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Special Issue "Sensor Network Signal Processing"

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

Deadline for manuscript submissions: 31 January 2021.

Special Issue Editors

Prof. Marko Beko
Website
Guest Editor
Universidade Lusófonade Humanidades e Tecnologias, Lisbon, Portugal
Interests: wireless communications and networking; signal processing; machine learning; sensor networks; cognitive radio; source localization; PAPR reduction; MIMO communications
Prof. Slavisa Tomic
Website
Guest Editor
Universidade Lusófona de Humanidades e Tecnologias, Portugal
Interests: target localization; non-convex optimization
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Sensing permits us to get a better comprehension of the world we live in. A sensor network consists of a number of small, low-cost, low-power devices called sensor nodes, which have some sensing, data processing, and communication capabilities. Remarkable progress in radio-frequency and micro-electro-mechanical systems integrated circuit design over the last two decades has enabled the use of wireless sensor networks with thousands of nodes. In general, these devices are deployed near the phenomenon that we desire to monitor. Sensor networks find applications in various fields, such as health care, structural and environmental monitoring, energy-efficient routing, homeland security, etc. Ad hoc deployments or the use of mobile sensors call for the autonomous organization of networks with the capability to execute distributed data processing. However, the intrinsic restrictions in battery power of individual nodes raise significant challenges in the design and development of signal-processing algorithms for sensor networks.

It is foreseen that fifth-generation networks will provide significantly higher bandwidth and faster data rates with potential for interconnecting myriads of heterogeneous devices (sensors, agents, users, machines, and vehicles) into a single network (of nodes), called Internet of Things. Hence, this Special Issue aims at promoting advanced solutions for signal processing in sensor networks in order to provide adequate support for emerging technologies.

Prof. Marko Beko
Prof. Slavisa Tomic
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 papers will be 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 2000 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

  • Distributed signal processing and analysis
  • Radionavigation and location estimation
  • Cooperative statistical signal processing and data fusion
  • New wireless communication paradigm towards edge intelligence
  • Computing and processing
  • Cooperative positioning using multi-dimensional signals and multi-agent strategies
  • Distributed machine learning and data-driven optimization
  • Machine learning and artificial intelligence approaches to sensor networks signal processing
  • Dynamic spectrum access and cognitive radio
  • Sensing, detection, and estimation in sensor networks
  • Communication, networking, and broadcast technologies
  • Wireless-specific security, privacy, and authentication
  • Applications of sensor networks signal processing in multi-agent contexts (social networks, smart agriculture, smart factory, smart grids, smart cities)

Published Papers (6 papers)

