Greedy Sensor Selection: Leveraging Submodularity Based on Volume Ratio of Information Ellipsoid

This article focuses on greedy approaches to select the most informative k sensors from N candidates to maximize the Fisher information, i.e., the determinant of the Fisher information matrix (FIM), which indicates the volume of the information ellipsoid (VIE) constructed by the FIM. However, it is a critical issue for conventional greedy...

Sensor Selection for Angle of Arrival Estimation Based on the Two-Target Cramér-Rao Bound

Sensor selection is a useful method to help reduce data throughput, as well as computational, power, and hardware requirements, while still maintaining acceptable performance. Although minimizing the Cramér-Rao bound has been adopted previously for sparse sensing, it did not consider multiple targets and unknown source models. In this work, we...

Array Design Based on the Worst-Case Cramér-Rao Bound to Account for Multiple Targets

Sensor selection is a useful method to help reduce computational, hardware, and power requirements while maintaining acceptable performance. Although minimizing the Cramér-Rao bound has been adopted previously for sparse sensing, it did not consider multiple targets and unknown target directions. We propose to tackle the sensor selection problem...

Monitoring structural responses during load testing of reinforced concrete bridges: a review

Nowadays, with the aging of the bridges and the advancements in technology, load testing has emerged as an effective method to assess existing concrete bridges with missing information, or where analytical methods do not provide an accurate assessment. Two types of load tests are identified: diagnostic load tests and proof load tests. Both...

Sensor placement and resource allocation for energy harvesting IoT networks

Optimal sensor selection for source parameter estimation in energy harvesting Internet of Things (IoT) networks is studied in this paper. Specifically, the focus is on the selection of the sensor locations which minimizes the estimation error at a fusion center, and to optimally allocate power and bandwidth for each selected sensor subject to...

Sensor selection and rate distribution based beamforming in wireless acoustic sensor networks

Power usage is an important aspect of wireless acoustic sensor networks (WASNs) and reducing the amount of information that is to be transmitted is one effective way to save it. In previous contributions, we presented sensor selection as well as rate distribution methods to reduce the power usage of beamforming algorithms in WASNs. Taking...

Sampling Design of Synthetic Volume Arrays for Three-Dimensional Microwave Imaging

In this paper, sampling design of three-dimensional (3-D) synthetic array (i.e., synthetic volume array) for microwave imaging is considered. Generally, the spatial sampling criteria for one- or two-dimensional arrays can be determined based on some narrowband/ultrawideband array theories. However, for 3-D arrays, where antennas are located in a...

Sparse sensing for composite matched subspace detection

In this paper, we propose sensor selection strategies, based on convex and greedy approaches, for designing sparse samplers for composite detection. Particularly, we focus our attention on sparse samplers for matched subspace detectors. Differently from previous works, that mostly rely on random matrices to perform compression of the sub...

Exploiting submodular value functions for scaling up active perception

In active perception tasks, an agent aims to select sensory actions that reduce its uncertainty about one or more hidden variables. For example, a mobile robot takes sensory actions to efficiently navigate in a new environment. While partially observable Markov decision processes (POMDPs) provide a natural model for such problems, reward...

Rate-Distributed Spatial Filtering Based Noise Reduction in Wireless Acoustic Sensor Networks

In wireless acoustic sensor networks (WASNs), sensors typically have a limited energy budget as they are often battery driven. Energy efficiency is therefore essential to the design of algorithms in WASNs. One way to reduce energy costs is to only select the sensors which are most informative, a problem known as sensor selection. In this way,...

Submodular Sparse Sensing for Gaussian Detection with Correlated Observations

Detection of a signal under noise is a classical signal processing problem. When monitoring spatial phenomena under a fixed budget, i.e., either physical, economical or computational constraints, the selection of a subset of available sensors, referred to as sparse sensing, that meets both the budget and performance requirements is highly...

Subset Selection for Kernel-Based Signal Reconstruction

In this work, we introduce subset selection strategies for signal reconstruction based on kernel methods, particularly for the case of kernel-ridge regression. Typically, these methods are employed for exploiting known prior information about the structure of the signal of interest. We use the mean squared error and a scalar function of the...

Consistent sensor, relay, and link selection in wireless sensor networks

In wireless sensor networks, where energy is scarce, it is inefficient to have all nodes active because they consume a non-negligible amount of battery. In this paper we consider the problem of jointly selecting sensors, relays and links in a wireless sensor network where the active sensors need to communicate their measurements to one or...

Sparsity-Aware Wireless Networks: Localization and Sensor Selection

Wireless networks have revolutionized nowadays world by providing real time cost-efficient service and connectivity. Even such an unprecedented level of service could not fulfill the insatiable desire of the modern world for more advanced technologies. As a result, a great deal of attention has been directed towards (mobile) wireless sensor...