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Wireless Sensor Networks for Condition Monitoring
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Wireless Sensor Networks for Condition Monitoring

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 7387

Special Issue Editor

Dr. Juan C. Granda

E-Mail Website
Guest Editor
Departament of Computer Science and Engineering, University of Oviedo, Campus de Gijón, 33203 Asturias, Spain
Interests: industrial IoT; predictive maintenance; wireless sensor networks; renewable energy

Special Issue Information

Dear Colleagues,

Condition monitoring (CM) usually requires continuous monitoring of physical variables such as vibrations, electric current, sound or temperature from running machinery to implement maintenance policies using machine learning models.

Traditionally, continuous monitoring has focused on critical machinery only, but wireless sensor networks (WSNs) enable the deployment of myriads of sensors capable of sensing, computing and communicating wirelessly to gather information from industrial equipment.

The Special Issue on "Wireless Sensor Networks for Condition Monitoring” aims to explore the latest advancements, challenges, and opportunities of WSNs across different sectors when applied to condition monitoring. Contributions from researchers, practitioners, and experts in the field proposing novel methodologies, applications, and best practices in this domain are welcomed.

Dr. Juan C. Granda
Guest Editor

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

  • industrial IoT
  • fault detection and diagnosis
  • predictive maintenance
  • sensors for condition monitoring
  • WSNs in harsh environments
  • deep learning or machine learning models for condition monitoring
  • energy-aware condition monitoring
  • cloud, edge, fog and combined cloud-edge computing architectures for condition monitoring
  • case studies and practical implementations of WSNs in monitoring critical infrastructure
  • security and privacy issues in WSN-based condition monitoring systems
  • energy harvesting in WSN

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Published Papers (6 papers)

