimization, task allocation, and graph signal processing. We are particularly interested in evaluating and quantifying the algorithms' performance in terms of throughput, robustness, and resilience under various realistic scenarios, as well as in developing rigorous empirical models and guidelines for future deployment on similar architectures.
In order to ensure robust, reproducible experimental environement, an integrated mobile multia-agent system emulator with realistic communication capabilities will be developed and will serve as a virtual testbed protoype.This research will involve theoretical algorithm analysis, algorithm development and implementation, conducting mobile network experiments and evaluations, innovative data analysis, model extraction and validation, as well as presentations and communicating research findings.
 A. Nedic, A. Olshevsky and M. G. Rabbat, "Network Topology and Communication-Computation Tradeoffs in Decentralized Optimization," in Proceedings of the IEEE, vol. 106, no. 5, pp. 953-976, May 2018.
 Xin R, Pu S, Nedic A, Khan UA. A General Framework for Decentralized Optimization with First-Order Methods. Proceedings of the IEEE. 2020 Nov;108(11):1869-1889. 9241497. doi: 10.1109/JPROC.2020.3024266
Collaborative autonomy; Distributed computing; Autonomous system validation and evaluation; Mobile computing; Decentralized learning; Consensus-based distributed optimization; Decentralized algorithms; Networked UAVs; Robotic communication; Multi-agent autonomous systems