  We carry out a structural and algorithmic study of a mobile sensor coverage
optimization problem targeting 2D surfaces embedded in a 3D workspace. The
investigated settings model multiple important applications including camera
network deployment for surveillance, geological monitoring/survey of 3D
terrains, and UVC-based surface disinfection for the prevention of the spread
of disease agents (e.g., SARS-CoV-2). Under a unified general "sensor coverage"
problem, three concrete formulations are examined, focusing on optimizing
visibility, single-best coverage quality, and cumulative quality, respectively.
After demonstrating the computational intractability of all these formulations,
we describe approximation schemes and mathematical programming models for
near-optimally solving them. The effectiveness of our methods is thoroughly
evaluated under realistic and practical scenarios.
