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Disruptive Technologies and Wireless Sensor Network Communication Algorithms

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

Deadline for manuscript submissions: closed (10 May 2023) | Viewed by 37802

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Special Issue Editors

Dr. Cheong Ghil Kim

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Department of Computer Science, Namseoul University, Cheonan 31020, Republic of Korea
Interests: mobile computing; 5G; wireless sensor networks; embedded systems
Special Issues, Collections and Topics in MDPI journals

Dr. Gyanendra Prasad Joshi

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Guest Editor

Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea
Interests: MAC; routing protocols for next-generation wireless networks; wireless sensor networks; cognitive radio networks; RFID systems; IoT; smart city; deep learning; digital convergence
Special Issues, Collections and Topics in MDPI journals

Dr. Chayapol Kamyod

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Guest Editor

School of Information Technology, Mae Fah Luang University, Chiang Rai 57100, Thailand
Interests: computer network and system; wireless sensor networks; embedded technology; IoT application
Special Issues, Collections and Topics in MDPI journals

Dr. Kyoungro Yoon

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Guest Editor

Department of Smart ICT Convergence Engineering, Konkuk University, Seoul 05029, Republic of Korea
Interests: multimedia information systems; computer vision; deep learning; digital twin; metaverse platform
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advancement of 5G, embedded, and sensor technologies has resulted in the rapid development of many services and applications. These evolutions will enable development and innovation in a variety of fields, including ultra-high-definition multimedia streaming and mixed reality, massive machine type communication, and ultra-reliable and low-latency communication. Furthermore, the Internet of Things (IoT), automation, and smart system applications will support new business and industrial innovation.

This Special Issue aims to represent the most recent advances in disruptive and sensor technology for business and industrial applications. We welcome contributions in all areas of communication and networks, including sensors, embedded systems, multimedia (AR, VR, MR), IoT, IoM, smart and automation systems, including automation systems, signal processing algorithms, and new or disruptive applications. These include, but are not limited to:

5G and next generation networks;
Mixed multimedia contents and applications;
Signal processing algorithms;
Deep learning;
Wireless sensor and networks;
Disruption technology and algorithms;
Industrial 4.0 and automation system;
Resilience and reliability of the system;
Smart grid and smart systems.

Dr. Cheong Ghil Kim
Dr. Gyanendra Prasad Joshi
Dr. Chayapol Kamyod
Dr. Kyoungro Yoon
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 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

5G
sensors
mixed multimedia
algorithms
wireless sensor
networks
deep learning
industry 4.0
automation
disruption
innovation
reliability

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Related Special Issue

Disruptive Technologies and Wireless Sensor Network Communication Algorithms (2nd Edition) in Sensors (2 articles)

Published Papers (13 papers)

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Research

12 pages, 1638 KiB

Open AccessCommunication

An Implementation of Inverse Cosine Hardware for Sound Rendering Applications

by Jinyoung Lee, Cheong-Ghil Kim, Yeon-Kug Moon and Woo-Chan Park

Sensors 2023, 23(15), 6731; https://doi.org/10.3390/s23156731 - 27 Jul 2023

Viewed by 1191

Abstract

Sound rendering is the process of determining the sound propagation path from an audio source to a listener and generating 3D sound based on it. This task demands complex calculations, including trigonometric functions. This paper presents hardware-based inverse cosine function calculations using the table method and linear approximation. This approach maintains a high accuracy while limiting hardware size for suitability in sound rendering applications. Consequently, our proposed hardware-based inverse cosine calculation method is a valuable tool for achieving high efficiency and accuracy in 3D sound rendering. Full article

(This article belongs to the Special Issue Disruptive Technologies and Wireless Sensor Network Communication Algorithms)

