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Special Issue "Artificial Intelligence and Machine Learning in Sensors Networks"

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

Deadline for manuscript submissions: 30 September 2018

Special Issue Editors

Guest Editor
Prof. Dr. Juan Manuel Corchado Rodríguez

BISITE Research Group, University of Salamanca, Edificio I+D+i, 37008 Salamanca, Spain
Website | E-Mail
Interests: multi-agent systems; artificial intelligence; internet of things; smart energy systems; intelligent distributed systems; information security
Guest Editor
Prof. Dr. Enrique Herrera-Viedma

Dept. Computer Science and Artificial Intelligence, E.T.S. de Ingenieria Informatica y de Telecomunicacion, University of Granada, 18071 Granada, Spain
Website | E-Mail
Interests: artificial intelligence; machine learning fuzzy sets; fuzzy decision making computing with words multiple criteria decision making consensus

Special Issue Information

Dear Colleagues,

At present, there is a growing number of solutions that provide Artificial Intelligence (AI) and Machine Learning (ML) based systems. These solutions facilitate the creation of new products and services in many different fields. Sensor networks (SNs) are undergoing great expansion and development and the combination of both AI and SNs are now realities that are going to change our lives. The integration of these two technologies benefits other areas such as Industry 4.0, Internet of Things, Demotic Systems, etc. Furthermore, sensor networks (SNs) are widely used to collect environmental parameters in homes, buildings, vehicles, etc., where they are used as a source of information that aids the decision-making process and, in particular it allows systems to learn and to monitor activity. New AI and ML real time or execution time algorithms are needed, as well as different strategies to embed these algorithms in sensors. New clustering and classification techniques, reinforcement learning methods, or data quality approaches are required, as well as distributed AI algorithms.

This Special Issue calls for innovative work that explores new frontiers and challenges in the field of applying AI algorithms to SNs. As mentioned previously, this work will include new machine learning models, distributed AI proposals, hybrid AI systems, etc., as well as case studies or reviews of the state-of-the-art.

The topics of interest include, but are not limited to:

  • Artificial Intelligence models for Sensor Networks.
  • Machine Learning models for Sensor Networks.
  • Clustering and classification algorithms for SNs.
  • Deep and reinforcement learning for SNs.
  • Intelligence processing algorithms for SNs.
  • Intelligence image processing algorithms for SNs.
  • Big Data analytics for data processing from SNs.
  • Fuzzy Systems proposals for SNs.
  • Expert Systems for SNs.
  • Hybrid Systems for SNs
  • Intelligent real time algorithms for SNs.
  • Intelligent execution time algorithms for SNs.
  • Intelligent security proposals for WSNs.
  • Blockchain in WSNs.
  • Multi Agent Systems.
  • Organization Based Multiagent Systems.
  • Virtual Organizations.
  • Applications of AI in SN domains: energy, IoT, Industry 4.0, etc.

Prof. Dr. Juan Manuel Corchado Rodríguez
Prof. Dr. Enrique Herrera
Guest Editors

Manuscript Submission Information

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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 monthly 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 1800 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

  • Machine Learning
  • Artificial Intelligence
  • Learning
  • Fuzzy
  • ANN

Published Papers (12 papers)

