2017/02/16 by Amirali Aghazadeh, Mohammad Golbabaee, Aghazadeh, Amirali +5 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Gene expression and cancer classification #Other Computer Science (cs.OH) #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.1702.07670
openalex publication_date 2017/02/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.