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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 September 2024 | Viewed by 866

Special Issue Editors


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

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

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

Published Papers (1 paper)

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Research

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 - 02 Mar 2024
Viewed by 586
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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