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Research

Open AccessArticle Decentralized Online Simultaneous Localization and Mapping for Multi-Agent Systems
Sensors 2018, 18(8), 2612; https://doi.org/10.3390/s18082612
Received: 10 July 2018 / Revised: 5 August 2018 / Accepted: 7 August 2018 / Published: 9 August 2018
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Abstract
Planning tasks performed by a robotic agent require previous access to a map of the environment and the position where the agent is located. This creates a problem when the agent is placed in a new environment. To solve it, the RA must
[...] Read more.
Planning tasks performed by a robotic agent require previous access to a map of the environment and the position where the agent is located. This creates a problem when the agent is placed in a new environment. To solve it, the RA must execute the task known as Simultaneous Location and Mapping (SLAM) which locates the agent in the new environment while generating the map at the same time, geometrically or topologically. One of the big problems in SLAM is the amount of memory required for the RA to store the details of the environment map. In addition, environment data capture needs a robust processing unit to handle data representation, which in turn is reflected in a bigger RA unit with higher energy use and production costs. This article presents a design for a system capable of a decentralized implementation of SLAM that is based on the use of a system comprised of wireless agents capable of storing and distributing the map as it is being generated by the RA. The proposed system was validated in an environment with a surface area of 25 m 2 , in which it was capable of generating the topological map online, and without relying on external units connected to the system. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
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Open AccessArticle Attributes’ Importance for Zero-Shot Pose-Classification Based on Wearable Sensors
Sensors 2018, 18(8), 2485; https://doi.org/10.3390/s18082485
Received: 15 June 2018 / Revised: 24 July 2018 / Accepted: 26 July 2018 / Published: 1 August 2018
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Abstract
This paper presents a simple yet effective method for improving the performance of zero-shot learning (ZSL). ZSL classifies instances of unseen classes, from which no training data is available, by utilizing the attributes of the classes. Conventional ZSL methods have equally dealt with
[...] Read more.
This paper presents a simple yet effective method for improving the performance of zero-shot learning (ZSL). ZSL classifies instances of unseen classes, from which no training data is available, by utilizing the attributes of the classes. Conventional ZSL methods have equally dealt with all the available attributes, but this sometimes causes misclassification. This is because an attribute that is effective for classifying instances of one class is not always effective for another class. In this case, a metric of classifying the latter class can be undesirably influenced by the irrelevant attribute. This paper solves this problem by taking the importance of each attribute for each class into account when calculating the metric. In addition to the proposal of this new method, this paper also contributes by providing a dataset for pose classification based on wearable sensors, named HDPoseDS. It contains 22 classes of poses performed by 10 subjects with 31 IMU sensors across full body. To the best of our knowledge, it is the richest wearable-sensor dataset especially in terms of sensor density, and thus it is suitable for studying zero-shot pose/action recognition. The presented method was evaluated on HDPoseDS and outperformed relative improvement of 5.9% in comparison to the best baseline method. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
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Open AccessArticle Improving Classification Algorithms by Considering Score Series in Wireless Acoustic Sensor Networks
Sensors 2018, 18(8), 2465; https://doi.org/10.3390/s18082465
Received: 11 June 2018 / Revised: 22 July 2018 / Accepted: 27 July 2018 / Published: 30 July 2018
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Abstract
The reduction in size, power consumption and price of many sensor devices has enabled the deployment of many sensor networks that can be used to monitor and control several aspects of various habitats. More specifically, the analysis of sounds has attracted a huge
[...] Read more.
The reduction in size, power consumption and price of many sensor devices has enabled the deployment of many sensor networks that can be used to monitor and control several aspects of various habitats. More specifically, the analysis of sounds has attracted a huge interest in urban and wildlife environments where the classification of the different signals has become a major issue. Various algorithms have been described for this purpose, a number of which frame the sound and classify these frames, while others take advantage of the sequential information embedded in a sound signal. In the paper, a new algorithm is proposed that, while maintaining the frame-classification advantages, adds a new phase that considers and classifies the score series derived after frame labelling. These score series are represented using cepstral coefficients and classified using standard machine-learning classifiers. The proposed algorithm has been applied to a dataset of anuran calls and its results compared to the performance obtained in previous experiments on sensor networks. The main outcome of our research is that the consideration of score series strongly outperforms other algorithms and attains outstanding performance despite the noisy background commonly encountered in this kind of application. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
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Open AccessArticle Voiceprint Identification for Limited Dataset Using the Deep Migration Hybrid Model Based on Transfer Learning
Sensors 2018, 18(7), 2399; https://doi.org/10.3390/s18072399
Received: 20 May 2018 / Revised: 21 June 2018 / Accepted: 10 July 2018 / Published: 23 July 2018
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Abstract
The convolutional neural network (CNN) has made great strides in the area of voiceprint recognition; but it needs a huge number of data samples to train a deep neural network. In practice, it is too difficult to get a large number of training
[...] Read more.
The convolutional neural network (CNN) has made great strides in the area of voiceprint recognition; but it needs a huge number of data samples to train a deep neural network. In practice, it is too difficult to get a large number of training samples, and it cannot achieve a better convergence state due to the limited dataset. In order to solve this question, a new method using a deep migration hybrid model is put forward, which makes it easier to realize voiceprint recognition for small samples. Firstly, it uses Transfer Learning to transfer the trained network from the big sample voiceprint dataset to our limited voiceprint dataset for the further training. Fully-connected layers of a pre-training model are replaced by restricted Boltzmann machine layers. Secondly, the approach of Data Augmentation is adopted to increase the number of voiceprint datasets. Finally, we introduce fast batch normalization algorithms to improve the speed of the network convergence and shorten the training time. Our new voiceprint recognition approach uses the TLCNN-RBM (convolutional neural network mixed restricted Boltzmann machine based on transfer learning) model, which is the deep migration hybrid model that is used to achieve an average accuracy of over 97%, which is higher than that when using either CNN or the TL-CNN network (convolutional neural network based on transfer learning). Thus, an effective method for a small sample of voiceprint recognition has been provided. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
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Open AccessArticle Robust Face Recognition Using the Deep C2D-CNN Model Based on Decision-Level Fusion
Sensors 2018, 18(7), 2080; https://doi.org/10.3390/s18072080
Received: 20 May 2018 / Revised: 20 June 2018 / Accepted: 25 June 2018 / Published: 28 June 2018
Cited by 2 | PDF Full-text (6021 KB) | HTML Full-text | XML Full-text
Abstract
Given that facial features contain a wide range of identification information and cannot be completely represented by a single feature, the fusion of multiple features is particularly significant for achieving a robust face recognition performance, especially when there is a big difference between
[...] Read more.
Given that facial features contain a wide range of identification information and cannot be completely represented by a single feature, the fusion of multiple features is particularly significant for achieving a robust face recognition performance, especially when there is a big difference between the test sets and the training sets. This has been proven in both traditional and deep learning approaches. In this work, we proposed a novel method named C2D-CNN (color 2-dimensional principal component analysis (2DPCA)-convolutional neural network). C2D-CNN combines the features learnt from the original pixels with the image representation learnt by CNN, and then makes decision-level fusion, which can significantly improve the performance of face recognition. Furthermore, a new CNN model is proposed: firstly, we introduce a normalization layer in CNN to speed up the network convergence and shorten the training time. Secondly, the layered activation function is introduced to make the activation function adaptive to the normalized data. Finally, probabilistic max-pooling is applied so that the feature information is preserved to the maximum extent while maintaining feature invariance. Experimental results show that compared with the state-of-the-art method, our method shows better performance and solves low recognition accuracy caused by the difference between test and training datasets. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
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Open AccessArticle Online Model Updating and Dynamic Learning Rate-Based Robust Object Tracking
Sensors 2018, 18(7), 2046; https://doi.org/10.3390/s18072046
Received: 26 April 2018 / Revised: 8 June 2018 / Accepted: 25 June 2018 / Published: 26 June 2018
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Abstract
Robust visual tracking is a significant and challenging issue in computer vision-related research fields and has attracted an immense amount of attention from researchers. Due to various practical applications, many studies have been done that have introduced numerous algorithms. It is considered to
[...] Read more.
Robust visual tracking is a significant and challenging issue in computer vision-related research fields and has attracted an immense amount of attention from researchers. Due to various practical applications, many studies have been done that have introduced numerous algorithms. It is considered to be a challenging problem due to the unpredictability of various real-time situations, such as illumination variations, occlusion, fast motion, deformation, and scale variation, even though we only know the initial target position. To address these matters, we used a kernelized-correlation-filter-based translation filter with the integration of multiple features such as histogram of oriented gradients (HOG) and color attributes. These powerful features are useful to differentiate the target from the surrounding background and are effective for motion blur and illumination variations. To minimize the scale variation problem, we designed a correlation-filter-based scale filter. The proposed adaptive model’s updating and dynamic learning rate strategies based on a peak-to-sidelobe ratio effectively reduce model-drifting problems by avoiding noisy appearance changes. The experiment results show that our method provides the best performance compared to other methods, with a distance precision score of 79.9%, overlap success score of 59.0%, and an average running speed of 74 frames per second on the object tracking benchmark (OTB-2015). Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
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Open AccessArticle An Efficient Neural-Network-Based Microseismic Monitoring Platform for Hydraulic Fracture on an Edge Computing Architecture
Sensors 2018, 18(6), 1828; https://doi.org/10.3390/s18061828
Received: 3 May 2018 / Revised: 2 June 2018 / Accepted: 3 June 2018 / Published: 5 June 2018
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Abstract
Microseismic monitoring is one of the most critical technologies for hydraulic fracturing in oil and gas production. To detect events in an accurate and efficient way, there are two major challenges. One challenge is how to achieve high accuracy due to a poor
[...] Read more.
Microseismic monitoring is one of the most critical technologies for hydraulic fracturing in oil and gas production. To detect events in an accurate and efficient way, there are two major challenges. One challenge is how to achieve high accuracy due to a poor signal-to-noise ratio (SNR). The other one is concerned with real-time data transmission. Taking these challenges into consideration, an edge-computing-based platform, namely Edge-to-Center LearnReduce, is presented in this work. The platform consists of a data center with many edge components. At the data center, a neural network model combined with convolutional neural network (CNN) and long short-term memory (LSTM) is designed and this model is trained by using previously obtained data. Once the model is fully trained, it is sent to edge components for events detection and data reduction. At each edge component, a probabilistic inference is added to the neural network model to improve its accuracy. Finally, the reduced data is delivered to the data center. Based on experiment results, a high detection accuracy (over 96%) with less transmitted data (about 90%) was achieved by using the proposed approach on a microseismic monitoring system. These results show that the platform can simultaneously improve the accuracy and efficiency of microseismic monitoring. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
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Open AccessArticle Agreement Technologies for Energy Optimization at Home
Sensors 2018, 18(5), 1633; https://doi.org/10.3390/s18051633
Received: 10 April 2018 / Revised: 14 May 2018 / Accepted: 15 May 2018 / Published: 19 May 2018
Cited by 1 | PDF Full-text (13639 KB) | HTML Full-text | XML Full-text
Abstract
Nowadays, it is becoming increasingly common to deploy sensors in public buildings or homes with the aim of obtaining data from the environment and taking decisions that help to save energy. Many of the current state-of-the-art systems make decisions considering solely the environmental
[...] Read more.
Nowadays, it is becoming increasingly common to deploy sensors in public buildings or homes with the aim of obtaining data from the environment and taking decisions that help to save energy. Many of the current state-of-the-art systems make decisions considering solely the environmental factors that cause the consumption of energy. These systems are successful at optimizing energy consumption; however, they do not adapt to the preferences of users and their comfort. Any system that is to be used by end-users should consider factors that affect their wellbeing. Thus, this article proposes an energy-saving system, which apart from considering the environmental conditions also adapts to the preferences of inhabitants. The architecture is based on a Multi-Agent System (MAS), its agents use Agreement Technologies (AT) to perform a negotiation process between the comfort preferences of the users and the degree of optimization that the system can achieve according to these preferences. A case study was conducted in an office building, showing that the proposed system achieved average energy savings of 17.15%. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
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Open AccessArticle Residual Error Based Anomaly Detection Using Auto-Encoder in SMD Machine Sound
Sensors 2018, 18(5), 1308; https://doi.org/10.3390/s18051308
Received: 2 March 2018 / Revised: 17 April 2018 / Accepted: 20 April 2018 / Published: 24 April 2018
PDF Full-text (2264 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Detecting an anomaly or an abnormal situation from given noise is highly useful in an environment where constantly verifying and monitoring a machine is required. As deep learning algorithms are further developed, current studies have focused on this problem. However, there are too
[...] Read more.
Detecting an anomaly or an abnormal situation from given noise is highly useful in an environment where constantly verifying and monitoring a machine is required. As deep learning algorithms are further developed, current studies have focused on this problem. However, there are too many variables to define anomalies, and the human annotation for a large collection of abnormal data labeled at the class-level is very labor-intensive. In this paper, we propose to detect abnormal operation sounds or outliers in a very complex machine along with reducing the data-driven annotation cost. The architecture of the proposed model is based on an auto-encoder, and it uses the residual error, which stands for its reconstruction quality, to identify the anomaly. We assess our model using Surface-Mounted Device (SMD) machine sound, which is very complex, as experimental data, and state-of-the-art performance is successfully achieved for anomaly detection. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
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Open AccessArticle Application of Deep Learning Architectures for Accurate and Rapid Detection of Internal Mechanical Damage of Blueberry Using Hyperspectral Transmittance Data
Sensors 2018, 18(4), 1126; https://doi.org/10.3390/s18041126
Received: 5 March 2018 / Revised: 3 April 2018 / Accepted: 4 April 2018 / Published: 7 April 2018
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Abstract
Deep learning has become a widely used powerful tool in many research fields, although not much so yet in agriculture technologies. In this work, two deep convolutional neural networks (CNN), viz. Residual Network (ResNet) and its improved version named ResNeXt, are used to
[...] Read more.
Deep learning has become a widely used powerful tool in many research fields, although not much so yet in agriculture technologies. In this work, two deep convolutional neural networks (CNN), viz. Residual Network (ResNet) and its improved version named ResNeXt, are used to detect internal mechanical damage of blueberries using hyperspectral transmittance data. The original structure and size of hypercubes are adapted for the deep CNN training. To ensure that the models are applicable to hypercube, we adjust the number of filters in the convolutional layers. Moreover, a total of 5 traditional machine learning algorithms, viz. Sequential Minimal Optimization (SMO), Linear Regression (LR), Random Forest (RF), Bagging and Multilayer Perceptron (MLP), are performed as the comparison experiments. In terms of model assessment, k-fold cross validation is used to indicate that the model performance does not vary with the different combination of dataset. In real-world application, selling damaged berries will lead to greater interest loss than discarding the sound ones. Thus, precision, recall, and F1-score are also used as the evaluation indicators alongside accuracy to quantify the false positive rate. The first three indicators are seldom used by investigators in the agricultural engineering domain. Furthermore, ROC curves and Precision-Recall curves are plotted to visualize the performance of classifiers. The fine-tuned ResNet/ResNeXt achieve average accuracy and F1-score of 0.8844/0.8784 and 0.8952/0.8905, respectively. Classifiers SMO/ LR/RF/Bagging/MLP obtain average accuracy and F1-score of 0.8082/0.7606/0.7314/0.7113/0.7827 and 0.8268/0.7796/0.7529/0.7339/0.7971, respectively. Two deep learning models achieve better classification performance than the traditional machine learning methods. Classification for each testing sample only takes 5.2 ms and 6.5 ms respectively for ResNet and ResNeXt, indicating that the deep learning framework has great potential for online fruit sorting. The results of this study demonstrate the potential of deep CNN application on analyzing the internal mechanical damage of fruit. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
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Open AccessArticle Comparative Performance Analysis of Support Vector Machine, Random Forest, Logistic Regression and k-Nearest Neighbours in Rainbow Trout (Oncorhynchus Mykiss) Classification Using Image-Based Features
Sensors 2018, 18(4), 1027; https://doi.org/10.3390/s18041027
Received: 21 February 2018 / Revised: 25 March 2018 / Accepted: 27 March 2018 / Published: 29 March 2018
PDF Full-text (13503 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The main aim of this study was to develop a new objective method for evaluating the impacts of different diets on the live fish skin using image-based features. In total, one-hundred and sixty rainbow trout (Oncorhynchus mykiss) were fed either a
[...] Read more.
The main aim of this study was to develop a new objective method for evaluating the impacts of different diets on the live fish skin using image-based features. In total, one-hundred and sixty rainbow trout (Oncorhynchus mykiss) were fed either a fish-meal based diet (80 fish) or a 100% plant-based diet (80 fish) and photographed using consumer-grade digital camera. Twenty-three colour features and four texture features were extracted. Four different classification methods were used to evaluate fish diets including Random forest (RF), Support vector machine (SVM), Logistic regression (LR) and k-Nearest neighbours (k-NN). The SVM with radial based kernel provided the best classifier with correct classification rate (CCR) of 82% and Kappa coefficient of 0.65. Although the both LR and RF methods were less accurate than SVM, they achieved good classification with CCR 75% and 70% respectively. The k-NN was the least accurate (40%) classification model. Overall, it can be concluded that consumer-grade digital cameras could be employed as the fast, accurate and non-invasive sensor for classifying rainbow trout based on their diets. Furthermore, these was a close association between image-based features and fish diet received during cultivation. These procedures can be used as non-invasive, accurate and precise approaches for monitoring fish status during the cultivation by evaluating diet’s effects on fish skin. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
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Open AccessArticle An Internet of Things System for Underground Mine Air Quality Pollutant Prediction Based on Azure Machine Learning
Sensors 2018, 18(4), 930; https://doi.org/10.3390/s18040930
Received: 20 February 2018 / Revised: 15 March 2018 / Accepted: 16 March 2018 / Published: 21 March 2018
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Abstract
The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can
[...] Read more.
The implementation of wireless sensor networks (WSNs) for monitoring the complex, dynamic, and harsh environment of underground coal mines (UCMs) is sought around the world to enhance safety. However, previously developed smart systems are limited to monitoring or, in a few cases, can report events. Therefore, this study introduces a reliable, efficient, and cost-effective internet of things (IoT) system for air quality monitoring with newly added features of assessment and pollutant prediction. This system is comprised of sensor modules, communication protocols, and a base station, running Azure Machine Learning (AML) Studio over it. Arduino-based sensor modules with eight different parameters were installed at separate locations of an operational UCM. Based on the sensed data, the proposed system assesses mine air quality in terms of the mine environment index (MEI). Principal component analysis (PCA) identified CH4, CO, SO2, and H2S as the most influencing gases significantly affecting mine air quality. The results of PCA were fed into the ANN model in AML studio, which enabled the prediction of MEI. An optimum number of neurons were determined for both actual input and PCA-based input parameters. The results showed a better performance of the PCA-based ANN for MEI prediction, with R2 and RMSE values of 0.6654 and 0.2104, respectively. Therefore, the proposed Arduino and AML-based system enhances mine environmental safety by quickly assessing and predicting mine air quality. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
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