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Mathematical Modelling and Analysis in Sensors Networks
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Mathematical Modelling and Analysis in Sensors Networks

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

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 11152

Special Issue Editor

Prof. Dr. Wojciech Kempa

E-Mail Website
Guest Editor
Department of Mathematics Applications and Methods for Artificial Intelligence, Faculty of Applied Mathematics, Silesian University of Technology, ul. Kaszubska 23, 44-100 Gliwice, Poland
Interests: Computer and telecommunication networks; data mining; energy saving algorithms; probability; queueing theory; statistics; stochastic modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The growing use of sensor networks in monitoring and controlling many areas of everyday life implies the need for efficient management of the network structure and optimization of its operation. Due to the wide range of sensor network applications (fire risk monitoring, air quality assessment, traffic control, etc.), it is very important to have universal network performance analysis tools that could be used in various network operating conditions, with different parameters determining the intensity of the traffic and for different network topologies.

In view of these challenges, it seems particularly desirable to use mathematical tools and models, stochastic modeling methods or statistical techniques. Their application can significantly affect the more effective use of the network, improvement of transmission quality, and better organization of network operation. Mathematical and analytical methods can significantly support the process of network topology control, queuing packets in individual nodes or development of routing protocols. A special task is the mathematical modeling and development of energy-saving algorithms/mechanisms in sensor networks, as well as the use of mathematical and statistical methods in the analysis of network disturbances (such as fading, breakdowns, etc.)

This Special Issue shall gather novel developments in the use of mathematical methods in sensor networks, including both recent methodological developments and new results in applications. Given the focus on methodological developments, we strongly encourage authors to deposit their source code in a public repository (e.g., GitHub) if possible. Topics include but are not limited to the following keywords.

Prof. Dr. Wojciech Kempa
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.

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

  • Routing algorithms
  • Input/output traffic
  • Queueing modeling
  • Energy saving
  • Autocorellated input flow
  • Topology control
  • Sensor network optimization
  • Burstiness and packet blocking
  • Fading and breakdowns modeling

Published Papers (5 papers)

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Research

13 pages, 3232 KiB  
Article
Generalized Frequency Division Multiplexing-Based Low-Power Underwater Acoustic Image Transceiver
by Chin-Feng Lin, Cheng-Fong Wu, Ching-Lung Hsieh, Shun-Hsyung Chang, Ivan A. Parinov and Sergey Shevtsov
Sensors 2022, 22(1), 313; https://0-doi-org.brum.beds.ac.uk/10.3390/s22010313 - 31 Dec 2021
Cited by 2 | Viewed by 1590
Abstract
In this paper, a low-power underwater acoustic (UWA) image transceiver based on generalized frequency division multiplexing (GFDM) modulation for underwater communication is proposed. The proposed transceiver integrates a low-density parity-check code error protection scheme, adaptive 4-quadrature amplitude modulation (QAM) and 16-QAM strategies, GFDM [...] Read more.
In this paper, a low-power underwater acoustic (UWA) image transceiver based on generalized frequency division multiplexing (GFDM) modulation for underwater communication is proposed. The proposed transceiver integrates a low-density parity-check code error protection scheme, adaptive 4-quadrature amplitude modulation (QAM) and 16-QAM strategies, GFDM modulation, and a power assignment mechanism in an UWA image communication environment. The transmission bit error rates (BERs), the peak signal-to-noise ratios (PSNRs) of the received underwater images, and the power-saving ratio (PSR) of the proposed transceiver obtained using 4-QAM and 16-QAM, with perfect channel estimation, and channel estimation errors (CEEs) of 5%, 10%, and 20% were simulated. The PSNR of the received underwater image is 44.46 dB when using 4-QAM with a CEE of 10%. In contrast, PSNR is 48.79 dB when using 16-QAM with a CEE of 10%. When BER is 10−4, the received UW images have high PSNR values and high resolutions, indicating that the proposed transceiver is suitable for underwater image sensor signal transmission. Full article
(This article belongs to the Special Issue Mathematical Modelling and Analysis in Sensors Networks)
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14 pages, 590 KiB  
Article
Flight Planning Optimization of Multiple UAVs for Internet of Things
by Lucas Rodrigues, André Riker, Maria Ribeiro, Cristiano Both, Filipe Sousa, Waldir Moreira, Kleber Cardoso and Antonio Oliveira-Jr
Sensors 2021, 21(22), 7735; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227735 - 20 Nov 2021
Cited by 3 | Viewed by 2110
Abstract
This article presents an approach to autonomous flight planning of Unmanned Aerial Vehicles (UAVs)-Drones as data collectors to the Internet of Things (IoT). We have proposed a model for only one aircraft, as well as for multiple ones. A clustering technique that extends [...] Read more.
This article presents an approach to autonomous flight planning of Unmanned Aerial Vehicles (UAVs)-Drones as data collectors to the Internet of Things (IoT). We have proposed a model for only one aircraft, as well as for multiple ones. A clustering technique that extends the scope of the number of IoT devices (e.g., sensors) visited by UAVs is also addressed. The flight plan generated from the model focuses on preventing breakdowns due to a lack of battery charge to maximize the number of nodes visited. In addition to the drone autonomous flight planning, a data storage limitation aspect is also considered. We have presented the energy consumption of drones based on the aerodynamic characteristics of the type of aircraft. Simulations show the algorithm’s behavior in generating routes, and the model is evaluated using a reliability metric. Full article
(This article belongs to the Special Issue Mathematical Modelling and Analysis in Sensors Networks)
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23 pages, 7160 KiB  
Article
Evaluation of Deep Learning Methods in a Dual Prediction Scheme to Reduce Transmission Data in a WSN
by Carlos R. Morales, Fernando Rangel de Sousa, Valner Brusamarello and Nestor C. Fernandes
Sensors 2021, 21(21), 7375; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217375 - 06 Nov 2021
Cited by 8 | Viewed by 2462
Abstract
One of the most important challenges in Wireless Sensor Networks (WSN) is the extension of the sensors lifetime, which are battery-powered devices, through a reduction in energy consumption. Using data prediction to decrease the amount of transmitted data is one of the approaches [...] Read more.
One of the most important challenges in Wireless Sensor Networks (WSN) is the extension of the sensors lifetime, which are battery-powered devices, through a reduction in energy consumption. Using data prediction to decrease the amount of transmitted data is one of the approaches to solve this problem. This paper provides a comparison of deep learning methods in a dual prediction scheme to reduce transmission. The structures of the models are presented along with their parameters. A comparison of the models is provided using different performance metrics, together with the percent of points transmitted per threshold, and the errors between the final data received by Base Station (BS) and the measured values. The results show that the model with better performance in the dataset was the model with Attention, saving a considerable amount of data in transmission and still maintaining a good representation of the measured data. Full article
(This article belongs to the Special Issue Mathematical Modelling and Analysis in Sensors Networks)
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17 pages, 3506 KiB  
Communication
On the Time to Buffer Overflow in a Queueing Model with a General Independent Input Stream and Power-Saving Mechanism Based on Working Vacations
by Martyna Kobielnik and Wojciech Kempa
Sensors 2021, 21(16), 5507; https://0-doi-org.brum.beds.ac.uk/10.3390/s21165507 - 16 Aug 2021
Cited by 3 | Viewed by 1368
Abstract
A single server GI/M/1 queue with a limited buffer and an energy-saving mechanism based on a single working vacation policy is analyzed. The general independent input stream and exponential service times are considered. When the queue is empty [...] Read more.
A single server GI/M/1 queue with a limited buffer and an energy-saving mechanism based on a single working vacation policy is analyzed. The general independent input stream and exponential service times are considered. When the queue is empty after a service completion epoch, the server lowers the service speed for a random amount of time following an exponential distribution. Packets that arrive while the buffer is saturated are rejected. The analysis is focused on the duration of the time period with no packet losses. A system of equations for the transient time to the first buffer overflow cumulative distribution functions conditioned by the initial state and working mode of the service unit is stated using the idea of an embedded Markov chain and the continuous version of the law of total probability. The explicit representation for the Laplace transform of considered characteristics is found using a linear algebra-based approach. The results are illustrated using numerical examples, and the impact of the key parameters of the model is investigated. Full article
(This article belongs to the Special Issue Mathematical Modelling and Analysis in Sensors Networks)
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21 pages, 716 KiB  
Article
Supervised Learning of Neural Networks for Active Queue Management in the Internet
by Jakub Szyguła, Adam Domański, Joanna Domańska, Dariusz Marek, Katarzyna Filus and Szymon Mendla
Sensors 2021, 21(15), 4979; https://0-doi-org.brum.beds.ac.uk/10.3390/s21154979 - 22 Jul 2021
Cited by 7 | Viewed by 2368
Abstract
The paper examines the AQM mechanism based on neural networks. The active queue management allows packets to be dropped from the router’s queue before the buffer is full. The aim of the work is to use machine learning to create a model that [...] Read more.
The paper examines the AQM mechanism based on neural networks. The active queue management allows packets to be dropped from the router’s queue before the buffer is full. The aim of the work is to use machine learning to create a model that copies the behavior of the AQM PIα mechanism. We create training samples taking into account the self-similarity of network traffic. The model uses fractional Gaussian noise as a source. The quantitative analysis is based on simulation. During the tests, we analyzed the length of the queue, the number of rejected packets and waiting times in the queues. The proposed mechanism shows the usefulness of the Active Queue Management mechanism based on Neural Networks. Full article
(This article belongs to the Special Issue Mathematical Modelling and Analysis in Sensors Networks)
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