Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.7
Journals
Applied Sciences
Special Issues
Wireless Sensor Networks in Smart Environments — 2nd Volume
share announcement

Wireless Sensor Networks in Smart Environments — 2nd Volume

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 7484

Special Issue Editors

Prof. Dr. Juan Francisco De Paz Santana

E-Mail Website
Guest Editor
Expert Systems and Applications Lab, Faculty of Science, University of Salamanca, 37008 Salamanca, Spain
Interests: ambient intelligence; artificial intelligence; multi-agent systems; wireless sensor networks; big data; edge computing; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Dr. Gabriel Villarrubia González

E-Mail Website
Guest Editor
Expert Systems and Applications Lab, Faculty of Science, University of Salamanca, 37008 Salamanca, Spain
Interests: artificial intelligence; multi-agent systems; ambient intelligence; wireless sensor networks; bigdata; edge computing; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vasco N. G. J. Soares

E-Mail Website
Guest Editor
1. Polytechnic Institute of Castelo Branco, Av. Pedro Álvares Cabral No 12, 6000-084 Castelo Branco, Portugal
2. Instituto de Telecomunicações, Rua Marquês d’Ávila e Bolama, 6201-001 Covilhã, Portugal
Interests: vehicular networks; delay/disruption-tolerant networks; Internet of Things; smart cities; smart farming
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Valderi R. Q. Leithardt

E-Mail Website
Guest Editor
ISEL-Lisbon School of Engineering, Rua Conselheiro Emídio Navarro 1, 1959-007 Lisboa, Portugal
Interests: distributed systems; algorithms; data privacy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of sensor networks, specifically wireless sensor networks, has allowed for the development of monitoring environments capable of obtaining information to support decision-making in smart environments. To carry out such decision-making, it is necessary to apply artificial intelligence techniques capable of adapting to changes in smart environments in order to create systems that evolve autonomously over time. Currently, it is necessary to apply new information fusion techniques that allow for the processing of information at low and high levels to improve the accuracy of such systems. We are looking for new research and case studies based on wireless sensor networks and information fusion techniques that utilize multiple sensor information for decision making.

We invite you to submit contributions relating to software/hardware developments and new trends in adaptative techniques to process information from wireless sensor networks in smart environments. Among the smart environments we will highlight smart cities in case studies, such as monitoring and tracking systems for the improvement of intelligent mobility and behavior analysis associated to the project PID2019-108883RB-C21 / AEI / 10.13039/501100011033.

Prof. Dr. Juan Francisco De Paz Santana
Dr. Gabriel Villarrubia González
Prof. Dr. Vasco N. G. J. Soares
Dr. Valderi R. Q. Leithardt
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. Applied Sciences 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

  • fusion information
  • wireless sensor networks
  • blockchain
  • smart contracts
  • artificial intelligence
  • multiagent systems
  • ambient intelligence
  • IoT

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 1486 KiB  
Article
Analysis of Adaptive Algorithms Based on Least Mean Square Applied to Hand Tremor Suppression Control
by Rafael Silfarney Alves Araújo, Jéssica Cristina Tironi, Wemerson Delcio Parreira, Renata Coelho Borges, Juan Francisco De Paz Santana and Valderi Reis Quietinho Leithardt
Appl. Sci. 2023, 13(5), 3199; https://doi.org/10.3390/app13053199 - 02 Mar 2023
Cited by 2 | Viewed by 1417
Abstract
The increase in life expectancy, according to the World Health Organization, is a fact, and with it rises the incidence of age-related neurodegenerative diseases. The most recurrent symptoms are those associated with tremors resulting from Parkinson’s disease (PD) or essential tremors (ETs). The [...] Read more.
The increase in life expectancy, according to the World Health Organization, is a fact, and with it rises the incidence of age-related neurodegenerative diseases. The most recurrent symptoms are those associated with tremors resulting from Parkinson’s disease (PD) or essential tremors (ETs). The main alternatives for the treatment of these patients are medication and surgical intervention, which sometimes have restrictions and side effects. Through computer simulations in Matlab software, this work investigates the performance of adaptive algorithms based on least mean squares (LMS) to suppress tremors in upper limbs, especially in the hands. The signals resulting from pathological hand tremors, related to PD, present components at frequencies that vary between 3 Hz and 6 Hz, with the more significant energy present in the fundamental and second harmonics, while physiological hand tremors, referred to ET, vary between 4 Hz and 12 Hz. We simulated and used these signals as reference signals in adaptive algorithms, filtered-x least mean square (Fx-LMS), filtered-x normalized least mean square (Fx-NLMS), and a hybrid Fx-LMS–NLMS purpose. Our results showed that the vibration control provided by the Fx-LMS–LMS algorithm is the most suitable for physiological tremors. For pathological tremors, we used a proposed algorithm with a filtered sinusoidal input signal, Fsinx-LMS, which presented the best results in this specific case. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Smart Environments — 2nd Volume)
►▼ Show Figures

