  Large infrastructure networks (e.g. for transportation and power
distribution) require constant monitoring for failures, congestion, and other
adversarial events. However, assigning a sensor to every link in the network is
often infeasible due to placement and maintenance costs. Instead, sensors can
be placed only on a few key links, and machine learning algorithms can be
leveraged for the inference of missing measurements (e.g. traffic counts, power
flows) across the network. This paper investigates the sensor placement problem
for networks. We first formalize the problem under a flow conservation
assumption and show that it is NP-hard to place a fixed set of sensors
optimally. Next, we propose an efficient and adaptive greedy heuristic for
sensor placement that scales to large networks. Our experiments, using datasets
from real-world application domains, show that the proposed approach enables
more accurate inference than existing alternatives from the literature. We
demonstrate that considering even imperfect or incomplete ground-truth
estimates can vastly improve the prediction error, especially when a small
number of sensors is available.
