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Signal Processing Techniques for Smart Sensor Communications
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Signal Processing Techniques for Smart Sensor Communications

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

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 29962

Special Issue Editors

Prof. Dr. Zahir M. Hussain

E-Mail Website
Guest Editor
School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia
Interests: signal processing; wireless sensor networks; digital communications; image processing; electronic engineering; applied mathematics
Special Issues, Collections and Topics in MDPI journals
Assoc. Prof. Dr. Iftekhar Ahmad

E-Mail Website
Guest Editor
School of Engineering, Edith Cowan University, Joondalup, WA, Australia
Interests: next generation communication networks; green communications; sensor technologies and data analytics; network security; electric vehicles; renewable energy systems
Prof. Dr. Subhas Mukhopadhyay

E-Mail Website
Guest Editor
School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
Interests: drones; robots; swarm drones; swarm robotics; IoT; smart sensors; mechatronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

For the last two decades, wireless sensor networks (WSNs) have had a significant impact on various fields including smart cities, health-care, intelligent home automation, industrial monitoring and control. WSNs are expected to be integrated in the Internet of Things (IoT) and will continue to have a significant impact on various disciplines for the next few decades. A wireless sensor network is a group of sensors dedicated to monitoring certain physical conditions of the environment, such as temperature, humidity, and pressure. These sensors communicate wirelessly in an ad hoc configuration. Due to their limited storage and computation capabilities, the design of WSNs requires special attention and ordinary techniques of signal processing and communications may not be directly applicable to WSNs. In addition, the performance of WSNs is directly related to the sensor network topology. This Special Issue of Sensors will focus on state-of-the-art design techniques that support efficient signal transmission over WSNs to enable higher data rates suitable for future applications, including image transmission, information security, and the IoT.

Topics to be covered include, but are not limited to the following:

  • Energy efficient techniques for WSNs
  • Node localization in WSNs
  • Security in WSNs
  • Mobile ad hoc WSNs
  • 3D WSNs
  • Optical WSNs
  • Efficient wireless biomedicals networks (WBANs)
  • WSNs for condition monitoring of industrial systems
  • WSNs for electronic performance measuring and tracking systems (in sports, health, etc.)
  • WSNs for industrial IoT (IIoT) and industry applications
  • Artificial intelligence for WSNs

Prof. Dr. Zahir M. Hussain
Assoc. Prof. Dr. Iftekhar Ahmad
Prof. Dr. Subhas Mukhopadhyay
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

  • wireless sensor network (WSN)
  • signal processing
  • ad hoc networks
  • 3-dimensional WSN
  • communications
  • WNS security
  • wireless biomedical sensor networks (WBSMs)
  • optical sensor networks
  • industrial sensor networks
  • artificial intelligence

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Published Papers (7 papers)