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Research

26 pages, 3654 KiB  
Article
Resistance Welding Quality Through Artificial Intelligence Techniques
by Luis Alonso Domínguez-Molina, Edgar Rivas-Araiza, Juan Carlos Jauregui-Correa, Jose Luis Gonzalez-Cordoba, Jesús Carlos Pedraza-Ortega and Andras Takacs
Sensors 2025, 25(6), 1744; https://doi.org/10.3390/s25061744 - 12 Mar 2025
Viewed by 579
Abstract
Quality assessment of the resistance spot welding process (RSW) is vital during manufacturing. Evaluating the quality without altering the joint material’s physical and mechanical properties has gained interest. This study uses a trained computer vision model to propose a cheap, non-destructive quality-evaluation methodology. [...] Read more.
Quality assessment of the resistance spot welding process (RSW) is vital during manufacturing. Evaluating the quality without altering the joint material’s physical and mechanical properties has gained interest. This study uses a trained computer vision model to propose a cheap, non-destructive quality-evaluation methodology. The methodology connects the welding input and during-process parameters with the output visual quality information. A manual resistance spot welding machine was used to monitor and record the process input and output parameters to generate the dataset for training. The welding current, welding time, and electrode pressure data were correlated with the welding spot nugget’s quality, mechanical characteristics, and thermal and visible images. Six machine learning models were trained on visible and thermographic images to classify the weld’s quality and connect the quality characteristics (pull force and welding diameter) and the manufacturing process parameters with the visible and thermographic images of the weld. Finally, a cross-validation method validated the robustness of these models. The results indicate that the welding time and the angle between electrodes are highly influential parameters on the mechanical strength of the joint. Additionally, models using visible images of the welding spot exhibited superior performance compared to thermal images. Full article
(This article belongs to the Special Issue Wireless Sensor Networks for Condition Monitoring)
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42 pages, 5674 KiB  
Article
Self-Organizing Wireless Sensor Networks Solving the Coverage Problem: Game-Theoretic Learning Automata and Cellular Automata-Based Approaches
by Franciszek Seredynski, Miroslaw Szaban, Jaroslaw Skaruz, Piotr Switalski and Michal Seredynski
Sensors 2025, 25(5), 1467; https://doi.org/10.3390/s25051467 - 27 Feb 2025
Viewed by 494
Abstract
In this paper, we focus on developing self-organizing algorithms aimed at solving, in a distributed way, the coverage problem in Wireless Sensor Networks (WSNs). For this purpose, we apply a game-theoretical framework based on an application of a variant of the Spatial Prisoner’s [...] Read more.
In this paper, we focus on developing self-organizing algorithms aimed at solving, in a distributed way, the coverage problem in Wireless Sensor Networks (WSNs). For this purpose, we apply a game-theoretical framework based on an application of a variant of the Spatial Prisoner’s Dilemma game. The framework is used to build a multi-agent system, where agent-players in the process of iterated games tend to achieve a Nash equilibrium, providing them the possible maximal values of payoffs. A reached equilibrium corresponds to a global solution for the coverage problem represented by the following two objectives: coverage and the corresponding number of sensors that need to be turned on. A multi-agent system using the game-theoretic framework assumes the creation of a graph model of WSNs and the further interpretation of nodes of the WSN graph as agents participating in iterated games. We use the following two types of reinforcement learning machines as agents: Learning Automata (LA) and Cellular Automata (CA). The main novelty of the paper is the development of a specialized reinforcement learning machine based on the application of (ϵ,h)-learning automata. As the second model of an agent, we use the adaptive CA that we recently proposed. While both agent models operate in discrete time, they differ in the way they store and use available information. LA-based agents store in their memories the current information obtained in the last h-time steps and only use this information to make a decision in the next time step. CA-based agents only retain information from the last time step. To make a decision in the next time step, they participate in local evolutionary competitions that determine their subsequent actions. We show that agent-players reaching the Nash equilibria corresponds to the system achieving a global optimization criterion related to the coverage problem, in a fully distributed way, without the agents’ knowledge of the global optimization criterion and without any central coordinator. We perform an extensive experimental study of both models and show that the proposed learning automata-based model significantly outperforms the cellular automata-based model. Full article
(This article belongs to the Special Issue Wireless Sensor Networks for Condition Monitoring)
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18 pages, 3859 KiB  
Article
A WSN and LoRa Hybrid Multimedia Transmission Protocol for Scalar Data and Image Transmission
by Quoc Hop Ta, Van Khoe Ta, Trang Tien Nguyen and Hoon Oh
Sensors 2024, 24(24), 8165; https://doi.org/10.3390/s24248165 - 21 Dec 2024
Viewed by 794
Abstract
The proposed protocol features reliable and fast image transmission while periodically transmitting scalar data without interruption by allowing two networks, a LoRa network and a wireless sensor network, with different transmission characteristics to cooperate. It adopts the RT-LoRa protocol for periodic scalar data [...] Read more.
The proposed protocol features reliable and fast image transmission while periodically transmitting scalar data without interruption by allowing two networks, a LoRa network and a wireless sensor network, with different transmission characteristics to cooperate. It adopts the RT-LoRa protocol for periodic scalar data transmission and uses a WSN-based pipelined transmission method that leverages single-hop message transmission of a LoRa network for image transmission. Thus, it can not only eliminate the control message overhead for time synchronization, slot scheduling, and path establishment for pipelined image transmission in WSNs but also eliminate interferences within WSNs, such as data collisions and data and message collisions, during pipelined image transmission, thereby enabling high reliability and fast transmission. According to experimental results obtained inside a university building, the proposed protocol achieved an image transfer rate of approximately 96% without packet loss, transmitted one 24 KB image in approximately 0.3 s, and achieved an image transfer rate of 100% under the tolerance of one image packet loss. These results indicate a speedup of about 25% compared to a recent pipelined protocol while ensuring near-perfect image transmission quality. Full article
(This article belongs to the Special Issue Wireless Sensor Networks for Condition Monitoring)
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21 pages, 3172 KiB  
Article
An Integrated Approach: A Hybrid Machine Learning Model for the Classification of Unscheduled Stoppages in a Mining Crushing Line Employing Principal Component Analysis and Artificial Neural Networks
