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Signal and Information Processing in Wireless Sensor Networks

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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 (20 January 2019) | Viewed by 22425

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Special Issue Editors

Dr. Paul Honeine

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Guest Editor

LITIS Lab, Université de Rouen Normandie, 76000 Rouen, France
Interests: machine learning and pattern recognition for signal and image processing; localization and tracking in wireless sensor networks; collaborative information processing; distributed algorithms
Special Issues, Collections and Topics in MDPI journals

Dr. Farah Mourad-Chehade

E-Mail Website
Guest Editor

LIST3N-Laboratoire d'Informatique et Société Numérique, Université de Technologie de Troyes (UTT), 10004 Troyes, France
Interests: artificial intelligence; data mining; pattern recognition; wireless sensor networks; sensors localization; biomedical signal processing; prediction and diagnoses of pathologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wireless Sensor Networks (WSNs) have attracted worldwide interest in the last two decades. Advances in communications, computations and miniaturization have led to their emergence and use in a large range of applications. WSNs are composed of smart nodes able to sense, process, collect, store, and share data. The main objective of using WSN is to create and monitor intelligent fields for military applications, environmental engineering, healthcare, industry, smart home, etc.

The aim of this Special Issue is to collect original papers, covering unpublished research, from academic and industrial actors. The fields of interest of this Special Issue are signal and information processing in all types of WSNs, from small-scale networks, such as wireless personal area networks, wireless body area networks and Internet of Things, to large-scale ad hoc networks, such as vehicular ad hoc networks and unmanned aerial vehicular ad hoc networks.

Topics of interest in signal and information processing include, but are not limited to:

Machine learning algorithms
Distributed optimization algorithms
Spectrum sensing and cognitive radio networks
Energy-efficient algorithms
Sensor data fusion
Big data processing
Biomedical signal processing
Pattern recognition and analysis
Other applications of WSN and industrial experiences

Prof. Dr. Paul Honeine
Dr. Farah Chehade
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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

Signal and information processing
Data fusion
Wireless sensor networks
Machine learning
Distributed algorithms
Internet of Things

Published Papers (6 papers)

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Research

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15 pages, 2003 KiB

Open AccessArticle

An Optimized Node Deployment Solution Based on a Virtual Spring Force Algorithm for Wireless Sensor Network Applications

by Xiaohua Deng, Zhiyong Yu, Rongxin Tang, Xin Qian, Kai Yuan and Shiyun Liu

Sensors 2019, 19(8), 1817; https://0-doi-org.brum.beds.ac.uk/10.3390/s19081817 - 16 Apr 2019

Cited by 33 | Viewed by 3497

Abstract

How to effectively deploy all wireless sensors and save a system’s energy consumption is a key issue in current wireless sensor network (WSN) applications. Theoretical analysis has proven that a hexagonal structure is the best topology in the two-dimensional network, which can provide the maximum coverage area with the minimum number of sensor nodes and minimum energy consumption. Recently, many scientists presented their self-deployment strategies based on different virtual forces and discussed the corresponding efficiency via several case studies. However, according to our statistical analysis, some virtual force algorithms, e.g., virtual spring force, can still cause holes or twisted structure in a small region of the final network distribution, which cannot achieve the ideal network topology and will waste the system energy in real applications. In this paper, we first statistically analyzed the convergence and deployment effect of the virtual spring force algorithm to derive our question. Then we presented an optimized strategy that sensor deployment begins from the center of the target region by adding an external central force. At the early stage, the external force will be added to the most peripheral nodes to promote the formation of hexagonal topology and avoid covering holes or unusual structure. Finally, a series of independent simulation experiments and corresponding statistical results proved that our optimized deployment solution is very stable and effective, which can improve the energy consumption of the whole sensor network and be used in the application of a large scale WSN. Full article

(This article belongs to the Special Issue Signal and Information Processing in Wireless Sensor Networks)

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31 pages, 9155 KiB

Open AccessArticle

2D Triangulation of Signals Source by Pole-Polar Geometric Models

by Aleksandro Montanha, Airton M. Polidorio, F. J. Dominguez-Mayo and María J. Escalona

Sensors 2019, 19(5), 1020; https://0-doi-org.brum.beds.ac.uk/10.3390/s19051020 - 27 Feb 2019

Cited by 5 | Viewed by 6000

Abstract

The 2D point location problem has applications in several areas, such as geographic information systems, navigation systems, motion planning, mapping, military strategy, location and tracking moves. We aim to present a new approach that expands upon current techniques and methods to locate the 2D position of a signal source sent by an emitter device. This new approach is based only on the geometric relationship between an emitter device and a system composed of

m \geq 2

signal receiving devices. Current approaches applied to locate an emitter can be deterministic, statistical or machine-learning methods. We propose to perform this triangulation by geometric models that exploit elements of pole-polar geometry. For this purpose, we are presenting five geometric models to solve the point location problem: (1) based on centroid of points of pole-polar geometry, PPC; (2) based on convex hull region among pole-points, CHC; (3) based on centroid of points obtained by polar-lines intersections, PLI; (4) based on centroid of points obtained by tangent lines intersections, TLI; (5) based on centroid of points obtained by tangent lines intersections with minimal angles, MAI. The first one has computational cost

O (n)

and whereas has the computational cost

O (n l o g n)

where

n

is the number of points of interest. Full article

(This article belongs to the Special Issue Signal and Information Processing in Wireless Sensor Networks)

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20 pages, 450 KiB

Open AccessArticle

Rate-Distortion Performance and Incremental Transmission Scheme of Compressive Sensed Measurements in Wireless Sensor Networks

by Felipe Da Rocha Henriques, Lisandro Lovisolo and Eduardo Antônio Barros da Silva

Sensors 2019, 19(2), 266; https://0-doi-org.brum.beds.ac.uk/10.3390/s19020266 - 11 Jan 2019

Cited by 4 | Viewed by 2818

Abstract

We consider a Wireless Sensor Network (WSN) monitoring environmental data. Compressive Sensing (CS) is explored to reduce the number of coefficients to transmit and consequently save the energy of sensor nodes. Each sensor node collects N samples of environmental data, these are CS [...] Read more.