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Research

35 pages, 718 KiB  
Article
Security Requirements for the Internet of Things: A Systematic Approach
by Shantanu Pal, Michael Hitchens, Tahiry Rabehaja and Subhas Mukhopadhyay
Sensors 2020, 20(20), 5897; https://doi.org/10.3390/s20205897 - 19 Oct 2020
Cited by 82 | Viewed by 10145
Abstract
There has been a tremendous growth in the number of smart devices and their applications (e.g., smart sensors, wearable devices, smart phones, smart cars, etc.) in use in our everyday lives. This is accompanied by a new form of interconnection between the physical [...] Read more.
There has been a tremendous growth in the number of smart devices and their applications (e.g., smart sensors, wearable devices, smart phones, smart cars, etc.) in use in our everyday lives. This is accompanied by a new form of interconnection between the physical and digital worlds, commonly known as the Internet of Things (IoT). This is a paradigm shift, where anything and everything can be interconnected via a communication medium. In such systems, security is a prime concern and protecting the resources (e.g., applications and services) from unauthorized access needs appropriately designed security and privacy solutions. Building secure systems for the IoT can only be achieved through a thorough understanding of the particular needs of such systems. The state of the art is lacking a systematic analysis of the security requirements for the IoT. Motivated by this, in this paper, we present a systematic approach to understand the security requirements for the IoT, which will help designing secure IoT systems for the future. In developing these requirements, we provide different scenarios and outline potential threats and attacks within the IoT. Based on the characteristics of the IoT, we group the possible threats and attacks into five areas, namely communications, device/services, users, mobility and integration of resources. We then examine the existing security requirements for IoT presented in the literature and detail our approach for security requirements for the IoT. We argue that by adhering to the proposed requirements, an IoT system can be designed securely by achieving much of the promised benefits of scalability, usability, connectivity, and flexibility in a practical and comprehensive manner. Full article
(This article belongs to the Special Issue Signal Processing Techniques for Smart Sensor Communications)
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20 pages, 5782 KiB  
Article
Recognizing Non-Collaborative Radio Station Communication Behaviors Using an Ameliorated LeNet
by Zilong Wu, Hong Chen and Yingke Lei
Sensors 2020, 20(15), 4320; https://doi.org/10.3390/s20154320 - 3 Aug 2020
Cited by 9 | Viewed by 2742
Abstract
This work improves a LeNet model algorithm based on a signal’s bispectral features to recognize the communication behaviors of a non-collaborative short-wave radio station. At first, the mapping relationships between the burst waveforms and the communication behaviors of a radio station are analyzed. [...] Read more.
This work improves a LeNet model algorithm based on a signal’s bispectral features to recognize the communication behaviors of a non-collaborative short-wave radio station. At first, the mapping relationships between the burst waveforms and the communication behaviors of a radio station are analyzed. Then, bispectral features of simulated behavior signals are obtained as the input of the network. With regard to the recognition neural network, the structure of LeNet and the size of the convolutional kernel in LeNet are optimized. Finally, the five types of communication behavior are recognized by using the improved bispectral estimation matrix of signals and the ameliorated LeNet. The experimental results show that when the signal-to-noise ratio (SNR) values are 8, 10, or 15 dB, the recognition accuracy values of the improved algorithm reach 81.5%, 94.5%, and 99.3%, respectively. Compared with other algorithms, the training time cost and recognition accuracy of the proposed algorithm are lower and higher, respectively; thus, the proposed algorithm is of great practical value. Full article
(This article belongs to the Special Issue Signal Processing Techniques for Smart Sensor Communications)
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20 pages, 6379 KiB  
Article
Multi-Stage Pedestrian Positioning Using Filtered WiFi Scanner Data in an Urban Road Environment
by Zilin Huang, Lunhui Xu and Yongjie Lin
Sensors 2020, 20(11), 3259; https://doi.org/10.3390/s20113259 - 8 Jun 2020
Cited by 21 | Viewed by 3002
Abstract
Since widespread applications of wireless sensors networks, low-speed traffic positioning based on the received signal strength indicator (RSSI) from personal devices with WiFi broadcasts has attracted considerable attention. This study presents a new range-based localization method for outdoor pedestrian positioning by using the [...] Read more.
Since widespread applications of wireless sensors networks, low-speed traffic positioning based on the received signal strength indicator (RSSI) from personal devices with WiFi broadcasts has attracted considerable attention. This study presents a new range-based localization method for outdoor pedestrian positioning by using the combination of offline RSSI distance estimation and real-time continuous position fitting, which can achieve high-position accuracy in the urban road environment. At the offline stage, the piecewise polynomial regression model (PPRM) is proposed to formulate the Euclidean distance between the targets and WiFi scanners by replacing the common propagation model (PM). The online stage includes three procedures. Firstly, a constant velocity Kalman filter (CVKF) is developed to smooth the real-time RSSI time series and estimate the target-detector distance. Then, a least squares Taylor series expansion (LS-TSE) is developed to calculate the actual 2-dimensional coordinate with the replacement of existing trilateral localization. Thirdly, a trajectory-based technique of the unscented Kalman filter (UKF) is introduced to smooth estimated positioning points. In tests that used field scenarios from Guangzhou, China, the experiments demonstrate that the combined CVKF and PPRM can achieve the highly accurate distance estimator of <1.98 m error with the probability of 90% or larger, which outperforms the existing propagation model. In addition, the online method can achieve average positioning error of 1.67 m with the much better than classical methods. Full article
(This article belongs to the Special Issue Signal Processing Techniques for Smart Sensor Communications)
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22 pages, 652 KiB  
Article
Tracking by Risky Particle Filtering over Sensor Networks
by Jaechan Lim and Hyung-Min Park
Sensors 2020, 20(11), 3109; https://doi.org/10.3390/s20113109 - 31 May 2020
Cited by 4 | Viewed by 2337
Abstract