by Pablo Viveros, Cristian Moya, Rodrigo Mena, Fredy Kristjanpoller and David R. Godoy
Sensors 2024, 24(17), 5804; https://doi.org/10.3390/s24175804 - 6 Sep 2024
Viewed by 1405
Abstract
This article implements a hybrid Machine Learning (ML) model to classify stoppage events in a copper-crushing equipment, more specifically, a conveyor belt. The model combines Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) with Principal Component Analysis (PCA) to identify the type [...] Read more.
This article implements a hybrid Machine Learning (ML) model to classify stoppage events in a copper-crushing equipment, more specifically, a conveyor belt. The model combines Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) with Principal Component Analysis (PCA) to identify the type of stoppage event when they occur in an industrial sector that is significant for the Chilean economy. This research addresses the critical need to optimise maintenance management in the mining industry, highlighting the technological relevance and motivation for using advanced ML techniques. This study focusses on combining and implementing three ML models trained with historical data composed of information from various sensors, real and virtual, as well from maintenance reports that report operational conditions and equipment failure characteristics. The main objective of this study is to improve the efficiency when identifying the nature of a stoppage serving as a basis for the subsequent development of a reliable failure prediction system. The results indicate that this approach significantly increases information reliability, addressing the persistent challenges in data management within the maintenance area. With a classification accuracy of 96.2% and a recall of 96.3%, the model validates and automates the classification of stoppage events, significantly reducing dependency on interdepartmental interactions. This advancement eliminates the need for reliance on external databases, which have previously been prone to errors, missing critical data, or containing outdated information. By implementing this methodology, a robust and reliable foundation is established for developing a failure prediction model, fostering both efficiency and reliability in the maintenance process. The application of ML in this context produces demonstrably positive outcomes in the classification of stoppage events, underscoring its significant impact on industry operations. Full article
(This article belongs to the Special Issue Wireless Sensor Networks for Condition Monitoring)
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14 pages, 4196 KiB  
Article
Edge Computing and Fault Diagnosis of Rotating Machinery Based on MobileNet in Wireless Sensor Networks for Mechanical Vibration
by Yi Huang, Shuang Liang, Tingqiong Cui, Xiaojing Mu, Tianhong Luo, Shengxue Wang and Guangyong Wu
Sensors 2024, 24(16), 5156; https://doi.org/10.3390/s24165156 - 9 Aug 2024
Cited by 2 | Viewed by 1493
Abstract
With the rapid development of the Industrial Internet of Things in rotating machinery, the amount of data sampled by mechanical vibration wireless sensor networks (MvWSNs) has increased significantly, straining bandwidth capacity. Concurrently, the safety requirements for rotating machinery have escalated, necessitating enhanced real-time [...] Read more.
With the rapid development of the Industrial Internet of Things in rotating machinery, the amount of data sampled by mechanical vibration wireless sensor networks (MvWSNs) has increased significantly, straining bandwidth capacity. Concurrently, the safety requirements for rotating machinery have escalated, necessitating enhanced real-time data processing capabilities. Conventional methods, reliant on experiential approaches, have proven inefficient in meeting these evolving challenges. To this end, a fault detection method for rotating machinery based on mobileNet in MvWSNs is proposed to address these intractable issues. The small and light deep learning model is helpful to realize nearly real-time sensing and fault detection, lightening the communication pressure of MvWSNs. The well-trained deep learning is implanted on the MvWSNs sensor node, an edge computing platform developed via embedded STM32 microcontrollers (STMicroelectronics International NV, Geneva, Switzerland). Data acquisition, data processing, and data classification are all executed on the computing- and energy-constrained sensor node. The experimental results demonstrate that the proposed fault detection method can achieve about 0.99 for the DDS dataset and an accuracy of 0.98 in the MvWSNs sensor node. Furthermore, the final transmission data size is only 0.1% compared to the original data size. It is also a time-saving method that can be accomplished within 135 ms while the raw data will take about 1000 ms to transmit to the monitoring center when there are four sensor nodes in the network. Thus, the proposed edge computing method shows good application prospects in fault detection and control of rotating machinery with high time sensitivity. Full article
(This article belongs to the Special Issue Wireless Sensor Networks for Condition Monitoring)
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23 pages, 10092 KiB  
Article
RSSI-WSDE: Wireless Sensing of Dynamic Events Based on RSSI
by Xiaoping Tian, Song Wu, Xiaoyan Zhang, Lei Du and Sencao Fan
Sensors 2024, 24(15), 4952; https://doi.org/10.3390/s24154952 - 31 Jul 2024
Viewed by 1374
Abstract
Wireless sensing is a crucial technology for building smart cities, playing a vital role in applications such as human monitoring, route planning, and traffic management. Analyzing the data provided by wireless sensing enables the formulation of more scientific decisions. The wireless sensing of [...] Read more.
Wireless sensing is a crucial technology for building smart cities, playing a vital role in applications such as human monitoring, route planning, and traffic management. Analyzing the data provided by wireless sensing enables the formulation of more scientific decisions. The wireless sensing of dynamic events is a significant branch of wireless sensing. Sensing the specific times and durations of dynamic events is a challenging problem due to the dynamic event information is concealed within static environments. To effectively sense the relevant information of event occurrence, we propose a wireless sensing method for dynamic events based on RSSI, named RSSI-WSDE. RSSI-WSDE utilizes variable-length sliding windows and statistical methods to process original RSSI time series, amplifying the differences between dynamic events and static environments. Subsequently, z-score normalization is employed to enhance the comparability of the sensing effects for different dynamic events. Furthermore, by setting the adaptive threshold, the occurrence of dynamic event is sensed and the relevant information is marked on the original RSSI time series. In this study, the sensing performance of RSSI-WSDE was tested in indoor corridors and outdoor urban road environments. The wireless sensing of dynamic events, including walking, running, cycling, and driving, was conducted. The experimental results demonstrate that RSSI-WSDE can accurately sense the occurrence of dynamic events, marking the specific time and duration with millisecond-level precision. Moreover, RSSI-WSDE exhibits robust performance in wireless sensing of dynamic events in both indoor and outdoor environments. Full article
(This article belongs to the Special Issue Wireless Sensor Networks for Condition Monitoring)
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