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11 pages, 667 KiB

Open AccessArticle

Development and Effectiveness Verification of Metaverse Cognitive Therapy Contents for MCI Patients

by Gi Sung Oh, Jehyun Kim, Wonjun Jeong, Seokhee Oh and Taeg Keun Whangbo

Sensors 2023, 23(13), 6010; https://doi.org/10.3390/s23136010 - 28 Jun 2023

Cited by 3 | Viewed by 1455

Abstract

It is very important to prevent dementia by intervening in advance in the stage of mild cognitive impairment, which is the pre-stage of dementia. Recently, cognitive therapy research using metaverse has been on the rise. We propose a way to utilize metaverse cognitive therapy content as a non-drug treatment method of mild cognitive impairment patients. This paper shows the results of clinical trials using metaverse cognitive therapy contents developed by us. We collected data from MCI patient groups and normal groups through MMSE-KC tests and in-content data collection systems. We conducted paired t-tests and repeat measurement ANOVA based on the collected data. The results of this study show how metaverse cognitive therapy content affects MCI patients, and suggest various factors to be considered when creating functional content. Full article

(This article belongs to the Special Issue Disruptive Technologies and Wireless Sensor Network Communication Algorithms)

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16 pages, 1918 KiB

Open AccessArticle

Contrastive Learning-Based Anomaly Detection for Actual Corporate Environments

by Gi-taek An, Jung-min Park and Kyung-soon Lee

Sensors 2023, 23(10), 4764; https://doi.org/10.3390/s23104764 - 15 May 2023

Viewed by 2068

Abstract

Information systems play an important role in business management, especially in personnel, budget, and financial management. If an anomaly ensues in an information system, all operations are paralyzed until their recovery. In this study, we propose a method for collecting and labeling datasets from actual operating systems in corporate environments for deep learning. The construction of a dataset from actual operating systems in a company’s information system involves constraints. Collecting anomalous data from these systems is challenging because of the need to maintain system stability. Even with data collected over a long period, the training dataset may have an imbalance of normal and anomalous data. We propose a method that utilizes contrastive learning with data augmentation through negative sampling for anomaly detection, which is particularly suitable for small datasets. To evaluate the effectiveness of the proposed method, we compared it with traditional deep learning models, such as the convolutional neural network (CNN) and long short-term memory (LSTM). The proposed method achieved a true positive rate (TPR) of 99.47%, whereas CNN and LSTM achieved TPRs of 98.8% and 98.67%, respectively. The experimental results demonstrate the method’s effectiveness in utilizing contrastive learning and detecting anomalies in small datasets from a company’s information system. Full article

(This article belongs to the Special Issue Disruptive Technologies and Wireless Sensor Network Communication Algorithms)

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13 pages, 2648 KiB

Open AccessArticle

Leveraging Metaheuristic Unequal Clustering for Hotspot Elimination in Energy-Aware Wireless Sensor Networks

by Hadeel Alsolai, Mashael Maashi, Muhammad Kashif Saeed, Abdullah Mohamed, Mohammed Assiri, Sitelbanat Abdelbagi, Suhanda Drar and Amgad Atta Abdelmageed

Sensors 2023, 23(5), 2636; https://doi.org/10.3390/s23052636 - 27 Feb 2023

Cited by 6 | Viewed by 1800

Abstract

Wireless sensor networks (WSNs) are becoming a significant technology for ubiquitous living and continue to be involved in active research because of their varied applications. Energy awareness will be a critical design problem in WSNs. Clustering is a widespread energy-efficient method and grants several benefits such as scalability, energy efficiency, less delay, and lifetime, but it results in hotspot issues. To solve this, unequal clustering (UC) has been presented. In UC, the size of the cluster differs with the distance to the base station (BS). This paper devises an improved tuna-swarm-algorithm-based unequal clustering for hotspot elimination (ITSA-UCHSE) technique in an energy-aware WSN. The ITSA-UCHSE technique intends to resolve the hotspot problem and uneven energy dissipation in the WSN. In this study, the ITSA is derived from the use of a tent chaotic map with the traditional TSA. In addition, the ITSA-UCHSE technique computes a fitness value based on energy and distance metrics. Moreover, the cluster size determination via the ITSA-UCHSE technique helps to address the hotspot issue. To demonstrate the enhanced performance of the ITSA-UCHSE approach, a series of simulation analyses were conducted. The simulation values stated that the ITSA-UCHSE algorithm has reached improved results over other models. Full article

(This article belongs to the Special Issue Disruptive Technologies and Wireless Sensor Network Communication Algorithms)

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19 pages, 796 KiB

Open AccessArticle

Fusion-Based Body-Worn IoT Sensor Platform for Gesture Recognition of Autism Spectrum Disorder Children

by Farman Ullah, Najah Abed AbuAli, Asad Ullah, Rehmat Ullah, Uzma Abid Siddiqui and Afsah Abid Siddiqui

Sensors 2023, 23(3), 1672; https://doi.org/10.3390/s23031672 - 3 Feb 2023

Cited by 5 | Viewed by 3631

Abstract

The last decade’s developments in sensor technologies and artificial intelligence applications have received extensive attention for daily life activity recognition. Autism spectrum disorder (ASD) in children is a neurological development disorder that causes significant impairments in social interaction, communication, and sensory action deficiency. Children with ASD have deficits in memory, emotion, cognition, and social skills. ASD affects children’s communication skills and speaking abilities. ASD children have restricted interests and repetitive behavior. They can communicate in sign language but have difficulties communicating with others as not everyone knows sign language. This paper proposes a body-worn multi-sensor-based Internet of Things (IoT) platform using machine learning to recognize the complex sign language of speech-impaired children. Optimal sensor location is essential in extracting the features, as variations in placement result in an interpretation of recognition accuracy. We acquire the time-series data of sensors, extract various time-domain and frequency-domain features, and evaluate different classifiers for recognizing ASD children’s gestures. We compare in terms of accuracy the decision tree (DT), random forest, artificial neural network (ANN), and k-nearest neighbour (KNN) classifiers to recognize ASD children’s gestures, and the results showed more than 96% recognition accuracy. Full article

(This article belongs to the Special Issue Disruptive Technologies and Wireless Sensor Network Communication Algorithms)

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17 pages, 6114 KiB

Open AccessArticle

Multi-Threaded Sound Propagation Algorithm to Improve Performance on Mobile Devices

by Eunjae Kim, Sukwon Choi, Cheong Ghil Kim and Woo-Chan Park

Sensors 2023, 23(2), 973; https://doi.org/10.3390/s23020973 - 14 Jan 2023

Cited by 3 | Viewed by 2272

Abstract

We propose a multi-threaded algorithm that can improve the performance of geometric acoustic (GA)-based sound propagation algorithms in mobile devices. In general, sound propagation algorithms require high computational cost because they perform based on ray tracing algorithms. For this reason, it is difficult to operate sound propagation algorithms in mobile environments. To solve this problem, we processed the early reflection and late reverberation steps in parallel and verified the performance in four scenes based on eight sound sources. The experimental results showed that the performance of the proposed method was on average 1.77 times better than that of the single-threaded method, demonstrating that our algorithm can improve the performance of mobile devices. Full article

(This article belongs to the Special Issue Disruptive Technologies and Wireless Sensor Network Communication Algorithms)

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22 pages, 9194 KiB

Open AccessArticle

Construction Site Safety Management: A Computer Vision and Deep Learning Approach

by Jaekyu Lee and Sangyub Lee

Sensors 2023, 23(2), 944; https://doi.org/10.3390/s23020944 - 13 Jan 2023

Cited by 24 | Viewed by 10173

Abstract

In this study, we used image recognition technology to explore different ways to improve the safety of construction workers. Three object recognition scenarios were designed for safety at a construction site, and a corresponding object recognition model was developed for each scenario. The first object recognition model checks whether there are construction workers at the site. The second object recognition model assesses the risk of falling (falling off a structure or falling down) when working at an elevated position. The third object recognition model determines whether the workers are appropriately wearing safety helmets and vests. These three models were newly created using the image data collected from the construction sites and synthetic image data collected from the virtual environment based on transfer learning. In particular, we verified an artificial intelligence model based on a virtual environment in this study. Thus, simulating and performing tests on worker falls and fall injuries, which are difficult to re-enact by humans, are efficient algorithm verification methods. The verification and synthesis data acquisition method based on a virtual environment is one of the main contributions of this study. This paper describes the overall application development approach, including the structure and method used to collect the construction site image data, structure of the training image dataset, image dataset augmentation method, and the artificial intelligence backbone model applied for transfer learning. Full article

(This article belongs to the Special Issue Disruptive Technologies and Wireless Sensor Network Communication Algorithms)

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14 pages, 4323 KiB

Open AccessArticle

Estimation of Number of Pigs Taking in Feed Using Posture Filtration

by Taeho Kim, Youjin Kim, Sehan Kim and Jaepil Ko

Sensors 2023, 23(1), 238; https://doi.org/10.3390/s23010238 - 26 Dec 2022

Cited by 6 | Viewed by 2251

Abstract

Pork production is hugely impacted by the health and breeding of pigs. Analyzing the eating pattern of pigs helps in optimizing the supply chain management with a healthy breeding environment. Monitoring the feed intake of pigs in a barn provides information about their eating habits, behavioral patterns, and surrounding environment, which can be used for further analysis to monitor growth in pigs and eventually contribute to the quality and quantity of meat production. In this paper, we present a novel method to estimate the number of pigs taking in feed by considering the pig’s posture. In order to solve problems arising from using the pig’s posture, we propose an algorithm to match the pig’s head and the corresponding pig’s body using the major-and-minor axis of the pig detection box. In our experiment, we present the detection performance of the YOLOv5 model according to the anchor box, and then we demonstrate that the proposed method outperforms previous methods. We therefore measure the number of pigs taking in feed over a period of 24 h and the number of times pigs consume feed in a day over a period of 30 days, and observe the pig’s feed intake pattern. Full article

(This article belongs to the Special Issue Disruptive Technologies and Wireless Sensor Network Communication Algorithms)

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12 pages, 1457 KiB

Open AccessArticle

Predicting Site Energy Usage Intensity Using Machine Learning Models

by Soualihou Ngnamsie Njimbouom, Kwonwoo Lee, Hyun Lee and Jeongdong Kim

Sensors 2023, 23(1), 82; https://doi.org/10.3390/s23010082 - 22 Dec 2022

Cited by 1 | Viewed by 2655

Abstract

Climate change is a shift in nature yet a devastating phenomenon, mainly caused by human activities, sometimes with the intent to generate usable energy required in humankind’s daily life. Addressing this alarming issue requires an urge for energy consumption evaluation. Predicting energy consumption is essential for determining what factors affect a site’s energy usage and in turn, making actionable suggestions to reduce wasteful energy consumption. Recently, a rising number of researchers have applied machine learning in various fields, such as wind turbine performance prediction, energy consumption prediction, thermal behavior analysis, and more. In this research study, using data publicly made available by the Women in Data Science (WiDS) Datathon 2022 (contains data on building characteristics and information collected by sensors), after appropriate data preparation, we experimented four main machine learning methods (random forest (RF), gradient boost decision tree (GBDT), support vector regressor (SVR), and decision tree for regression (DT)). The most performant model was selected using evaluation metrics: root mean square error (RMSE) and mean absolute error (MAE). The reported results proved the robustness of the proposed concept in capturing the insight and hidden patterns in the dataset, and effectively predicting the energy usage of buildings. Full article

(This article belongs to the Special Issue Disruptive Technologies and Wireless Sensor Network Communication Algorithms)

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14 pages, 3519 KiB

Open AccessArticle

Dynamic Reconstruction and Mesh Compression of 4D Volumetric Model Using Correspondence-Based Deformation for Streaming Service

by Byung-Seo Park, Sol Lee, Jung-Tak Park, Jin-Kyum Kim, Woosuk Kim and Young-Ho Seo

