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Special Issue "Internet of Things for Sensors and Biosensors"

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

Deadline for manuscript submissions: 31 July 2021.

Special Issue Editors

Dr. Jesus Lozano
E-Mail Website
Guest Editor
Escuela de Ingenierías Industriales. Universidad de Extremadura. Av. Elvas s/n. Badajoz, Spain
Interests: chemical sensors; biosensors; electronic intrumentation; pattern recognition; machine learning; electronic nose technology; Internet of Things
Special Issues and Collections in MDPI journals
Dr. Daniel Matatagui
E-Mail Website
Guest Editor
Instituto de Tecnologías Físicas y de la Información. Consejo Superior de Investigaciones Científicas. C/ Serrano 144. Madrid, Spain
Interests: chemical sensors; biosensors; nanotechology; microtechnology; intrumentation; surface acoustic wave devices; spin wave devices
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Internet of Things (IoT) is a fast-growing innovation that will greatly change the way humans live. It can be thought of as the next big step in Internet technology. The number of things connected to the Internet will be much larger than the number of humans, and things will become the major generators and receivers of traffic. What really enable IoT to be a possibility are the various technologies that make it up: from sensors to actuators technology, including data acquisition, computing, and networking technologies. Many technologies are currently present that aim to serve as components to the IoT paradigm.

A sensor is a very broad term used to describe an object that can acquire data. Sensors enhance our capacity to observe and report on the world around us. However, the variety of sensors is a big part of IoT: chemical and physical sensors, biosensors, image sensors, and so on.

The motivation of this Special Issue is to solicit efforts and ongoing research work in the domain of the technologies used in IoT systems. This Issue will elaborate on the key aspects of sensor networks, communication protocols, and present and future applications of IoT, thus providing the opportunity for research community across the globe to share their ideas on these newly emerging fields of IoT.

Dr. Jesus Lozano
Dr. Daniel Matatagui
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 2200 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 Networks
  • Wearables
  • Smart sensors and actuators
  • Smartcities, smart environment, and intelligent-ambient
  • Communication protocols
  • Security
  • Cloud computing for data processing
  • IoT applications.

Published Papers (2 papers)

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Research

Article
Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits
Sensors 2020, 20(19), 5523; https://doi.org/10.3390/s20195523 - 27 Sep 2020
Cited by 7 | Viewed by 1340
Abstract
With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome [...] Read more.
With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome a number of significant limitations of unimodal biometric systems. In this paper, a new multimodal biometric human identification system is proposed, which is based on a deep learning algorithm for recognizing humans using biometric modalities of iris, face, and finger vein. The structure of the system is based on convolutional neural networks (CNNs) which extract features and classify images by softmax classifier. To develop the system, three CNN models were combined; one for iris, one for face, and one for finger vein. In order to build the CNN model, the famous pertained model VGG-16 was used, the Adam optimization method was applied and categorical cross-entropy was used as a loss function. Some techniques to avoid overfitting were applied, such as image augmentation and dropout techniques. For fusing the CNN models, different fusion approaches were employed to explore the influence of fusion approaches on recognition performance, therefore, feature and score level fusion approaches were applied. The performance of the proposed system was empirically evaluated by conducting several experiments on the SDUMLA-HMT dataset, which is a multimodal biometrics dataset. The obtained results demonstrated that using three biometric traits in biometric identification systems obtained better results than using two or one biometric traits. The results also showed that our approach comfortably outperformed other state-of-the-art methods by achieving an accuracy of 99.39%, with a feature level fusion approach and an accuracy of 100% with different methods of score level fusion. Full article
(This article belongs to the Special Issue Internet of Things for Sensors and Biosensors)
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Article
Resistance of IoT Sensors against DDoS Attack in Smart Home Environment
Sensors 2020, 20(18), 5298; https://doi.org/10.3390/s20185298 - 16 Sep 2020
Cited by 2 | Viewed by 1465
Abstract
Smart devices along with sensors are gaining in popularity with the promise of making life easier for the owner. As the number of sensors in an Internet of Things (IoT) system grows, a question arises as to whether the transmission between the sensors [...] Read more.
Smart devices along with sensors are gaining in popularity with the promise of making life easier for the owner. As the number of sensors in an Internet of Things (IoT) system grows, a question arises as to whether the transmission between the sensors and the IoT devices is reliable and whether the user receives alerts correctly and in a timely manner. Increased deployment of IoT devices with sensors increases possible safety risks. It is IoT devices that are often misused to create Distributed Denial of Service (DDoS) attacks, which is due to the weak security of IoT devices against misuse. The article looks at the issue from the opposite point of view, when the target of a DDoS attack are IoT devices in a smart home environment. The article examines how IoT devices and the entire smart home will behave if they become victims of a DDoS attack aimed at the smart home from the outside. The question of security was asked in terms of whether a legitimate user can continue to control and receive information from IoT sensors, which is available during normal operation of the smart home. The case study was done both from the point of view of the attack on the central units managing the IoT sensors directly, as well as on the smart-home personal assistant systems, with which the user can control the IoT sensors. The article presents experimental results for individual attacks performed in the case study and demonstrates the resistance of real IoT sensors against DDoS attack. The main novelty of the article is that the implementation of a personal assistant into the smart home environment increases the resistance of the user’s communication with the sensors. This study is a pilot testing the selected sensor sample to show behavior of smart home under DDoS attack. Full article
(This article belongs to the Special Issue Internet of Things for Sensors and Biosensors)
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