M < N

values to a sink node. The M CS coefficients are uniformly quantized and entropy coded. We investigate the rate-distortion performance of this approach even under CS coefficient losses. The results show the robustness of the CS coding framework against packet loss. We devise a simple strategy to successively approximate/quantize CS coefficients, allowing for an efficient incremental transmission of CS coded data. Tests show that the proposed successive approximation scheme provides rate allocation adaptivity and flexibility with a minimum rate-distortion performance penalty. Full article

(This article belongs to the Special Issue Signal and Information Processing in Wireless Sensor Networks)

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21 pages, 3512 KiB

Open AccessArticle

Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks

by Xing Hu, Linhua Ma, Yongqiang Ding, Jin Xu, Yan Li and Shiping Ma

Sensors 2019, 19(1), 196; https://0-doi-org.brum.beds.ac.uk/10.3390/s19010196 - 07 Jan 2019

Cited by 22 | Viewed by 4067

Abstract

The geographic routing protocol only requires the location information of local nodes for routing decisions, and is considered very efficient in multi-hop wireless sensor networks. However, in dynamic wireless sensor networks, it increases the routing overhead while obtaining the location information of destination nodes by using a location server algorithm. In addition, the routing void problem and location inaccuracy problem also occur in geographic routing. To solve these problems, a novel fuzzy logic-based geographic routing protocol (FLGR) is proposed. The selection criteria and parameters for the assessment of the next forwarding node are also proposed. In FLGR protocol, the next forward node can be selected based on the fuzzy location region of the destination node. Finally, the feasibility of the FLGR forwarding mode is verified and the performance of FLGR protocol is analyzed via simulation. Simulation results show that the proposed FLGR forwarding mode can effectively avoid the routing void problem. Compared with existing protocols, the FLGR protocol has lower routing overhead, and a higher packet delivery rate in a sparse network. Full article

(This article belongs to the Special Issue Signal and Information Processing in Wireless Sensor Networks)

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15 pages, 844 KiB

Open AccessArticle

Diffusion Logarithm-Correntropy Algorithm for Parameter Estimation in Non-Stationary Environments over Sensor Networks

by Limei Hu, Feng Chen, Shukai Duan and Lidan Wang

Sensors 2018, 18(10), 3381; https://0-doi-org.brum.beds.ac.uk/10.3390/s18103381 - 10 Oct 2018

Cited by 2 | Viewed by 2354

Abstract

This paper considers the parameter estimation problem under non-stationary environments in sensor networks. The unknown parameter vector is considered to be a time-varying sequence. To further promote estimation performance, this paper suggests a novel diffusion logarithm-correntropy algorithm for each node in the network. Such an algorithm can adopt both the logarithm operation and correntropy criterion to the estimation error. Moreover, if the error gets larger due to the non-stationary environments, the algorithm can respond immediately by taking relatively steeper steps. Thus, the proposed algorithm achieves smaller error in time. The tracking performance of the proposed logarithm-correntropy algorithm is analyzed. Finally, experiments verify the validity of the proposed algorithmic schemes, which are compared to other recent algorithms that have been proposed for parameter estimation. Full article

(This article belongs to the Special Issue Signal and Information Processing in Wireless Sensor Networks)

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Review

Jump to: Research

27 pages, 3150 KiB

Open AccessReview

On Consensus-Based Distributed Blind Calibration of Sensor Networks

by Miloš S. Stanković, Srdjan S. Stanković, Karl Henrik Johansson, Marko Beko and Luis M. Camarinha-Matos

Sensors 2018, 18(11), 4027; https://0-doi-org.brum.beds.ac.uk/10.3390/s18114027 - 19 Nov 2018

Cited by 12 | Viewed by 2839

Abstract

This paper deals with recently proposed algorithms for real-time distributed blind macro-calibration of sensor networks based on consensus (synchronization). The algorithms are completely decentralized and do not require a fusion center. The goal is to consolidate all of the existing results on the subject, present them in a unified way, and provide additional important analysis of theoretical and practical issues that one can encounter when designing and applying the methodology. We first present the basic algorithm which estimates local calibration parameters by enforcing asymptotic consensus, in the mean-square sense and with probability one (w.p.1), on calibrated sensor gains and calibrated sensor offsets. For the more realistic case in which additive measurement noise, communication dropouts and additive communication noise are present, two algorithm modifications are discussed: one that uses a simple compensation term, and a more robust one based on an instrumental variable. The modified algorithms also achieve asymptotic agreement for calibrated sensor gains and offsets, in the mean-square sense and w.p.1. The convergence rate can be determined in terms of an upper bound on the mean-square error. The case when the communications between nodes is completely asynchronous, which is of substantial importance for real-world applications, is also presented. Suggestions for design of a priori adjustable weights are given. We also present the results for the case in which the underlying sensor network has a subset of (precalibrated) reference sensors with fixed calibration parameters. Wide applicability and efficacy of these algorithms are illustrated on several simulation examples. Finally, important open questions and future research directions are discussed. Full article

(This article belongs to the Special Issue Signal and Information Processing in Wireless Sensor Networks)

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