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Wireless Sensor Networks in Industrial/Agricultural Environments
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Wireless Sensor Networks in Industrial/Agricultural Environments

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

Deadline for manuscript submissions: 25 December 2024 | Viewed by 2098

Special Issue Editors

Dr. Rogério Dionísio

E-Mail Website1 Website2
Guest Editor
1. Polytechnic Institute of Castelo Branco, Av. Pedro Álvares Cabral No 12, 6000-084 Castelo Branco, Portugal
2. INESC TEC-Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal
Interests: wireless sensor networks; communication protocols; low-power sensor applications; cognitive radios
Special Issues, Collections and Topics in MDPI journals
Dr. Pedro M. B. Torres

E-Mail Website
Guest Editor
1. Polytechnic Institute of Castelo Branco, Av. Pedro Álvares Cabral No 12, 6000-084 Castelo Branco, Portugal
2. SYSTEC—Research Center for Systems and Technologies, ARISE—Advanced Production and Intelligent Systems Associated Laboratory, 4200-465 Porto, Portugal
Interests: electronics; instrumentation; automation; control; robotics; cyber-physical systems; computer vision; image processing and machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Carlos Cambra

E-Mail Website
Guest Editor
Applied Computational Intelligence Research Group (GICAP), Digitalization Department, University of Burgos, Burgos, Spain
Interests: Industry 4.0; IoT networks; robotics; smart farming; computer vision; image processing and machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, research in industrial and agricultural domains is driven by the need for greater efficiency, sustainability, and competitiveness. Wireless sensor networks can revolutionize these sectors by providing the data and insights required to make informed decisions and enhance overall performance. Additionally, this topic aligns with global trends towards sustainability, resource conservation, and technological advancement.

This Special Issue aims to bring together the latest research and innovations in the field of wireless sensor networks and their application in the industrial and agricultural domains and provide a platform for researchers, engineers, and experts to present their findings and insights in this rapidly evolving field.

Topics of interest for this Special Issue include, but are not limited to:

  • Deployment and optimization of WSNs in industrial and agricultural contexts.
  • Energy-efficient sensor node design and communication protocols.
  • Data collection, analysis, and visualization techniques for WSNs.
  • Security and privacy considerations in WSNs.
  • Integration of WSNs with Internet of Things (IoT) technologies.
  • Applications of WSNs in precision agriculture, industrial automation, and smart factories.
  • Case studies, field trials, and real-world implementations.
  • Challenges and future directions in WSN research for industrial and agricultural use cases.

We invite researchers and experts to submit their original research articles, reviews, and case studies on these topics to contribute to the knowledge and development of wireless sensor networks in industrial and agricultural environments.

Sincerely,
Dr. Rogério Dionísio
Dr. Pedro M. B. Torres
Dr. Carlos Cambra
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

  • energy-efficient sensor design
  • data fusion and aggregation
  • machine learning and AI for data analysis
  • IoT integration
  • edge and fog computing
  • communication protocols
  • wireless security and privacy
  • sensor localization
  • environmental monitoring
  • precision agriculture
  • industrial process optimization
  • smart factories
  • human-machine interaction
  • sustainability and green IoT
  • robotic and drone integration
  • blockchain and distributed ledger technology
  • cross-domain collaboration
  • case studies and field trials

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Published Papers (2 papers)

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14 pages, 3653 KiB  
Article
Edge Integration of Artificial Intelligence into Wireless Smart Sensor Platforms for Railroad Bridge Impact Detection
by Omobolaji Lawal, Shaik Althaf Veluthedath Shajihan, Kirill Mechitov and Billie F. Spencer, Jr.
Sensors 2024, 24(17), 5633; https://doi.org/10.3390/s24175633 - 30 Aug 2024
Viewed by 572
Abstract
Of the 100,000 railroad bridges in the United States, 50% are over 100 years old. Many of these bridges do not meet the minimum vertical clearance standards, making them susceptible to impact from over-height vehicles. The impact can cause structural damage and unwanted [...] Read more.
Of the 100,000 railroad bridges in the United States, 50% are over 100 years old. Many of these bridges do not meet the minimum vertical clearance standards, making them susceptible to impact from over-height vehicles. The impact can cause structural damage and unwanted disruption to railroad bridge services; rapid notification of the railroad authorities is crucial to ensure that the bridges are safe for continued use and to affect timely repairs. Therefore, researchers have developed approaches to identify these impacts on railroad bridges. Some recent approaches use machine learning to more effectively identify impacts from the sensor data. Typically, the collected sensor data are transmitted to a central location for processing. However, the challenge with this centralized approach is that the transfer of data to a central location can take considerable time, which is undesirable for time-sensitive events, like impact detection, that require a rapid assessment and response to potential damage. To address the challenges posed by the centralized approach, this study develops a framework for edge implementation of machine-learning predictions on wireless smart sensors. Wireless sensors are used because of their ease of installation and lower costs compared to their wired counterparts. The framework is implemented on the Xnode wireless smart sensor platform, thus bringing artificial intelligence models directly to the sensor nodes and eliminating the need to transfer data to a central location for processing. This framework is demonstrated using data obtained from events on a railroad bridge near Chicago; results illustrate the efficacy of the proposed edge computing framework for such time-sensitive structural health monitoring applications. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Industrial/Agricultural Environments)
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17 pages, 1049 KiB  
Article
A Framework for Detecting False Data Injection Attacks in Large-Scale Wireless Sensor Networks
by Jiamin Hu, Xiaofan Yang and Lu-Xing Yang
Sensors 2024, 24(5), 1643; https://doi.org/10.3390/s24051643 - 2 Mar 2024
Cited by 1 | Viewed by 1020
Abstract
False data injection attacks (FDIAs) on sensor networks involve injecting deceptive or malicious data into the sensor readings that cause decision-makers to make incorrect decisions, leading to serious consequences. With the ever-increasing volume of data in large-scale sensor networks, detecting FDIAs in large-scale [...] Read more.
False data injection attacks (FDIAs) on sensor networks involve injecting deceptive or malicious data into the sensor readings that cause decision-makers to make incorrect decisions, leading to serious consequences. With the ever-increasing volume of data in large-scale sensor networks, detecting FDIAs in large-scale sensor networks becomes more challenging. In this paper, we propose a framework for the distributed detection of FDIAs in large-scale sensor networks. By extracting the spatiotemporal correlation information from sensor data, the large-scale sensors are categorized into multiple correlation groups. Within each correlation group, an autoregressive integrated moving average (ARIMA) is built to learn the temporal correlation of cross-correlation, and a consistency criterion is established to identify abnormal sensor nodes. The effectiveness of the proposed detection framework is validated based on a real dataset from the U.S. smart grid and simulated under both the simple FDIA and the stealthy FDIA strategies. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Industrial/Agricultural Environments)
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