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Special Issue "Internet of Mobile Things and Wireless Sensor Networks"

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

Deadline for manuscript submissions: 31 October 2023 | Viewed by 5372

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Special Issue Editors

Prof. Dr. Vladimir M. Vishnevsky

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Guest Editor

Department of Information and Networking Technologies, Institute of Control Sciences of Russian Academy of Sciences, 65 Profsoyuznaya street, 119991 Moscow, Russia
Interests: computer systems and networks; queuing systems; telecommunications; discrete mathematics (extremal graph theory, mathematical programming); and wireless data transmission networks
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Arkady Zaslavsky

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Guest Editor

School of Information Technology, Deakin University, Burwood, VIC 3125, Australia
Interests: IoT; context-awareness; smart cities; waste management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things involves adding digital sensors and networking technologies to the devices and systems that we use every day in the analog world, transforming and digitizing it and thereby making everything connected. The exponential growth of smart devices and technologies has allowed humankind to be in constant communication. As the flagship of the fourth industrial revolution (Industry 4.0) technologies, IoT enables on-site measurements to be taken remotely and in real time.

IoT and the trend toward greater connectivity means a significant increase in the number of data collected from more sources to enable real-time decisions, increase revenue, productivity, and efficiency and deliver new and improved solutions and services to all stakeholders. This raises a large number of network issues, open technical problems, and challenges related to performance, modeling, maintenance, reliability, and security.

This Special Issue aims to gather contributions in the form of original research papers and a limited number of review and survey papers exploring developments and advancements in the following research directions:

Internet of Things and wireless sensor networks,
Data collection from sensor fields using autonomous and tethered high-altitude unmanned platforms,
Application of RFID sensors for vehicle identification in road safety systems,
IoT systems and applications for smart cities,
Sensors and their application in smart farming and livestock,
Sensors and navigation systems in autonomous and tethered UAVs and analysis of systems of this class using analytical methods and numerical solutions based on machine learning,
Recent progress in intelligent sensor development.

Prof. Dr. Vladimir M. Vishnevsky
Prof. Dr. Arkady Zaslavsky
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

Internet of Things
wireless sensors networks (WSN)
sensor fields
RFID sensors
wireless connectivity
smart city
smart agriculture
UAV-based sensor systems
machine learning

Published Papers (5 papers)

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Research

Open AccessArticle

Bistability of AlGaAs/GaAs Resonant-Tunneling Diodes Heterostructural Channel

and

Sensors 2023, 23(18), 7977; https://doi.org/10.3390/s23187977 - 19 Sep 2023

Viewed by 233

Abstract

This paper presents an effective compact model of current transfer for the estimation of hysteresis parameters on the volt-ampere characteristics of resonant-tunneling diodes. In the framework of the compact model, the appearance of hysteresis is explained as a manifestation of internal bistability due to interelectronic interaction in the channel of the resonant-tunneling structure. Unlike the models based on the method of equivalent circuits, the interelectronic interaction in the compact model is taken into account using the concentration parameter. Model validation allowed us to confirm the high accuracy of the model not only at the initial section of the volt-ampere characteristics, but also at the hysteresis parameters traditionally predicted with low accuracy, namely the loop width (∆ < 0.5%) and contrast (∆ < 7%). Thus, it is concluded that the models are promising for integration into systems for synthesizing the electrical characteristics of resonant-tunneling diodes. Full article

(This article belongs to the Special Issue Internet of Mobile Things and Wireless Sensor Networks)

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Open AccessArticle

Optimal Scheduling in General Multi-Queue System by Combining Simulation and Neural Network Techniques

Dmitry Efrosinin

Vladimir Vishnevsky

and

Natalia Stepanova

Sensors 2023, 23(12), 5479; https://doi.org/10.3390/s23125479 - 10 Jun 2023

Viewed by 550

Abstract

The problem of optimal scheduling in a system with parallel queues and a single server has been extensively studied in queueing theory. However, such systems have mostly been analysed by assuming homogeneous attributes of arrival and service processes, or Markov queueing models were usually assumed in heterogeneous cases. The calculation of the optimal scheduling policy in such a queueing system with switching costs and arbitrary inter-arrival and service time distributions is not a trivial task. In this paper, we propose to combine simulation and neural network techniques to solve this problem. The scheduling in this system is performed by means of a neural network informing the controller at a service completion epoch on a queue index which has to be serviced next. We adapt the simulated annealing algorithm to optimize the weights and the biases of the multi-layer neural network initially trained on some arbitrary heuristic control policy with the aim to minimize the average cost function which in turn can be calculated only via simulation. To verify the quality of the obtained optimal solutions, the optimal scheduling policy was calculated by solving a Markov decision problem formulated for the corresponding Markovian counterpart. The results of numerical analysis show the effectiveness of this approach to find the optimal deterministic control policy for the routing, scheduling or resource allocation in general queueing systems. Moreover, a comparison of the results obtained for different distributions illustrates statistical insensitivity of the optimal scheduling policy to the shape of inter-arrival and service time distributions for the same first moments. Full article