Figure 1

23 pages, 9341 KiB  
Article
CarTwin—Development of a Digital Twin for a Real-World In-Vehicle CAN Network
by Lucian Popa, Adriana Berdich and Bogdan Groza
Appl. Sci. 2023, 13(1), 445; https://doi.org/10.3390/app13010445 - 29 Dec 2022
Cited by 3 | Viewed by 2576
Abstract
Digital twins are used to replicate the behavior of physical systems, and in-vehicle networks can greatly benefit from this technology. This is mainly because in-vehicle networks circulate large amounts of data coming from various sources such as wired, or in some cases even [...] Read more.
Digital twins are used to replicate the behavior of physical systems, and in-vehicle networks can greatly benefit from this technology. This is mainly because in-vehicle networks circulate large amounts of data coming from various sources such as wired, or in some cases even wireless, sensors that are fused by actuators responsible for safety-critical tasks that require careful testing. In this work, we build a laboratory in-vehicle network that mimics a real vehicle network in regards to wire length, number of stubs and devices that are connected to it. The Controller Area Network (CAN), which is still the most popular communication bus inside cars, is used as a network layer. Using models defined in MATLAB for various subsystems, e.g., Anti-lock Braking System (ABS), Powertrain and Electric Power-Steering, deployed on automotive-grade microcontrollers, we evaluate the in-vehicle bus digital twin by providing realistic inputs and recording and reproducing in-vehicle network traffic. The experimental results showed good correlation between the output of the implemented digital twin and the data collected from an actual car. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Smart Environments — 2nd Volume)
►▼ Show Figures

Figure 1

27 pages, 7972 KiB  
Article
A Novel mHealth Approach for the Monitoring and Assisted Therapeutics of Obstructive Sleep Apnea
by José Rebelo, Pedro D. Gaspar, Vasco N. G. J. Soares and João M. L. P. Caldeira
Appl. Sci. 2022, 12(20), 10257; https://doi.org/10.3390/app122010257 - 12 Oct 2022
Cited by 1 | Viewed by 1145
Abstract
Obstructive sleep apnea is a respiratory problem that has serious consequences for physical and mental health, but also in monetary terms, since traffic accidents and poor work performance, among other direct consequences, are attributed to it. It is estimated that between 9% and [...] Read more.
Obstructive sleep apnea is a respiratory problem that has serious consequences for physical and mental health, but also in monetary terms, since traffic accidents and poor work performance, among other direct consequences, are attributed to it. It is estimated that between 9% and 38% of the world’s population has this disease. This is a multifactorial disease, therefore, there are several methods of detection and treatment; however, all of them cause discomfort to the patient, or to those around them. In this article we propose a system for the detection and control of obstructive sleep apnea that promises to overcome the drawbacks of the existing therapies, therefore, potentially making it a practical and effective solution for this disease. The proof of concept presented in this paper makes use of an electromyography sensor to collect the myoelectric signal produced by the genioglossus muscle. Surface electrodes provide the electromyography signals to an ESP32 microcontroller, which has the function of analyzing and comparing the data obtained with a predefined value of the apnea threshold. After the detection of an apnea, the circuit is able to create a stimulus signal that is applied directly to the muscle, so that airway occlusion does not occur, and the user does not wake up. The data from each use are automatically sent to a database to be viewed and analyzed at a later point. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Smart Environments — 2nd Volume)
►▼ Show Figures

Figure 1

12 pages, 2587 KiB  
Article
A Ground Moving Target Detection Method for Seismic and Sound Sensor Based on Evolutionary Neural Networks
by Kunsheng Xing, Nan Wang and Wei Wang
Appl. Sci. 2022, 12(18), 9343; https://doi.org/10.3390/app12189343 - 18 Sep 2022
Viewed by 1592
Abstract
The accurate identification of moving target types in alert areas is a fundamental task for unattended ground sensors. Considering that the seismic and sound signals generated by ground moving targets in urban areas are easily affected by environmental noise and the power consumption [...] Read more.
The accurate identification of moving target types in alert areas is a fundamental task for unattended ground sensors. Considering that the seismic and sound signals generated by ground moving targets in urban areas are easily affected by environmental noise and the power consumption of unattended ground sensors needs to be reduced to achieve low-power consumption, this paper proposes a ground moving target detection method based on evolutionary neural networks. The technique achieves the selection of feature extraction methods and the design of evolving neural network structures. The experimental results show that the improved model can achieve high recognition accuracy with a smaller feature vector and lower network complexity. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Smart Environments — 2nd Volume)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop