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

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 29139

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Special Issue Editors

Dr. Ke Yan

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Guest Editor

Department of Building School of Design and Environment, National University of Singapore, Singapore 119077, Singapore
Interests: machine learning; AI; smart facilities; applied energy

Dr. Yang Xu

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Guest Editor

Department of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
Interests: distributed computing; blockchain; AI; cyber security

Prof. Dr. Fuhua Lin

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Guest Editor

School of Computing and Information Systems, Athabasca University, Athabasca, AB T9S 3A3, Canada
Interests: adaptive systems; intelligent systems; multiagent systems; virtual reality; reinforcement learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Qun Jin

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Guest Editor

Department of Human Informatics and Cognitive Sciences, Waseda University, Tokorozawa 359-1192, Japan
Interests: ubiquitous computing; big data; cyber security; blockchain; intelligence computing

Special Issue Information

Dear Colleagues,

AI, machine learning and big data technology applications in the field of the Internet of Things (IoT) have received increasing attention from both data scientists and engineers. With the massive amount of data or information being collected using sensors, AI technologies are training data science models for next-generation IoT operations, such as smart facility management, IoT predictive maintenance, smart cities, cyber security of information exchange, intelligence computation for security measures, blockchain, etc. The whole process of data collection through sensors and data processing using AI is called big data analysis as regards sensors and applications.

This Special Issue intends to provide an international forum for researchers to showcase the up-to-date results on AI, machine learning, big data and cyber security technologies in the fields of sensors. Recent progress and future directions of AI in sensors and applications will be investigated. This Special Issue also intends to bring together impressive efforts in computer science and various engineering fields in relation to finding common and cross-discipline research topics. This Special Issue will include, but is not limited to, the following topics:

- AI-enhanced facility management;

- Human-centric ubiquitous computing;

- AI-based computer vision topics on sensor-delivered images;

- Big data research with sensors and applications;

- IoT applications with sensors;

- Cyber-physics systems with sensors;

- Intelligent devices and instruments for smart building/city design;

- Machine learning in IoT;

- Blockchain applications in IoT with sensors;

- Security and privacy in intelligent IoT systems;

- Building Information Modeling (BIM) and its applications;

- IoT and sensors enabled smart healthcare;

- Behavior and cognitive informatics with smart sensors.

Dr. Ke Yan
Dr. Yang Xu
Prof. Dr. Fuhua Lin
Prof. Dr. Qun Jin
Guest Editors

Manuscript Submission Information

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Keywords

artificial intelligence
sensor
sensor networks
smart facility
Internet of Things
big data
cyber-physical system
cyber security and privacy
predictive maintenance with sensors
smart building
smart city
building information modeling
ubiquitous computing
blockchain
facility management
Smart Health
Cyber Learning

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Published Papers (11 papers)

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Research

19 pages, 1003 KiB

Open AccessArticle

AirMLP: A Multilayer Perceptron Neural Network for Temporal Correction of PM2.5 Values in Turin

by Martina Casari, Laura Po and Leonardo Zini

Sensors 2023, 23(23), 9446; https://doi.org/10.3390/s23239446 - 27 Nov 2023

Cited by 4 | Viewed by 1190

Abstract

In recent times, pollution has emerged as a significant global concern, with European regulations stipulating limits on PM 2.5 particle levels. Addressing this challenge necessitates innovative approaches. Smart low-cost sensors suffer from imprecision, and can not replace legal stations in terms of accuracy, however, their potential to amplify the capillarity of air quality evaluation on the territory is not under discussion. In this paper, we propose an AI system to correct PM 2.5 levels in low-cost sensor data. Our research focuses on data from Turin, Italy, emphasizing the impact of humidity on low-cost sensor accuracy. In this study, different Neural Network architectures that vary the number of neurons per layer, consecutive records and batch sizes were used and compared to gain a deeper understanding of the network’s performance under various conditions. The AirMLP7-1500 model, with an impressive R-squared score of 0.932, stands out for its ability to correct PM 2.5 measurements. While our approach is tailored to the city of Turin, it offers a systematic methodology for the definition of those models and holds the promise to significantly improve the accuracy of air quality data collected from low-cost sensors, increasing the awareness of citizens and municipalities about this critical environmental information. Full article

(This article belongs to the Special Issue AI and Big Data Analytics in Sensors and Applications)

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22 pages, 1394 KiB

Open AccessArticle

Anomaly Detection in a Smart Industrial Machinery Plant Using IoT and Machine Learning

by Angel Jaramillo-Alcazar, Jaime Govea and William Villegas-Ch

Sensors 2023, 23(19), 8286; https://doi.org/10.3390/s23198286 - 7 Oct 2023

Cited by 3 | Viewed by 2731

Abstract

In an increasingly technology-driven world, the security of Internet-of-Things systems has become a top priority. This article presents a study on the implementation of security solutions in an innovative manufacturing plant using IoT and machine learning. The research was based on collecting historical data from telemetry sensors, IoT cameras, and control devices in a smart manufacturing plant. The data provided the basis for training machine learning models, which were used for real-time anomaly detection. After training the machine learning models, we achieved a 13% improvement in the anomaly detection rate and a 3% decrease in the false positive rate. These results significantly impacted plant efficiency and safety, with faster and more effective responses seen to unusual events. The results showed that there was a significant impact on the efficiency and safety of the smart manufacturing plant. Improved anomaly detection enabled faster and more effective responses to unusual events, decreasing critical incidents and improving overall security. Additionally, algorithm optimization and IoT infrastructure improved operational efficiency by reducing unscheduled downtime and increasing resource utilization. This study highlights the effectiveness of machine learning-based security solutions by comparing the results with those of previous research on IoT security and anomaly detection in industrial environments. The adaptability of these solutions makes them applicable in various industrial and commercial environments. Full article

(This article belongs to the Special Issue AI and Big Data Analytics in Sensors and Applications)

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16 pages, 6031 KiB

Open AccessArticle

An Efficient Compression Method for Lightning Electromagnetic Pulse Signal Based on Convolutional Neural Network and Autoencoder

by Jinhua Guo, Jiaquan Wang, Fang Xiao, Xiao Zhou, Yongsheng Liu and Qiming Ma

Sensors 2023, 23(8), 3908; https://doi.org/10.3390/s23083908 - 12 Apr 2023

Cited by 2 | Viewed by 1782

Abstract

Advances in technology have facilitated the development of lightning research and data processing. The electromagnetic pulse signals emitted by lightning (LEMP) can be collected by very low frequency (VLF)/low frequency (LF) instruments in real time. The storage and transmission of the obtained data is a crucial link, and a good compression method can improve the efficiency of this process. In this paper, a lightning convolutional stack autoencoder (LCSAE) model for compressing LEMP data was designed, which converts the data into low-dimensional feature vectors through the encoder part and reconstructs the waveform through the decoder part. Finally, we investigated the compression performance of the LCSAE model for LEMP waveform data under different compression ratios. The results show that the compression performance is positively correlated with the minimum feature of the neural network extraction model. When the compressed minimum feature is 64, the average coefficient of determination

R^{2}

of the reconstructed waveform and the original waveform can reach 96.7%. It can effectively solve the problem regarding the compression of LEMP signals collected by the lightning sensor and improve the efficiency of remote data transmission. Full article

(This article belongs to the Special Issue AI and Big Data Analytics in Sensors and Applications)

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16 pages, 6179 KiB

Open AccessArticle

A Solar Irradiance Forecasting Framework Based on the CEE-WGAN-LSTM Model

by Qianqian Li, Dongping Zhang and Ke Yan

Sensors 2023, 23(5), 2799; https://doi.org/10.3390/s23052799 - 3 Mar 2023

Cited by 10 | Viewed by 2423

Abstract

With the rapid development of solar energy plants in recent years, the accurate prediction of solar power generation has become an important and challenging problem in modern intelligent grid systems. To improve the forecasting accuracy of solar energy generation, an effective and robust decomposition-integration method for two-channel solar irradiance forecasting is proposed in this study, which uses complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), a Wasserstein generative adversarial network (WGAN), and a long short-term memory network (LSTM). The proposed method consists of three essential stages. First, the solar output signal is divided into several relatively simple subsequences using the CEEMDAN method, which has noticeable frequency differences. Second, high and low-frequency subsequences are predicted using the WGAN and LSTM models, respectively. Last, the predicted values of each component are integrated to obtain the final prediction results. The developed model uses data decomposition technology, together with advanced machine learning (ML) and deep learning (DL) models to identify the appropriate dependencies and network topology. The experiments show that compared with many traditional prediction methods and decomposition-integration models, the developed model can produce accurate solar output prediction results under different evaluation criteria. Compared to the suboptimal model, the MAEs, MAPEs, and RMSEs of the four seasons decreased by 3.51%, 6.11%, and 2.25%, respectively. Full article

