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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 February 2022) | Viewed by 18721

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Prof. Dr. Juan Francisco De Paz Santana

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Guest Editor

Expert Systems and Applications Lab, Faculty of Science, University of Salamanca, 37008 Salamanca, Spain
Interests: ambient intelligence; artificial intelligence; multi-agent systems; wireless sensor networks; big data; edge computing; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Dr. Gabriel Villarrubia González

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Guest Editor

Expert Systems and Applications Lab, Faculty of Science, University of Salamanca, 37008 Salamanca, Spain
Interests: artificial intelligence; multi-agent systems; ambient intelligence; wireless sensor networks; bigdata; edge computing; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Daer Colleagues,

The use of sensor networks and, more specifically, wireless sensor networks has allowed for the development of monitoring environments, which are capable of obtaining values to support decision-making in smart environments. To carry out such decision-making, it is necessary to apply artificial intelligence techniques capable of adapting to changes in smart environments in order to create systems that evolve autonomously over time. Currently, it is necessary to apply new information fusion techniques that allow for the processing of information at low and high levels to improve the accuracy of such systems. We are looking for new research and cases studies based on wireless sensor networks and information fusion techniques that utilize multiple sensor information for decision-making.

We invite you to submit contributions relating to software/hardware developments and new trends in adaptative techniques to process information from wireless sensor networks in smart environments.

Prof. Dr. Juan Francisco De Paz Santana
Dr. Gabriel Villarrubia González
Guest Editors

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Keywords

Fusion information
Wireless sensor networks
Blockchain
Smart contracts
Artificial intelligence
Multiagent systems
Ambient intelligence
IoT

Published Papers (6 papers)

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Research

21 pages, 783 KiB

Open AccessArticle

Performance and Security Evaluation on a Blockchain Architecture for License Plate Recognition Systems

by Iago Sestrem Ochôa, Valderi Reis Quietinho Leithardt, Leonardo Calbusch, Juan Francisco De Paz Santana, Wemerson Delcio Parreira, Laio Oriel Seman and Cesar Albenes Zeferino

Appl. Sci. 2021, 11(3), 1255; https://doi.org/10.3390/app11031255 - 29 Jan 2021

Cited by 17 | Viewed by 3535

Abstract

Since the early 2000s, life in cities has changed significantly due to the Internet of Things (IoT). This concept enables developers to integrate different devices collecting, storing, and processing a large amount of data, enabling new services to improve various professional and personal activities. However, privacy issues arise with a large amount of data generated, and solutions based on blockchain technology and smart contract have been developed to address these issues. Nevertheless, several issues must still be taken into account when developing blockchain architectures aimed at the IoT scenario because security flaws still exist in smart contracts, mainly due to the lack of ease when building the code. This article presents a blockchain storage architecture focused on license plate recognition (LPR) systems for smart cities focusing on privacy, performance, and security. The proposed architecture relies on the Ethereum platform. Each smart contract matches the privacy preferences of a license plate to be anonymized through public encryption. The storage of data captured by the LPR system can only be done if the smart contract enables it. However, in the case of motivation foreseen by the legislation, a competent user can change the smart contract and enable the storage of the data captured by the LPR system. Experimental results show that the performance of the proposed architecture is satisfactory, regarding the scalability of the built private network. Furthermore, tests on our smart contract using security and structure analysis tools on the developed script demonstrate that our solution is fraud-proof. The results obtained in all experiments bring evidence that our architecture is feasible to be used in real scenarios. Full article

(This article belongs to the Special Issue Wireless Sensor Networks in Smart Environments)

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19 pages, 2836 KiB

Open AccessArticle

Network Traffic Modeling in a Wi-Fi System with Intelligent Soil Moisture Sensors (WSN) Using IoT Applications for Potato Crops and ARIMA and SARIMA Time Series

by Alfonso José López Rivero, Carlos Andrés Martínez Alayón, Roberto Ferro, Daniel Hernández de la Iglesia and Vidal Alonso Secades

Appl. Sci. 2020, 10(21), 7702; https://doi.org/10.3390/app10217702 - 30 Oct 2020

Cited by 5 | Viewed by 2173

Abstract

This article presents the results obtained by analyzing the data traffic that originated in a system with intelligent soil moisture sensors (Wireless Sensor Network—WSN) that transmit through a wireless network. This study sought to integrate smart agriculture and IoT (Internet of Things) applications in potato crops in various rural settings. Using these measurements, the data analysis was performed through the ARIMA (autoregressive integrated moving average model) and SARIMA (seasonal autoregressive integrated moving average model) time series following the Box–Jenkins methodology. GRETL (Gnu Regression, Econometrics and Time-series Library) free software was used to generate a teletraffic behavior prediction model in a larger-scale implementation. The main objective was the creation of a model that allows an analysis and simulation about the behavior of the main performance parameters that a medium-scale WSN system would have for the monitoring of a crop. Thanks to this analysis, it will be possible to determine the technical characteristics that a sensor deployment should have in a specific area and for a specific crop. Full article

(This article belongs to the Special Issue Wireless Sensor Networks in Smart Environments)

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19 pages, 2806 KiB

Open AccessArticle

Hybrid Impedance-Admittance Control for Upper Limb Exoskeleton Using Electromyography

by Lucas D. L. da Silva, Thiago F. Pereira, Valderi R. Q. Leithardt, Laio O. Seman and Cesar A. Zeferino

Appl. Sci. 2020, 10(20), 7146; https://doi.org/10.3390/app10207146 - 14 Oct 2020

Cited by 14 | Viewed by 3168

Abstract

Exoskeletons are wearable mobile robots that combine various technologies to enable limb movement with greater strength and endurance, being used in several application areas, such as industry and medicine. In this context, this paper presents the development of a hybrid control method for exoskeletons, combining admission and impedance control based on electromyographic input signals. A proof of concept of a robotic arm with two degrees of freedom, mimicking the functions of a human’s upper limb, was built to evaluate the proposed control system. Through tests that measured the discrepancy between the angles of the human joint and the joint of the exoskeleton, it was possible to determine that the system remained within an acceptable error range. The average error is lower than 4.3%, and the robotic arm manages to mimic the movements of the upper limbs of a human in real-time. Full article

(This article belongs to the Special Issue Wireless Sensor Networks in Smart Environments)

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14 pages, 1747 KiB

Open AccessArticle

A Novel Digital Modulation Recognition Algorithm Based on Deep Convolutional Neural Network

by Kaiyuan Jiang, Jiawei Zhang, Haibin Wu, Aili Wang and Yuji Iwahori

Appl. Sci. 2020, 10(3), 1166; https://doi.org/10.3390/app10031166 - 09 Feb 2020

Cited by 31 | Viewed by 3672

Abstract

The modulation recognition of digital signals under non-cooperative conditions is one of the important research contents here. With the rapid development of artificial intelligence technology, deep learning theory is also increasingly being applied to the field of modulation recognition. In this paper, a novel digital signal modulation recognition algorithm is proposed, which has combined the InceptionResNetV2 network with transfer adaptation, called InceptionResnetV2-TA. Firstly, the received signal is preprocessed and generated the constellation diagram. Then, the constellation diagram is used as the input of the InceptionResNetV2 network to identify different kinds of signals. Transfer adaptation is used for feature extraction and SVM classifier is used to identify the modulation mode of digital signal. The constellation diagram of three typical signals, including Binary Phase Shift Keying(BPSK), Quadrature Phase Shift Keying(QPSK) and 8 Phase Shift Keying(8PSK), was made for the experiments. When the signal-to-noise ratio(SNR) is 4dB, the recognition rates of BPSK, QPSK and 8PSK are respectively 1.0, 0.9966 and 0.9633 obtained by InceptionResnetV2-TA, and at the same time, the recognition rate can be 3% higher than other algorithms. Compared with the traditional modulation recognition algorithms, the experimental results show that the proposed algorithm in this paper has a higher accuracy rate for digital signal modulation recognition at low SNR. Full article

(This article belongs to the Special Issue Wireless Sensor Networks in Smart Environments)

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13 pages, 441 KiB

Open AccessArticle

Tele-Treatment Application Design for Disable Patients with Wireless Sensors

by Alberto Arteta Albert, Luis Fernando de Mingo López and Nuria Gómez Blas

Appl. Sci. 2020, 10(3), 1142; https://doi.org/10.3390/app10031142 - 08 Feb 2020

Cited by 1 | Viewed by 2277

Abstract

This paper consists of the development of a system to help patients with different disabilities, affected by rare or chronic diseases or any kind of dependence through tele assistance, virtual interaction and intelligent monitoring. The main goal is to increase the quality of life of the minorities who cannot take full advantage of the healthcare system by providing an alternative way of monitoring them with the technology embedded in this paper. The result of the paper is not intended to be a single solution, but a modular system that allows the construction of an application that is able to measure the needs of a health administration and the patients. The paper also pursues an educational training to the facultative trainees in a new way to approach patient treatments. It can improve the quality of life of the patients by saving them time and other resources in moving to the Health center and the professionals can also save time as they can take advantage of the online treatments by using the proposed system. Full article

(This article belongs to the Special Issue Wireless Sensor Networks in Smart Environments)

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15 pages, 3026 KiB

Open AccessArticle

Heterogeneous Defect Prediction Based on Transfer Learning to Handle Extreme Imbalance

by Kaiyuan Jiang, Yutong Zhang, Haibin Wu, Aili Wang and Yuji Iwahori

Appl. Sci. 2020, 10(1), 396; https://doi.org/10.3390/app10010396 - 05 Jan 2020

Cited by 6 | Viewed by 2718

Abstract

Software systems are now ubiquitous and are used every day for automation purposes in personal and enterprise applications; they are also essential to many safety-critical and mission-critical systems, e.g., air traffic control systems, autonomous cars, and Supervisory Control And Data Acquisition (SCADA) systems. With the availability of massive storage capabilities, high speed Internet, and the advent of Internet of Things devices, modern software systems are growing in both size and complexity. Maintaining a high quality of such complex systems while manually keeping the error rate at a minimum is a challenge. This paper proposed a heterogeneous defect prediction method considering class extreme imbalance problem in real software datasets. In the first stage, Sampling with the Majority method (SWIM) based on Mahalanobis Distance is used to balance the dataset to reduce the influence of minority samples in defect data. Due to the negative impact of uncorrelated features on the classification algorithm, the second stage uses ensemble learning and joint similarity measurement to select the most relevant and representative features between the source project and the target project. The third phase realizes the transfer learning from the source project to the target project in the Grassmann manifold space. Our experiments, conducted using nine projects of three public domain software defect libraries and compared with four existing advanced methods to verify the effectiveness of the proposed method in this paper. The experimental results indicate that the proposed method is more accurate in terms of Area under curve (AUC). Full article

(This article belongs to the Special Issue Wireless Sensor Networks in Smart Environments)

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