The system of wireless sensor networks is high of interest due to a large number of demanded applications, such as the Internet of Things (IoT). The positioning of targets is one of crucial problems in wireless sensor networks. Particularly, in this paper, we [...] Read more.
The system of wireless sensor networks is high of interest due to a large number of demanded applications, such as the Internet of Things (IoT). The positioning of targets is one of crucial problems in wireless sensor networks. Particularly, in this paper, we propose minimax particle filtering (PF) for tracking a target in wireless sensor networks where multiple-RSS-measurements of received signal strength (RSS) are available at networked-sensors. The minimax PF adopts the maximum risk when computing the weights of particles, which results in the decreased variance of the weights and the immunity against the degeneracy problem of generic PF. Via the proposed approach, we can obtain improved tracking performance beyond the asymptotic-optimal performance of PF from a probabilistic perspective. We show the validity of the employed strategy in the applications of various PF variants, such as auxiliary-PF (APF), regularized-PF (RPF), Kullback–Leibler divergence-PF (KLDPF), and Gaussian-PF (GPF), besides the standard PF (SPF) in the problem of tracking a target in wireless sensor networks. Full article
(This article belongs to the Special Issue Signal Processing Techniques for Smart Sensor Communications)
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21 pages, 3744 KiB  
Article
A Novel Approach to 3D-DOA Estimation of Stationary EM Signals Using Convolutional Neural Networks
by Dong Chen and Young Hoon Joo
Sensors 2020, 20(10), 2761; https://doi.org/10.3390/s20102761 - 12 May 2020
Cited by 5 | Viewed by 3284
Abstract
This paper proposes a novel three-dimensional direction-of-arrival (3D-DOA) estimation method for electromagnetic (EM) signals using convolutional neural networks (CNN) in a Gaussian or non-Gaussian noise environment. First of all, in the presence of Gaussian noise, four output covariance matrices of the uniform triangular [...] Read more.
This paper proposes a novel three-dimensional direction-of-arrival (3D-DOA) estimation method for electromagnetic (EM) signals using convolutional neural networks (CNN) in a Gaussian or non-Gaussian noise environment. First of all, in the presence of Gaussian noise, four output covariance matrices of the uniform triangular array (UTA) are normalized and then fed into four neural networks for 1D-DOA estimation with identical parameters in parallel; then four 1D-DOA estimations of the UTA can be obtained, and finally, the 3D-DOA estimation could be obtained through post-processing. Secondly, in the presence of non-Gaussian noise, the array output covariance matrices are normalized by the infinity-norm and then processed in Gaussian noise environment; the infinity-norm normalization could effectively suppress impulsive outliers and then provide appropriate input features for the neural network. In addition, the outputs of the neural network are controlled by a signal monitoring network to avoid misjudgments. Comprehensive simulations demonstrate that in Gaussian or non-Gaussian noise environment, the proposed method is superior and effective in computation speed and accuracy in 1D-DOA and 3D-DOA estimations, and the signal monitoring network could also effectively control the neural network outputs. Consequently, we can conclude that CNN has better generalization ability in DOA estimation. Full article
(This article belongs to the Special Issue Signal Processing Techniques for Smart Sensor Communications)
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17 pages, 6706 KiB  
Article
Reduction of the Multipath Propagation Effect in a Hydroacoustic Channel Using Filtration in Cepstrum
by Agnieszka Czapiewska, Andrzej Luksza, Ryszard Studanski and Andrzej Zak
Sensors 2020, 20(3), 751; https://doi.org/10.3390/s20030751 - 29 Jan 2020
Cited by 13 | Viewed by 3480
Abstract
During data transmission in a hydroacoustic channel, one of the problems is the multipath propagation effect, which leads to a decrease in the transmission parameters and sometimes completely prevents it. Therefore, we have attempted to develop a method, which is based on a [...] Read more.
During data transmission in a hydroacoustic channel, one of the problems is the multipath propagation effect, which leads to a decrease in the transmission parameters and sometimes completely prevents it. Therefore, we have attempted to develop a method, which is based on a recorded hydroacoustic signal, that allows us to recreate the original (generated) signal by eliminating the multipath effect. In our method, we use cepstral analysis to eliminate replicas of the generated signal. The method has been tested in simulation and during measurements in a real environment. Additionally, the influence of the method on data transmission in the hydroacoustic channel was tested. The obtained results confirmed the usefulness of the application of the developed method and improved the quality of data transmission by reducing the multipath propagation effect. Full article
(This article belongs to the Special Issue Signal Processing Techniques for Smart Sensor Communications)
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14 pages, 10944 KiB  
Article
Knocking and Listening: Learning Mechanical Impulse Response for Understanding Surface Characteristics
by Semin Ryu and Seung-Chan Kim
Sensors 2020, 20(2), 369; https://doi.org/10.3390/s20020369 - 9 Jan 2020
Cited by 7 | Viewed by 3665
Abstract
Inspired by spiders that can generate and sense vibrations to obtain information regarding a substrate, we propose an intelligent system that can recognize the type of surface being touched by knocking the surface and listening to the vibrations. Hence, we developed a system [...] Read more.
Inspired by spiders that can generate and sense vibrations to obtain information regarding a substrate, we propose an intelligent system that can recognize the type of surface being touched by knocking the surface and listening to the vibrations. Hence, we developed a system that is equipped with an electromagnetic hammer for hitting the ground and an accelerometer for measuring the mechanical responses induced by the impact. We investigate the feasibility of sensing 10 different daily surfaces through various machine-learning techniques including recent deep-learning approaches. Although some test surfaces are similar, experimental results show that our system can recognize 10 different surfaces remarkably well (test accuracy of 98.66%). In addition, our results without directly hitting the surface (internal impact) exhibited considerably high test accuracy (97.51%). Finally, we conclude this paper with the limitations and future directions of the study. Full article
(This article belongs to the Special Issue Signal Processing Techniques for Smart Sensor Communications)
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