(This article belongs to the Special Issue AI and Big Data Analytics in Sensors and Applications)

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18 pages, 4863 KiB

Open AccessArticle

Data-Augmented Deep Learning Models for Abnormal Road Manhole Cover Detection

by Dongping Zhang, Xuecheng Yu, Li Yang, Daying Quan, Hongmei Mi and Ke Yan

Sensors 2023, 23(5), 2676; https://doi.org/10.3390/s23052676 - 1 Mar 2023

Cited by 4 | Viewed by 2855

Abstract

Anomalous road manhole covers pose a potential risk to road safety in cities. In the development of smart cities, computer vision techniques use deep learning to automatically detect anomalous manhole covers to avoid these risks. One important problem is that a large amount of data are required to train a road anomaly manhole cover detection model. The number of anomalous manhole covers is usually small, which makes it a challenge to create training datasets quickly. To expand the dataset and improve the generalization of the model, researchers usually copy and paste samples from the original data to other data in order to achieve data augmentation. In this paper, we propose a new data augmentation method, which uses data that do not exist in the original dataset as samples to automatically select the pasting position of manhole cover samples and predict the transformation parameters via visual prior experience and perspective transformations, making it more accurately capture the actual shape of manhole covers on a road. Without using other data enhancement processes, our method raises the mean average precision (mAP) by at least 6.8 compared with the baseline model. Full article

(This article belongs to the Special Issue AI and Big Data Analytics in Sensors and Applications)

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18 pages, 5617 KiB

Open AccessArticle

Pred-SF: A Precipitation Prediction Model Based on Deep Neural Networks

by Rongnian Tang, Pu Zhang, Jingjin Wu, Youlong Chen, Lingyu Dong, Song Tang and Chuang Li

Sensors 2023, 23(5), 2609; https://doi.org/10.3390/s23052609 - 27 Feb 2023

Cited by 2 | Viewed by 1826

Abstract

How to predict precipitation accurately and efficiently is the key and difficult problem in the field of weather forecasting. At present, we can obtain accurate meteorological data through many high-precision weather sensors and use them to forecast precipitation. However, the common numerical weather forecasting methods and radar echo extrapolation methods have insurmountable defects. Based on some common characteristics of meteorological data, this paper proposes a Pred-SF model for precipitation prediction in target areas. The model focuses on the combination of multiple meteorological modal data to carry out self-cyclic prediction and a step-by-step prediction structure. The model divides the precipitation prediction into two steps. In the first step, the spatial encoding structure and PredRNN-V2 network are used to construct the autoregressive spatio-temporal prediction network for the multi-modal data, and the preliminary predicted value of the multi-modal data is generated frame by frame. In the second step, the spatial information fusion network is used to further extract and fuse the spatial characteristics of the preliminary predicted value and, finally, output the predicted precipitation value of the target region. In this paper, ERA5 multi-meteorological mode data and GPM precipitation measurement data are used for testing to predict the continuous precipitation of a specific area for 4 h. The experimental results show that Pred-SF has strong precipitation prediction ability. Some comparative experiments were also set up for comparison to demonstrate the advantages of the combined prediction method of multi-modal data and the stepwise prediction method of Pred-SF. Full article

(This article belongs to the Special Issue AI and Big Data Analytics in Sensors and Applications)

