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Special Issue "Security and Information Flow in Intelligent Systems for the Internet of Things"

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 4209

Special Issue Editors

Dr. Dawid Połap
E-Mail Website
Guest Editor
Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
Interests: machine learning; multiagent systems; bioinformatics; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gautam Srivastava
E-Mail Website
Guest Editor
Dr. Marta Wlodarczyk-Sielicka
E-Mail Website
Guest Editor
Department of Navigation, Maritime University of Szczecin, Waly Chrobrego 1-2, 70-500 Szczecin, Poland
Interests: spatial big data; spatial analysis; artificial neural networks; deep learning; data fusion; processing of bathymetric data; sea bottom modeling; data reduction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Extensive development of IT systems and sensors that can acquire knowledge must be secured during data flow, as well as during storage. Secure information in various types of devices and sensors allows for safe decision making by artificial intelligence algorithms. The transmitted data and their analysis are important from a technical point of view of the sensors in the devices. In many cases, collected data from the environment must be quickly processed and stored for long-lasting training processes of artificial intelligence. This Special Issue focuses on the operation of sensors on the Internet of Things, with particular emphasis on their storage, processing, and protection when sending or sharing information.

Intelligent systems for the Internet of Things are based on sensors that acquire knowledge from environment. The main idea behind this Special Issue is to take up the topic of security and information processing of data obtained by these sensors, which are used in solutions in intelligent homes/cities or even in the Internet of Medical Things.

Dr. Dawid Połap
Dr. Guatam Srivastava
Dr. Marta Włodarczyk-Sielicka
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 2400 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

  • biomedical sensors
  • big data
  • Internet of Things
  • machine learning
  • multiagent systems
  • augmented/virtual/mixed reality

Published Papers (4 papers)

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Research

Article
Human Gait Recognition: A Single Stream Optimal Deep Learning Features Fusion
Sensors 2021, 21(22), 7584; https://doi.org/10.3390/s21227584 - 15 Nov 2021
Cited by 8 | Viewed by 829
Abstract
Human Gait Recognition (HGR) is a biometric technique that has been utilized for security purposes for the last decade. The performance of gait recognition can be influenced by various factors such as wearing clothes, carrying a bag, and the walking surfaces. Furthermore, identification [...] Read more.
Human Gait Recognition (HGR) is a biometric technique that has been utilized for security purposes for the last decade. The performance of gait recognition can be influenced by various factors such as wearing clothes, carrying a bag, and the walking surfaces. Furthermore, identification from differing views is a significant difficulty in HGR. Many techniques have been introduced in the literature for HGR using conventional and deep learning techniques. However, the traditional methods are not suitable for large datasets. Therefore, a new framework is proposed for human gait recognition using deep learning and best feature selection. The proposed framework includes data augmentation, feature extraction, feature selection, feature fusion, and classification. In the augmentation step, three flip operations were used. In the feature extraction step, two pre-trained models were employed, Inception-ResNet-V2 and NASNet Mobile. Both models were fine-tuned and trained using transfer learning on the CASIA B gait dataset. The features of the selected deep models were optimized using a modified three-step whale optimization algorithm and the best features were chosen. The selected best features were fused using the modified mean absolute deviation extended serial fusion (MDeSF) approach. Then, the final classification was performed using several classification algorithms. The experimental process was conducted on the entire CASIA B dataset and achieved an average accuracy of 89.0. Comparison with existing techniques showed an improvement in accuracy, recall rate, and computational time. Full article
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Article
Optimization and Security of Hazardous Waste Incineration Plants with the Use of a Heuristic Algorithm
Sensors 2021, 21(21), 7247; https://doi.org/10.3390/s21217247 - 30 Oct 2021
Cited by 3 | Viewed by 501
Abstract
The amount of generated waste, which increases every year, is a serious problem of the modern world. In particular, attention should be paid to hazardous waste and methods of its disposal. One of the most used in this context is thermal treatment in [...] Read more.
The amount of generated waste, which increases every year, is a serious problem of the modern world. In particular, attention should be paid to hazardous waste and methods of its disposal. One of the most used in this context is thermal treatment in dedicated incinerators equipped with a rotary kiln. Conducting the process requires, inter alia, supplying the furnace with a batch of batch material with appropriate parameters. Improper operation in this regard may cause negative environmental effects and operational problems. The key here is to select different types of hazardous waste and compose batch portions. The paper presents an application that optimizes the work of waste incineration plant operators. At the same time, this tool can be described as ensuring security at this stage of the process. The application implements an ant colony algorithm that selects the optimal solution to the problem, which has been formulated here as the types and masses of the batch mixture components with given parameters. The application has been tested in the laboratory and real conditions with satisfactory results. Full article
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Article
A Possible World-Based Fusion Estimation Model for Uncertain Data Clustering in WBNs
Sensors 2021, 21(3), 875; https://doi.org/10.3390/s21030875 - 28 Jan 2021
Cited by 3 | Viewed by 680
Abstract
In data clustering, the measured data are usually regarded as uncertain data. As a probability-based clustering technique, possible world can easily cluster the uncertain data. However, the method of possible world needs to satisfy two conditions: determine the data of different possible worlds [...] Read more.
In data clustering, the measured data are usually regarded as uncertain data. As a probability-based clustering technique, possible world can easily cluster the uncertain data. However, the method of possible world needs to satisfy two conditions: determine the data of different possible worlds and determine the corresponding probability of occurrence. The existing methods mostly make multiple measurements and treat each measurement as deterministic data of a possible world. In this paper, a possible world-based fusion estimation model is proposed, which changes the deterministic data into probability distribution according to the estimation algorithm, and the corresponding probability can be confirmed naturally. Further, in the clustering stage, the Kullback–Leibler divergence is introduced to describe the relationships of probability distributions among different possible worlds. Then, an application in wearable body networks (WBNs) is given, and some interesting conclusions are shown. Finally, simulations show better performance when the relationships between features in measured data are more complex. Full article
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Article
Determinism in Cyber-Physical Systems Specified by Interpreted Petri Nets
Sensors 2020, 20(19), 5565; https://doi.org/10.3390/s20195565 - 28 Sep 2020
Cited by 9 | Viewed by 1051
Abstract
In this paper, we study selected aspects of determinism in the control part of a cyber-physical system (CPS) that is specified by a Petri net-based model. In particular, the control interpreted Petri nets (CIPNs) are applied, which are an extension of the ordinary [...] Read more.
In this paper, we study selected aspects of determinism in the control part of a cyber-physical system (CPS) that is specified by a Petri net-based model. In particular, the control interpreted Petri nets (CIPNs) are applied, which are an extension of the ordinary Petri nets, supplemented by signals (related to sensors and actuators) that permit communication with the environment. The notions of weak and strong determinism in a system described by a CIPN are introduced in the paper. The proposed concepts are supported by formal definitions and theorems. Moreover, a novel modelling methodology for a deterministic system specified by a CIPN is proposed. The presented solutions are illustrated by a case study example of a real-life cyber-physical system. Finally, the results of experimental verification of the proposed determinism-based techniques are demonstrated and discussed. Full article
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