(This article belongs to the Special Issue Internet of Mobile Things and Wireless Sensor Networks)

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Open AccessArticle

An Anomaly Intrusion Detection for High-Density Internet of Things Wireless Communication Network Based Deep Learning Algorithms

Emad Hmood Salman

Montadar Abas Taher

Yousif I. Hammadi

Omar Abdulkareem Mahmood

Ammar Muthanna

and

Andrey Koucheryavy

Sensors 2023, 23(1), 206; https://doi.org/10.3390/s23010206 - 25 Dec 2022

Cited by 5 | Viewed by 1352

Abstract

Telecommunication networks are growing exponentially due to their significant role in civilization and industry. As a result of this very significant role, diverse applications have been appeared, which require secured links for data transmission. However, Internet-of-Things (IoT) devices are a substantial field that utilizes the wireless communication infrastructure. However, the IoT, besides the diversity of communications, are more vulnerable to attacks due to the physical distribution in real world. Attackers may prevent the services from running or even forward all of the critical data across the network. That is, an Intrusion Detection System (IDS) has to be integrated into the communication networks. In the literature, there are numerous methodologies to implement the IDSs. In this paper, two distinct models are proposed. In the first model, a custom Convolutional Neural Network (CNN) was constructed and combined with Long Short Term Memory (LSTM) deep network layers. The second model was built about the all fully connected layers (dense layers) to construct an Artificial Neural Network (ANN). Thus, the second model, which is a custom of an ANN layers with various dimensions, is proposed. Results were outstanding a compared to the Logistic Regression algorithm (LR), where an accuracy of 97.01% was obtained in the second model and 96.08% in the first model, compared to the LR algorithm, which showed an accuracy of 92.8%. Full article

(This article belongs to the Special Issue Internet of Mobile Things and Wireless Sensor Networks)

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Open AccessArticle

Dynamic Smoothing, Filtering and Differentiation of Signals Defining the Path of the UAV

and

Sensors 2022, 22(23), 9472; https://doi.org/10.3390/s22239472 - 04 Dec 2022

Cited by 1 | Viewed by 806

Abstract

On the example of a control system for an unmanned aerial vehicle, we consider the problems of filtering, smoothing and restoring derivatives of reference action signals. These signals determine the desired spatial path of the plant at the first approximation. As a rule, researchers have considered these problems separately and have used different methods to solve each of them. The paper aims to develop a unified approach that provides a comprehensive solution to mentioned problems. We propose a dynamic admissible path generator. It is constructed as a copy of the canonical control plant model with smooth and bounded sigmoid corrective actions. For the deterministic case, a synthesis procedure has been developed, which ensures that the output variables of the generator track a non-smooth reference signal. Moreover, it considers the constraints on the velocity and acceleration of the plant. As a result, the generator variables produce a naturally smoothed spatial curve and its derivatives, which are realizable reference actions for the plant. The construction of the generator does not require exact knowledge of the plant parameters. Its dynamic order is less than that of the standard differentiators. We confirm the effectiveness of the approach by the results of numerical simulation. Full article

(This article belongs to the Special Issue Internet of Mobile Things and Wireless Sensor Networks)

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Open AccessArticle

On the Problem of State Recognition in Injection Molding Based on Accelerometer Data Sets

and

Sensors 2022, 22(16), 6165; https://doi.org/10.3390/s22166165 - 17 Aug 2022

Cited by 3 | Viewed by 1176

Abstract

The last few decades have been characterised by a very active application of smart technologies in various fields of industry. This paper deals with industrial activities, such as injection molding, where it is required to monitor continuously the manufacturing process to identify both the effective running time and down-time periods. Supervised machine learning algorithms are developed to recognize automatically the periods of the injection molding machines. The former algorithm uses directly the features of the descriptive statistics, while the latter one utilizes a convolutional neural network. The automatic state recognition system is equipped with an 3D-accelerometer sensor whose datasets are used to train and verify the proposed algorithms. The novelty of our contribution is that accelerometer data-based machine learning models are used to distinguish producing and non-producing periods by means of recognition of key steps in an injection molding cycle. The first testing results show the approximate overall balanced accuracy of 72–92% that illustrates the large potential of the monitoring system with the accelerometer. According to the ANOVA test, there are no sufficient statistical differences between the comparative algorithms, but the results of the neural network exhibit higher variances of the defined accuracy metrics. Full article

(This article belongs to the Special Issue Internet of Mobile Things and Wireless Sensor Networks)

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