Computational and Data Science Ph.D. student, Robert Dohner, presents an overview of algorithms for computing open-locating-dominating (OLD) sets, a graph-based framework for fault detection and sensor placement in networks.
This seminar introduces Infrastructure as Code (IaC) and discusses its relevance to data science research. Using Terraform as an example, the talk demonstrates how computing environments and machine learning workflows can be defined and deployed programmatically in the cloud.
For a graph G, distinguishing sets can be used for detection purposes. Whether it be setting up sensors to detect a thief in a facility or detecting a faulty component in a network of processors, these types of sets can be used to minimize the number of sensors required for the grid or network. Recently, there has been a lot of research into dealing with distinguishing sets, including on open-locating-dominating (OLD) sets, which is the graphical parameter this research focuses on. For our research, programs were developed to find OLD sets and the value of OLD(G) for various classes of graphs. In particular, in order to find the minimum density of an OLD set for the infinite king’s graph, an algorithm was created that parallelized the program using a backtracking algorithm, and the program has confirmed the previous results. Additionally, we present a proof for the NP-completeness of a fault-tolerant OLD set called a redundant OLD set.
Watch the webinar here.