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21 pages, 8069 KiB

Open AccessEditor’s ChoiceArticle

Anomaly Detection and Repairing for Improving Air Quality Monitoring

by Federica Rollo, Chiara Bachechi and Laura Po

Sensors 2023, 23(2), 640; https://doi.org/10.3390/s23020640 - 6 Jan 2023

Cited by 12 | Viewed by 3607

Abstract

Clean air in cities improves our health and overall quality of life and helps fight climate change and preserve our environment. High-resolution measures of pollutants’ concentrations can support the identification of urban areas with poor air quality and raise citizens’ awareness while encouraging more sustainable behaviors. Recent advances in Internet of Things (IoT) technology have led to extensive use of low-cost air quality sensors for hyper-local air quality monitoring. As a result, public administrations and citizens increasingly rely on information obtained from sensors to make decisions in their daily lives and mitigate pollution effects. Unfortunately, in most sensing applications, sensors are known to be error-prone. Thanks to Artificial Intelligence (AI) technologies, it is possible to devise computationally efficient methods that can automatically pinpoint anomalies in those data streams in real time. In order to enhance the reliability of air quality sensing applications, we believe that it is highly important to set up a data-cleaning process. In this work, we propose AIrSense, a novel AI-based framework for obtaining reliable pollutant concentrations from raw data collected by a network of low-cost sensors. It enacts an anomaly detection and repairing procedure on raw measurements before applying the calibration model, which converts raw measurements to concentration measurements of gasses. There are very few studies of anomaly detection in raw air quality sensor data (millivolts). Our approach is the first that proposes to detect and repair anomalies in raw data before they are calibrated by considering the temporal sequence of the measurements and the correlations between different sensor features. If at least some previous measurements are available and not anomalous, it trains a model and uses the prediction to repair the observations; otherwise, it exploits the previous observation. Firstly, a majority voting system based on three different algorithms detects anomalies in raw data. Then, anomalies are repaired to avoid missing values in the measurement time series. In the end, the calibration model provides the pollutant concentrations. Experiments conducted on a real dataset of 12,000 observations produced by 12 low-cost sensors demonstrated the importance of the data-cleaning process in improving calibration algorithms’ performances. Full article

(This article belongs to the Special Issue AI and Big Data Analytics in Sensors and Applications)

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17 pages, 4801 KiB

Open AccessArticle

Short-Term Photovoltaic Power Forecasting Based on Historical Information and Deep Learning Methods

by Xianchao Guo, Yuchang Mo and Ke Yan

Sensors 2022, 22(24), 9630; https://doi.org/10.3390/s22249630 - 8 Dec 2022

Cited by 9 | Viewed by 2590

Abstract

The accurate prediction of photovoltaic (PV) power is essential for planning power systems and constructing intelligent grids. However, this has become difficult due to the intermittency and instability of PV power data. This paper introduces a deep learning framework based on 7.5 min-ahead and 15 min-ahead approaches to predict short-term PV power. Specifically, we propose a hybrid model based on singular spectrum analysis (SSA) and bidirectional long short-term memory (BiLSTM) networks with the Bayesian optimization (BO) algorithm. To begin, the SSA decomposes the PV power series into several sub-signals. Then, the BO algorithm automatically adjusts hyperparameters for the deep neural network architecture. Following that, parallel BiLSTM networks predict the value of each component. Finally, the prediction of the sub-signals is summed to generate the final prediction results. The performance of the proposed model is investigated using two datasets collected from real-world rooftop stations in eastern China. The 7.5 min-ahead predictions generated by the proposed model can reduce up to 380.51% error, and the 15 min-ahead predictions decrease by up to 296.01% error. The experimental results demonstrate the superiority of the proposed model in comparison to other forecasting methods. Full article

(This article belongs to the Special Issue AI and Big Data Analytics in Sensors and Applications)

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18 pages, 545 KiB

Open AccessArticle

Multiclass Classification of Metrologically Resourceful Tripartite Quantum States with Deep Neural Networks

by Syed Muhammad Abuzar Rizvi, Naema Asif, Muhammad Shohibul Ulum, Trung Q. Duong and Hyundong Shin

Sensors 2022, 22(18), 6767; https://doi.org/10.3390/s22186767 - 7 Sep 2022

Cited by 1 | Viewed by 1916

Abstract

Quantum entanglement is a unique phenomenon of quantum mechanics, which has no classical counterpart and gives quantum systems their advantage in computing, communication, sensing, and metrology. In quantum sensing and metrology, utilizing an entangled probe state enhances the achievable precision more than its classical counterpart. Noise in the probe state preparation step can cause the system to output unentangled states, which might not be resourceful. Hence, an effective method for the detection and classification of tripartite entanglement is required at that step. However, current mathematical methods cannot robustly classify multiclass entanglement in tripartite quantum systems, especially in the case of mixed states. In this paper, we explore the utility of artificial neural networks for classifying the entanglement of tripartite quantum states into fully separable, biseparable, and fully entangled states. We employed Bell’s inequality for the dataset of tripartite quantum states and train the deep neural network for multiclass classification. This entanglement classification method is computationally efficient due to using a small number of measurements. At the same time, it also maintains generalization by covering a large Hilbert space of tripartite quantum states. Full article

(This article belongs to the Special Issue AI and Big Data Analytics in Sensors and Applications)

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9 pages, 1708 KiB

Open AccessArticle

Optimization of Magnetoplasmonic ε-Near-Zero Nanostructures Using a Genetic Algorithm

by Felipe A. P. de Figueiredo, Edwin Moncada-Villa and Jorge Ricardo Mejía-Salazar

Sensors 2022, 22(15), 5789; https://doi.org/10.3390/s22155789 - 3 Aug 2022

Cited by 2 | Viewed by 1550

Abstract

Magnetoplasmonic permittivity-near-zero (

ε

-near-zero) nanostructures hold promise for novel highly integrated (bio)sensing devices. These platforms merge the high-resolution sensing from the magnetoplasmonic approach with the

ε

-near-zero-based light-to-plasmon coupling (instead of conventional gratings or bulky prism couplers), providing a way for sensing [...] Read more.

Magnetoplasmonic permittivity-near-zero (

ε

-near-zero) nanostructures hold promise for novel highly integrated (bio)sensing devices. These platforms merge the high-resolution sensing from the magnetoplasmonic approach with the

ε

-near-zero-based light-to-plasmon coupling (instead of conventional gratings or bulky prism couplers), providing a way for sensing devices with higher miniaturization levels. However, the applications are mostly hindered by tedious and time-consuming numerical analyses, due to the lack of an analytical relation for the phase-matching condition. There is, therefore, a need to develop mechanisms that enable the exploitation of magnetoplasmonic

ε

-near-zero nanostructures’ capabilities. In this work, we developed a genetic algorithm (GA) for the rapid design (in a few minutes) of magnetoplasmonic nanostructures with optimized TMOKE (transverse magneto-optical Kerr effect) signals and magnetoplasmonic sensing. Importantly, to illustrate the power and simplicity of our approach, we designed a magnetoplasmonic

ε

-near-zero sensing platform with a sensitivity higher than

56^{\circ} / RIU

and a figure of merit in the order of

10^{2}

. These last results, higher than any previous magnetoplasmonic

ε

-near-zero sensing approach, were obtained by the GA intelligent program in times ranging from 2 to 5 min (using a simple inexpensive dual-core CPU computer). Full article

(This article belongs to the Special Issue AI and Big Data Analytics in Sensors and Applications)

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18 pages, 3455 KiB

Open AccessArticle

Analysis on the Subdivision of Skilled Mowing Movements on Slopes

by Bo Wu, Yuan Wu, Shoji Nishimura and Qun Jin

Sensors 2022, 22(4), 1372; https://doi.org/10.3390/s22041372 - 10 Feb 2022

Cited by 7 | Viewed by 2196

Abstract

Owing to the aging of the rural population in the hilly and mountainous areas of Japan, mowing on narrow ridges and steep slopes is done manually by the elderly—individuals over 65 years of age. Studies have shown that many accidents that occurred during mowing were caused by workers’ unstable posture, especially when mowing on steep surfaces where there is a high risk of falling. It is necessary to analyze the body movements of mowing workers to elucidate the elements related to the risk of falls. Therefore, in this study, based on a high-precision motion-capture device and a series of experiments with elderly, skilled mowing workers, we focused on the movements of mowing. We sought to identify effective and safe mowing patterns and the factors that lead to the risk of falls. In various mowing styles, compared to the stride (S) and downward (D) mowing patterns, the basic (B) and moving (M) patterns were the most efficient; however, the risk of falls was also the highest among these patterns. While mowing, workers need to pay more attention to their arm strength and take appropriate measures to reduce the risk of falls according to their age and physique. The results can be used as data for the development of fall-detection systems and offer useful insights for the training of new mowing workers. Full article

(This article belongs to the Special Issue AI and Big Data Analytics in Sensors and Applications)

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