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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: closed (31 October 2025) | Viewed by 16331

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 250 words) can be sent to the Editorial Office for assessment.

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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (6 papers)

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Research

29 pages, 2488 KB  
Article
SILDSO: Dynamic Switching Optimization Scheme for Solar Insecticidal Lamp Based on Multi-Pest Phototactic Rhythm
by Heyang Yao, Lei Shu, Xing Yang, Kailiang Li and Miguel Martínez-García
Sensors 2025, 25(23), 7332; https://doi.org/10.3390/s25237332 - 2 Dec 2025
Cited by 1 | Viewed by 633
Abstract
Grain crops are regarded as fundamental to China’s agricultural production and food security. Effective control of nocturnal phototactic pests is essential for ensuring crop yields and achieving sustainable agricultural development. However, traditional solar insecticidal lamps often suffer from low energy utilization efficiency, dynamic [...] Read more.
Grain crops are regarded as fundamental to China’s agricultural production and food security. Effective control of nocturnal phototactic pests is essential for ensuring crop yields and achieving sustainable agricultural development. However, traditional solar insecticidal lamps often suffer from low energy utilization efficiency, dynamic switching control schemes, and poor adaptability in multi-pest coexistence scenarios. A multi-period intelligent switching control optimization scheme based on integrating a multi-pest phototactic rhythm is proposed, focusing on Cnaphalocrocis medinalis and Chilo suppressalis in rice fields. By considering the phototactic behavioral rhythm, energy consumption patterns, and residual energy levels, the proposed scheme dynamically optimizes the switching cycles of solar insecticidal lamps to maximize pest control effectiveness and energy efficiency. The rhythm modeling approach and dynamic adjustment mechanisms are employed to accurately align insecticidal working hours with varying pest activity patterns, thereby improving the pest control effectiveness of IoT-based solar insecticidal lamps. Simulation experiments demonstrate that, compared to traditional switching control schemes, the dynamic switching control scheme improves the average insecticidal rate by 17.7%, increases the effective insecticidal energy efficiency value by approximately 66.1%, and enhances the energy utilization rate by about 38.5%. The proposed dynamic switching control and intelligent energy management scheme not only improves the precision of pest control and energy utilization but also promotes the more efficient application of networked solar insecticidal lamps in smart agriculture. This work provides theoretical support and practical reference for intelligent pest control in complex agricultural environments, promoting the precision and sustainability of pest management practices. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Industrial/Agricultural Environments)
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21 pages, 1163 KB  
Article
MQTT-Based Architecture for Real-Time Data Collection and Anomaly Detection in Smart Livestock Housing
by Kyeong Il Ko and Meong Hun Lee
Sensors 2025, 25(23), 7186; https://doi.org/10.3390/s25237186 - 25 Nov 2025
Viewed by 2258
Abstract
This study designed a message queuing telemetry transport (MQTT)-based communication framework to acquire environmental data with stable, low-latency response (soft real-time capability) and detect anomalies in smart livestock housing. We validated the performance of the proposed framework using actual sensor data. It comprises [...] Read more.
This study designed a message queuing telemetry transport (MQTT)-based communication framework to acquire environmental data with stable, low-latency response (soft real-time capability) and detect anomalies in smart livestock housing. We validated the performance of the proposed framework using actual sensor data. It comprises environmental sensor nodes, a Mosquitto MQTT broker, and a GRU-based anomaly detection model, with data transmission via a WiFi-based network. Comparing quality of service (QoS) levels, the QoS 1 configuration demonstrated the most stable performance, with an average latency of ~150 ms, a data collection rate ≥ 99%, and a packet loss rate ≤ 0.5%. In the sensor node expansion experiment, responsiveness (≤200 ms) persisted for 10–15 nodes, whereas latency increased to 238.7 ms for 20 or more nodes. The GRU model proved suitable for low-latency analysis, achieving 97.5% accuracy, an F1-score of 0.972, and 18.5 ms/sample inference latency. In the integrated experiment, we recorded an average end-to-end latency of 185.4 ms, a data retention rate of 98.9%, processing throughput of 5.39 samples/s, and system uptime of 99.6%. These findings demonstrate that combining QoS 1-based lightweight MQTT communication with the GRU model ensures stable system response and low-latency operation (soft real-time capability) in monitoring livestock housing environments, achieving an average end-to-end latency of 185.4 ms. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Industrial/Agricultural Environments)
