Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Wireless Sensor Networks for Monitoring in Healthcare, Environment, and Industry
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Wireless Sensor Networks for Monitoring in Healthcare, Environment, and Industry

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

Deadline for manuscript submissions: 30 May 2024 | Viewed by 2233

Special Issue Editors

Dr. Yi Gu

E-Mail Website
Guest Editor
Department of Computer Science, Middle Tennessee State University, Murfreesboro, TN 37132, USA
Interests: wireless sensor networks; parallel and distributed computing; workflow scheduling; cloud/green computing; cybersecurity
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chase Wu

E-Mail Website
Guest Editor
Department of Data Science, New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: big data; data-intensive computing; parallel and distributed computing; high-performance networking; large-scale scientific visualization; wireless sensor networks; cyber security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wireless sensor networks (WSNs) play a critical role in data monitoring in various areas, including healthcare, environment, and industry, in order to provide cost-effective, real-time, and remote monitoring solutions. WSNs usually consist of small and battery-powered sensors equipped with various special sensors (such as motion, sound, temperature, humidity, light, or gravity sensors, etc.), which can communicate wirelessly with each other, or a central hub or gateway.

This Special Issue aims to put together original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of efficient, accurate, scalable and secure data monitoring in healthcare, environment, and industry using WSNs. Potential research topics include, but are not limited to, the following:

  • Wearable sensor technologies and their issues;
  • WSNs for emergency response systems;
  • WSNs for biological and ecological monitoring;
  • WSNs for climate change studies;
  • Quality control and process optimization;
  • WSNs for industrial IoT integration and real-time monitoring;
  • Human–machine interaction and AI applications;
  • WSNs for supply chain optimization;
  • Data privacy and other security issues;
  • Machine/deep learning and predictive analytics;
  • Energy efficiency and green computing;
  • Data fusion, integration, and routing;
  • Fault tolerance and reliability in WSNs;
  • WSNs communication and protocols;
  • Sensing data analytics in cloud/edge/fog computing.

Dr. Yi Gu
Prof. Dr. Chase Wu
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.

Published Papers (3 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

22 pages, 902 KiB  
Article
Multi-Hop Clustering and Routing Protocol Based on Enhanced Snake Optimizer and Golden Jackal Optimization in WSNs
by Zhen Wang, Jin Duan and Pengzhan Xing
Sensors 2024, 24(4), 1348; https://doi.org/10.3390/s24041348 - 19 Feb 2024
Viewed by 592
Abstract
A collection of smaller, less expensive sensor nodes called wireless sensor networks (WSNs) use their sensing range to gather environmental data. Data are sent in a multi-hop manner from the sensing node to the base station (BS). The bulk of these sensor nodes [...] Read more.
A collection of smaller, less expensive sensor nodes called wireless sensor networks (WSNs) use their sensing range to gather environmental data. Data are sent in a multi-hop manner from the sensing node to the base station (BS). The bulk of these sensor nodes run on batteries, which makes replacement and maintenance somewhat difficult. Preserving the network’s energy efficiency is essential to its longevity. In this study, we propose an energy-efficient multi-hop routing protocol called ESO-GJO, which combines the enhanced Snake Optimizer (SO) and Golden Jackal Optimization (GJO). The ESO-GJO method first applies the traditional SO algorithm and then integrates the Brownian motion function in the exploitation stage. The process then integrates multiple parameters, including the energy consumption of the cluster head (CH), node degree of CH, and distance between node and BS to create a fitness function that is used to choose a group of appropriate CHs. Lastly, a multi-hop routing path between CH and BS is created using the GJO optimization technique. According to simulation results, the suggested scheme outperforms LSA, LEACH-IACA, and LEACH-ANT in terms of lowering network energy consumption and extending network lifetime. Full article
(This article belongs to the Special Issue Wireless Sensor Networks for Monitoring in Healthcare, Environment, and Industry)
►▼ Show Figures

Figure 1

25 pages, 12662 KiB  
Article
A Self-Localization Algorithm for Mobile Targets in Indoor Wireless Sensor Networks Using Wake-Up Media Access Control Protocol
by Rihab Souissi, Salwa Sahnoun, Mohamed Khalil Baazaoui, Robert Fromm, Ahmed Fakhfakh and Faouzi Derbel
Sensors 2024, 24(3), 802; https://doi.org/10.3390/s24030802 - 25 Jan 2024
Viewed by 686
Abstract
Indoor localization of a mobile target represents a prominent application within wireless sensor network (WSN), showcasing significant values and scientific interest. Interference, obstacles, and energy consumption are critical challenges for indoor applications and battery replacements. A proposed tracking system deals with several factors [...] Read more.
Indoor localization of a mobile target represents a prominent application within wireless sensor network (WSN), showcasing significant values and scientific interest. Interference, obstacles, and energy consumption are critical challenges for indoor applications and battery replacements. A proposed tracking system deals with several factors such as latency, energy consumption, and accuracy presenting an innovative solution for the mobile localization application. In this paper, a novel algorithm introduces a self-localization algorithm for mobile targets using the wake-up media access control (MAC) protocol. The developed tracking application is based on the trilateration technique with received signal strength indication (RSSI) measurements. Simulations are implemented in the objective modular network testbed in C++ (OMNeT++) discrete event simulator using the C++ programming language, and the RSSI values introduced are based on real indoor measurements. In addition, a determination approach for finding the optimal parameters of RSSI is assigned to implement for the simulation parameters. Simulation results show a significant reduction in power consumption and exceptional accuracy, with an average error of 1.91 m in 90% of cases. This method allows the optimization of overall energy consumption, which consumes only 2.69% during the localization of 100 different positions. Full article
(This article belongs to the Special Issue Wireless Sensor Networks for Monitoring in Healthcare, Environment, and Industry)
►▼ Show Figures

Figure 1

14 pages, 3733 KiB  
Article
Optimizing Temperature Setting for Decomposition Furnace Based on Attention Mechanism and Neural Networks
by Shangkun Liu, Wei Shen, Chase Q. Wu and Xukang Lyu
Sensors 2023, 23(24), 9754; https://doi.org/10.3390/s23249754 - 11 Dec 2023
Viewed by 652
Abstract
The temperature setting for a decomposition furnace is of great importance for maintaining the normal operation of the furnace and other equipment in a cement plant and ensuring the output of high-quality cement products. Based on the principles of deep convolutional neural networks [...] Read more.
The temperature setting for a decomposition furnace is of great importance for maintaining the normal operation of the furnace and other equipment in a cement plant and ensuring the output of high-quality cement products. Based on the principles of deep convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and attention mechanisms, we propose a CNN-LSTM-A model to optimize the temperature settings for a decomposition furnace. The proposed model combines the features selected by Least Absolute Shrinkage and Selection Operator (Lasso) with others suggested by domain experts as inputs, and uses CNN to mine spatial features, LSTM to extract time series information, and an attention mechanism to optimize weights. We deploy sensors to collect production measurements at a real-life cement factory for experimentation and investigate the impact of hyperparameter changes on the performance of the proposed model. Experimental results show that CNN-LSTM-A achieves a superior performance in terms of prediction accuracy over existing models such as the basic LSTM model, deep-convolution-based LSTM model, and attention-mechanism-based LSTM model. The proposed model has potentials for wide deployment in cement plants to automate and optimize the operation of decomposition furnaces. Full article
(This article belongs to the Special Issue Wireless Sensor Networks for Monitoring in Healthcare, Environment, and Industry)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop