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Signal and Information Processing in Wireless Sensor Networks
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Signal and Information Processing in Wireless Sensor Networks

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 25351

Special Issue Editors

Dr. Paul Honeine

E-Mail Website
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The aim of this Special Issue is to collect original papers, covering unpublished research, from academic and industrial actors.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

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.

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

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Published Papers (6 papers)

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Research

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15 pages, 2003 KiB  
Article
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://doi.org/10.3390/s19081817 - 16 Apr 2019
Cited by 34 | Viewed by 4056
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 [...] Read more.
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  
Article
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://doi.org/10.3390/s19051020 - 27 Feb 2019
Cited by 5 | Viewed by 6671
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 [...] Read more.
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 ≥ 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  
Article
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://doi.org/10.3390/s19020266 - 11 Jan 2019
Cited by 5 | Viewed by 3108
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.
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 coded to transmit 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  
Article
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://doi.org/10.3390/s19010196 - 7 Jan 2019
Cited by 24 | Viewed by 4527
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 [...] Read more.
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  
Article
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://doi.org/10.3390/s18103381 - 10 Oct 2018
Cited by 2 | Viewed by 2639
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. [...] Read more.
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

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27 pages, 3150 KiB  
Review
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://doi.org/10.3390/s18114027 - 19 Nov 2018
Cited by 12 | Viewed by 3256
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 [...] Read more.
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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