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Graph Signal Processing for Sensing Applications

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (1 February 2021) | Viewed by 8829

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Special Issue Editor

Dr. Vladimir Stankovic

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Guest Editor

Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, UK
Interests: signal and information processing with applications to sensing; microseismic signal analysis; environmental signal analysis and healthcare

Special Issue Information

Dear Colleagues,

This Special Issue will present both review and original research articles related to application of graph signal processing (GSP) tools to various sensor processing tasks, including sensing, filtering, sensor data classification, clustering, anomaly detection, and prediction. The Special Issue is open to both theoretical contributions and applied articles addressing time-series signals as well as image, video, and heterogeneous signals.

Graph signal processing (GSP) is an emerging field used to represent irregular data structures on graphs. GSP extends classical digital signal processing (DSP) to signals on graphs by combining algebraic and spectral graph theory with DSP and provides a potential solution to numerous real-world problems that involve signals defined on topologically complex domains, such as social networks, point clouds, biological networks, environmental and condition monitoring sensor networks, etc. The goal of this Special Issue is to demonstrate the potential of GSP tools for various sensing tasks, including, but not limited to sampling, distributed filtering, denoising, as well as data processing tasks, including data representation, classification and clustering, prediction, and anomaly detection.

Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

graph signal processing
graph Laplacian
classification
sensing
clustering

Published Papers (4 papers)

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Research

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22 pages, 2220 KiB

Open AccessArticle

Sequential Sampling and Estimation of Approximately Bandlimited Graph Signals

by Sijie Lin, Ke Xu, Hui Feng and Bo Hu

Sensors 2021, 21(4), 1460; https://doi.org/10.3390/s21041460 - 19 Feb 2021

Viewed by 1766

Abstract

Graph signal sampling has been widely studied in recent years, but the accurate signal models required by most of the existing sampling methods are usually unavailable prior to any observations made in a practical environment. In this paper, a sequential sampling and estimation algorithm is proposed for approximately bandlimited graph signals, in the absence of prior knowledge concerning signal properties. We approach the problem from a Bayesian perspective in which we formulate the signal prior by a multivariate Gaussian distribution with unknown hyperparameters. To overcome the interconnected problems associated with the parameter estimation, in the proposed algorithm, hyperparameter estimation and sample selection are performed in an alternating way. At each step, the unknown hyperparameters are updated by an expectation maximization procedure based on historical observations, and then the next node in the sampling operation is chosen by uncertainty sampling with the latest hyperparameters. We prove that under some specific conditions, signal estimation in the proposed algorithm is consistent. Subsequent validation of the approach through simulations shows that the proposed procedure yields performances which are significantly better than existing state-of-the-art approaches notwithstanding the additional attribute of robustness in the presence of a broad range of signal attributes. Full article

(This article belongs to the Special Issue Graph Signal Processing for Sensing Applications)

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13 pages, 1262 KiB

Open AccessArticle

Near-Optimal Graph Signal Sampling by Pareto Optimization

by Dongqi Luo, Binqiang Si, Saite Zhang, Fan Yu and Jihong Zhu

Sensors 2021, 21(4), 1415; https://doi.org/10.3390/s21041415 - 18 Feb 2021

Viewed by 1854

Abstract

In this paper, we focus on the bandlimited graph signal sampling problem. To sample graph signals, we need to find small-sized subset of nodes with the minimal optimal reconstruction error. We formulate this problem as a subset selection problem, and propose an efficient Pareto Optimization for Graph Signal Sampling (POGSS) algorithm. Since the evaluation of the objective function is very time-consuming, a novel acceleration algorithm is proposed in this paper as well, which accelerates the evaluation of any solution. Theoretical analysis shows that POGSS finds the desired solution in quadratic time while guaranteeing nearly the best known approximation bound. Empirical studies on both Erdos-Renyi graphs and Gaussian graphs demonstrate that our method outperforms the state-of-the-art greedy algorithms. Full article

(This article belongs to the Special Issue Graph Signal Processing for Sensing Applications)

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16 pages, 335 KiB

Open AccessArticle

Building a Graph Signal Processing Model Using Dynamic Time Warping for Load Disaggregation

by Kanghang He, Vladimir Stankovic and Lina Stankovic

Sensors 2020, 20(22), 6628; https://doi.org/10.3390/s20226628 - 19 Nov 2020

Cited by 9 | Viewed by 2247

Abstract

Building on recent unsupervised Non-intrusive load monitoring (NILM) algorithms that use graph Laplacian regularization (GLR) and achieve state-of-the-art performance, in this paper, we propose a novel unsupervised approach to design an underlying graph to model the correlation within time-series smart meter measurements. We propose a variable-length data segmentation approach to extract potential events, assign all measurements associated with an identified event to each graph node, employ dynamic time warping to define the adjacency matrix of the graph, and propose a robust cluster labeling approach. Our simulation results on four different datasets show up to 10% improvement in classification performance over competing approaches. Full article

(This article belongs to the Special Issue Graph Signal Processing for Sensing Applications)

Other

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12 pages, 512 KiB

Open AccessLetter

Bandwidth Detection of Graph Signals with a Small Sample Size

by Xuan Xie, Hui Feng and Bo Hu

Sensors 2021, 21(1), 146; https://doi.org/10.3390/s21010146 - 28 Dec 2020

Cited by 2 | Viewed by 1725

Abstract

Bandwidth is the crucial knowledge to sampling, reconstruction or estimation of the graph signal (GS). However, it is typically unknown in practice. In this paper, we focus on detecting the bandwidth of bandlimited GS with a small sample size, where the number of spectral components of GS to be tested may greatly exceed the sample size. To control the significance of the result, the detection procedure is implemented by multi-stage testing. In each stage, a Bayesian score test, which introduces a prior to the spectral components, is adopted to face the high dimensional challenge. By setting different priors in each stage, we make the test more powerful against alternatives that have similar bandwidth to the null hypothesis. We prove that the Bayesian score test is locally most powerful in expectation against the alternatives following the given prior. Finally, numerical analysis shows that our method has a good performance in bandwidth detection and is robust to the noise. Full article

(This article belongs to the Special Issue Graph Signal Processing for Sensing Applications)

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