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Research

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Open AccessArticle
Particle Filtering for Three-Dimensional TDoA-Based Positioning Using Four Anchor Nodes
Sensors 2020, 20(16), 4516; https://doi.org/10.3390/s20164516 - 12 Aug 2020
Abstract
In this article, the four-anchor time difference of arrival (TDoA)-based three-dimensional (3D) positioning by particle filtering is addressed. The implemented particle filter uses 1000 particles to represent the probability density function (pdf) of interest, i.e., the posterior pdf of the target node’s state [...] Read more.
In this article, the four-anchor time difference of arrival (TDoA)-based three-dimensional (3D) positioning by particle filtering is addressed. The implemented particle filter uses 1000 particles to represent the probability density function (pdf) of interest, i.e., the posterior pdf of the target node’s state (position). A resampling procedure is used to generate particles in the prediction step, and TDoA measurements are used to determine the importance, i.e., weight, of each particle to enable updating the posterior pdf and estimating the position of the target node. The simulation results show the feasibility of this approach and the possibility to employ it in indoor positioning applications under the assumed working conditions using, e.g., the ultra-wideband (UWB) wireless technology. Therefore, it is possible to enable unmanned air vehicle (UAV) positioning applications, e.g., inventory management in large warehouses, without the need for an excessive number of anchor nodes. Full article
(This article belongs to the Special Issue Sensor Network Signal Processing)
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Open AccessArticle
Macrodiversity Reception with Distributed Hard-Decision Receivers for Maritime Wireless Sensor Networks
Sensors 2020, 20(14), 3925; https://doi.org/10.3390/s20143925 - 15 Jul 2020
Abstract
Maritime wireless sensor networks are considered to be the primary means of monitoring methods in the marine environment. The transmission between sensor node and sink node in maritime wireless sensor networks is usually unreliable due to the harsh propagation environment. To extend the [...] Read more.
Maritime wireless sensor networks are considered to be the primary means of monitoring methods in the marine environment. The transmission between sensor node and sink node in maritime wireless sensor networks is usually unreliable due to the harsh propagation environment. To extend the transmission range or to enhance the transmission reliability between sensor nodes and sink node, we propose a macrodiversity reception scheme in the sink node equipped with distributed multiple hard-decision receivers. Multiple receivers are divided into several clusters and placed at different locations to receive different signal copies suffering from different fadings. Furthermore, a cascaded combining strategy based on hard-decision information is used to reduce the overall complexity of receiving side. The experimental results in the ocean scenarios show that the macrodiversity reception scheme with two antenna clusters has a transmission gain of 3–4 dB compared with the single antenna reception when the package loss rate is 10 2 . The study casts a new method for reliable transmission in maritime wireless sensor networks using commercial transceivers which can only output hard-decision results. Full article
(This article belongs to the Special Issue Sensor Network Signal Processing)
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Open AccessArticle
Crowd-Based Cognitive Perception of the Physical World: Towards the Internet of Senses
Sensors 2020, 20(9), 2437; https://doi.org/10.3390/s20092437 - 25 Apr 2020
Cited by 1
Abstract
This paper introduces a possible architecture and discusses the research directions for the realization of the Cognitive Perceptual Internet (CPI), which is enabled by the convergence of wired and wireless communications, traditional sensor networks, mobile crowd-sensing, and machine learning techniques. The CPI concept [...] Read more.
This paper introduces a possible architecture and discusses the research directions for the realization of the Cognitive Perceptual Internet (CPI), which is enabled by the convergence of wired and wireless communications, traditional sensor networks, mobile crowd-sensing, and machine learning techniques. The CPI concept stems from the fact that mobile devices, such as smartphones and wearables, are becoming an outstanding mean for zero-effort world-sensing and digitalization thanks to their pervasive diffusion and the increasing number of embedded sensors. Data collected by such devices provide unprecedented insights into the physical world that can be inferred through cognitive processes, thus originating a digital sixth sense. In this paper, we describe how the Internet can behave like a sensing brain, thus evolving into the Internet of Senses, with network-based cognitive perception and action capabilities built upon mobile crowd-sensing mechanisms. The new concept of hyper-map is envisioned as an efficient geo-referenced repository of knowledge about the physical world. Such knowledge is acquired and augmented through heterogeneous sensors, multi-user cooperation and distributed learning mechanisms. Furthermore, we indicate the possibility to accommodate proactive sensors, in addition to common reactive sensors such as cameras, antennas, thermometers and inertial measurement units, by exploiting massive antenna arrays at millimeter-waves to enhance mobile terminals perception capabilities as well as the range of new applications. Finally, we distillate some insights about the challenges arising in the realization of the CPI, corroborated by preliminary results, and we depict a futuristic scenario where the proposed Internet of Senses becomes true. Full article
(This article belongs to the Special Issue Sensor Network Signal Processing)
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Open AccessArticle
Signal Processing and Target Fusion Detection via Dual Platform Radar Cooperative Illumination
Sensors 2019, 19(24), 5341; https://doi.org/10.3390/s19245341 - 04 Dec 2019
Cited by 2
Abstract
A modified signal processing and target fusion detection method based on the dual platform cooperative detection model is proposed in this paper. In this model, a single transmitter and dual receiver radar system is adopted, which can form a single radar and bistatic [...] Read more.
A modified signal processing and target fusion detection method based on the dual platform cooperative detection model is proposed in this paper. In this model, a single transmitter and dual receiver radar system is adopted, which can form a single radar and bistatic radar system, respectively. Clutter suppression is achieved by an adaptive moving target indicator (AMTI). By combining the AMTI technology and the traditional radar signal processing technology (i.e., pulse compression and coherent accumulation processing), the SNR is improved, and false targets generated by direct wave are suppressed. The decision matrix is obtained by cell averaging constant false alarm (CA-CFAR) and order statistics constant false alarm (OS-CFAR) processing. Then, the echo signals processed in the two receivers are fused by the AND-like fusion rule and OR-like fusion rule, and the detection probability after fusion detection in different cases is analyzed. Finally, the performance of the proposed method is quantitatively analyzed. Experimental results based on simulated data demonstrate that: (1) The bistatic radar system with a split transceiver has a larger detection distance than the single radar system, but the influence of clutter is greater; (2) the direct wave can be eliminated effectively, and no false target can be formed after suppression; (3) the detection probability of the bistatic radar system with split transceivers is higher than that of the single radar system; and (4) the detection probability of signal fusion detection based on two receivers is higher than that of the bistatic radar system and single radar system. Full article
(This article belongs to the Special Issue Sensor Network Signal Processing)
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Review