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15 pages, 2327 KB  
Article
Edge-Computing Smart Irrigation Controller Using LoRaWAN and LSTM for Predictive Controlled Deficit Irrigation
by Carlos Cambra Baseca, Rogério Dionísio, Fernando Ribeiro and José Metrôlho
Sensors 2025, 25(22), 7079; https://doi.org/10.3390/s25227079 - 20 Nov 2025
Cited by 1 | Viewed by 2850
Abstract
Enhancing sustainability in agriculture has become a significant challenge today where in the current context of climate change, particularly in countries of the Mediterranean area, the amount of water available for irrigation is becoming increasingly limited. Automating irrigation processes using affordable sensors can [...] Read more.
Enhancing sustainability in agriculture has become a significant challenge today where in the current context of climate change, particularly in countries of the Mediterranean area, the amount of water available for irrigation is becoming increasingly limited. Automating irrigation processes using affordable sensors can help save irrigation water and produce almonds more sustainably. This work presents an IoT-enabled edge computing model for smart irrigation systems focused on precision agriculture. This model combines IoT sensors, hybrid machine learning algorithms, and edge computing to predict soil moisture and manage Controlled Deficit Irrigation (CDI) strategies in high density almond tree fields applying reductions of 35% ETc (crop evapotranspiration). By gathering and analyzing meteorological, humidity soil, and crop data, a soft ML (Machine Learning) model has been developed to enhance irrigation practices and identify crop anomalies in real-time without cloud computing. This methodology has the potential to transform agricultural practices by enabling precise and efficient water management, even in remote locations with lack of internet access. This study represents an initial step toward implementing ML algorithms for irrigation CDI strategies. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Industrial/Agricultural Environments)
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11 pages, 483 KB  
Communication
Optimizing the Agricultural Internet of Things (IoT) with Edge Computing and Low-Altitude Platform Stations
by Deshan Yang, Jingwen Wu and Yixin He
Sensors 2024, 24(21), 7094; https://doi.org/10.3390/s24217094 - 4 Nov 2024
Cited by 9 | Viewed by 3306
Abstract
Using low-altitude platform stations (LAPSs) in the agricultural Internet of Things (IoT) enables the efficient and precise monitoring of vast and hard-to-reach areas, thereby enhancing crop management. By integrating edge computing servers into LAPSs, data can be processed directly at the edge in [...] Read more.
Using low-altitude platform stations (LAPSs) in the agricultural Internet of Things (IoT) enables the efficient and precise monitoring of vast and hard-to-reach areas, thereby enhancing crop management. By integrating edge computing servers into LAPSs, data can be processed directly at the edge in real time, significantly reducing latency and dependency on remote cloud servers. Motivated by these advancements, this paper explores the application of LAPSs and edge computing in the agricultural IoT. First, we introduce an LAPS-aided edge computing architecture for the agricultural IoT, in which each task is segmented into several interdependent subtasks for processing. Next, we formulate a total task processing delay minimization problem, taking into account constraints related to task dependency and priority, as well as equipment energy consumption. Then, by treating the task dependencies as directed acyclic graphs, a heuristic task processing algorithm with priority selection is developed to solve the formulated problem. Finally, the numerical results show that the proposed edge computing scheme outperforms state-of-the-art works and the local computing scheme in terms of the total task processing delay. Full article
(This article belongs to the Special Issue Wireless Sensor Networks in Industrial/Agricultural Environments)
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14 pages, 3653 KB  
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
Cited by 14 | Viewed by 2678
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 KB  
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 10 | Viewed by 3186
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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