Title: Insense: Incoherent Sensor Selection for Sparse Signals,
Abstract: Sensor selection refers to the problem of intelligently selecting a small
subset of a collection of available sensors to reduce the sensing cost while
preserving signal acquisition performance. The majority of sensor selection
algorithms find the subset of sensors that best recovers an arbitrary signal
from a number of linear measurements that is larger than the dimension of the
signal. In this paper, we develop a new sensor selection algorithm for sparse
(or near sparse) signals that finds a subset of sensors that best recovers such
signals from a number of measurements that is much smaller than the dimension
of the signal. Existing sensor selection algorithms cannot be applied in such
situations. Our proposed Incoherent Sensor Selection (Insense) algorithm
minimizes a coherence-based cost function that is adapted from recent results
in sparse recovery theory. Using six datasets, including two real-world
datasets on microbial diagnostics and structural health monitoring, we
demonstrate the superior performance of Insense for sparse-signal sensor
selection.