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Open AccessReview
Detection and Classification of Multirotor Drones in Radar Sensor Networks: A Review
Sensors 2020, 20(15), 4172; https://doi.org/10.3390/s20154172 - 27 Jul 2020
Abstract
Thanks to recent technological advances, a new generation of low-cost, small, unmanned aerial vehicles (UAVs) is available. Small UAVs, often called drones, are enabling unprecedented applications but, at the same time, new threats are arising linked to their possible misuse (e.g., drug smuggling, [...] Read more.
Thanks to recent technological advances, a new generation of low-cost, small, unmanned aerial vehicles (UAVs) is available. Small UAVs, often called drones, are enabling unprecedented applications but, at the same time, new threats are arising linked to their possible misuse (e.g., drug smuggling, terrorist attacks, espionage). In this paper, the main challenges related to the problem of drone identification are discussed, which include detection, possible verification, and classification. An overview of the most relevant technologies is provided, which in modern surveillance systems are composed into a network of spatially-distributed sensors to ensure full coverage of the monitored area. More specifically, the main focus is on the frequency modulated continuous wave (FMCW) radar sensor, which is a key technology also due to its low cost and capability to work at relatively long distances, as well as strong robustness to illumination and weather conditions. This paper provides a review of the existing literature on the most promising approaches adopted in the different phases of the identification process, i.e., detection of the possible presence of drones, target verification, and classification. Full article
(This article belongs to the Special Issue Sensor Network Signal Processing)
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Other

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Open AccessLetter
Efficient Estimation of CFO-Affected OFDM BER Floor in Small Cells with Resource-Limited IoT End-Points
Sensors 2020, 20(13), 3747; https://doi.org/10.3390/s20133747 - 04 Jul 2020
Abstract
Contemporary wireless networks dramatically enhance data rates and latency to become a key enabler of massive communication among various low-cost devices of limited computational power, standardized by the Long-Term Evolution (LTE) downscaled derivations LTE-M or narrowband Internet of Things (NB IoT), in particular. [...] Read more.
Contemporary wireless networks dramatically enhance data rates and latency to become a key enabler of massive communication among various low-cost devices of limited computational power, standardized by the Long-Term Evolution (LTE) downscaled derivations LTE-M or narrowband Internet of Things (NB IoT), in particular. Specifically, assessment of the physical-layer transmission performance is important for higher-layer protocols determining the extent of the potential error recovery escalation upwards the protocol stack. Thereby, it is needed that the end-points of low processing capacity most efficiently estimate the residual bit error rate (BER) solely determined by the main orthogonal frequency-division multiplexing (OFDM) impairment–carrier frequency offset (CFO), specifically in small cells, where the signal-to-noise ratio is large enough, as well as the OFDM symbol cyclic prefix, preventing inter-symbol interference. However, in contrast to earlier analytical models with computationally demanding estimation of BER from the phase deviation caused by CFO, in this paper, after identifying the optimal sample instant in a power delay profile, we abstract the CFO by equivalent time dispersion (i.e., by additional spreading of the power delay profile that would produce the same BER degradation as the CFO). The proposed BER estimation is verified by means of the industry-standard LTE software simulator. Full article
(This article belongs to the Special Issue Sensor Network Signal Processing)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

PMBM 5G mapping Using the Clustering and Assignment (C&A) Approach with Diffuse Multipath
 
Yu Ge, Fuxi Wen, Hyowon Kim, Meifang Zhu, Fan Jiang, Sunwoo Kim, Henk Wymeersch
 
5G signals have several beneficial properties for localization and mapping. Based on high-resolution estimates of multipath channel parameters, we propose a clustering method of the estimated parameters operating in a non-Euclidean space. After clustering, we propose a novel 5G mapping method that exploits geometric channel parameters (angles and delays), as well as estimated channel gains. Finally, we propose a method for fusing maps between different devices. Numerical results in a vehicular scenario with specular and diffuse multipath demonstrate the performance gains of the proposed approach over the state of the art. 
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