  Existing network analysis methods struggle to optimize observer placements in
dynamic environments with limited visibility. This dissertation introduces the
novel ROBUST (Ranged Observer Bipartite-Unipartite SpatioTemporal) framework,
offering a significant advancement in modeling, analyzing, and optimizing
observer networks within complex spatiotemporal domains. ROBUST leverages a
unique bipartite-unipartite approach, distinguishing between observer and
observable entities while incorporating spatial constraints and temporal
dynamics.
  This research extends spatiotemporal network theory by introducing novel
graph-based measures, including myopic degree, spatial closeness centrality,
and edge length proportion. These measures, coupled with advanced clustering
techniques like Proximal Recurrence, provide insights into network structure,
resilience, and the effectiveness of observer placements. The ROBUST framework
demonstrates superior resource allocation and strategic responsiveness compared
to conventional models. Case studies in oceanographic monitoring, urban safety
networks, and multi-agent path planning showcases its practical applicability
and adaptability. Results demonstrate significant improvements in coverage,
response times, and overall network efficiency.
  This work paves the way for future research in incorporating imperfect
knowledge, refining temporal pathing methodologies, and expanding the scope of
applications. By bridging theoretical advancements with practical solutions,
ROBUST stands as a significant contribution to the field, promising to inform
and inspire ongoing and future endeavors in network optimization and
multi-agent system planning.