Sensors 2022, 22(22), 8815; https://doi.org/10.3390/s22228815 - 15 Nov 2022

Cited by 1 | Viewed by 2140

Abstract

A sequence of 3D models generated using volumetric capture has the advantage of retaining the characteristics of dynamic objects and scenes. However, in volumetric data, since 3D mesh and texture are synthesized for every frame, the mesh of every frame has a different shape, and the brightness and color quality of the texture is various. This paper proposes an algorithm to consistently create a mesh of 4D volumetric data using dynamic reconstruction. The proposed algorithm comprises remeshing, correspondence searching, and target frame reconstruction by key frame deformation. We make non-rigid deformation possible by applying the surface deformation method of the key frame. Finally, we propose a method of compressing the target frame using the target frame reconstructed using the key frame with error rates of up to 98.88% and at least 20.39% compared to previous studies. The experimental results show the proposed method’s effectiveness by measuring the geometric error between the deformed key frame and the target frame. Further, by calculating the residual between two frames, the ratio of data transmitted is measured to show a compression performance of 18.48%. Full article

(This article belongs to the Special Issue Disruptive Technologies and Wireless Sensor Network Communication Algorithms)

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20 pages, 4348 KiB

Open AccessArticle

Robust Estimation and Optimized Transmission of 3D Feature Points for Computer Vision on Mobile Communication Network

by Jin-Kyum Kim, Byung-Seo Park, Woosuk Kim, Jung-Tak Park, Sol Lee and Young-Ho Seo

Sensors 2022, 22(21), 8563; https://doi.org/10.3390/s22218563 - 7 Nov 2022

Cited by 1 | Viewed by 2419

Abstract

Due to the amount of transmitted data and the security of personal or private information in wireless communication, there are cases where the information for a multimedia service should be directly transferred from the user’s device to the cloud server without the captured original images. This paper proposes a new method to generate 3D (dimensional) keypoints based on a user’s mobile device with a commercial RGB camera in a distributed computing environment such as a cloud server. The images are captured with a moving camera and 2D keypoints are extracted from them. After executing feature extraction between continuous frames, disparities are calculated between frames using the relationships between matched keypoints. The physical distance of the baseline is estimated by using the motion information of the camera, and the actual distance is calculated by using the calculated disparity and the estimated baseline. Finally, 3D keypoints are generated by adding the extracted 2D keypoints to the calculated distance. A keypoint-based scene change method is proposed as well. Due to the existing similarity between continuous frames captured from a camera, not all 3D keypoints are transferred and stored, only the new ones. Compared with the ground truth of the TUM dataset, the average error of the estimated 3D keypoints was measured as 5.98 mm, which shows that the proposed method has relatively good performance considering that it uses a commercial RGB camera on a mobile device. Furthermore, the transferred 3D keypoints were decreased to about 73.6%. Full article

(This article belongs to the Special Issue Disruptive Technologies and Wireless Sensor Network Communication Algorithms)

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17 pages, 4284 KiB

Open AccessArticle

Wavelet Mutation with Aquila Optimization-Based Routing Protocol for Energy-Aware Wireless Communication

by Someah Alangari, Marwa Obayya, Abdulbaset Gaddah, Ayman Yafoz, Raed Alsini, Omar Alghushairy, Ahmed Ashour and Abdelwahed Motwakel

Sensors 2022, 22(21), 8508; https://doi.org/10.3390/s22218508 - 4 Nov 2022

Cited by 7 | Viewed by 1797

Abstract

Wireless sensor networks (WSNs) have been developed recently to support several applications, including environmental monitoring, traffic control, smart battlefield, home automation, etc. WSNs include numerous sensors that can be dispersed around a specific node to achieve the computing process. In WSNs, routing becomes a very significant task that should be managed prudently. The main purpose of a routing algorithm is to send data between sensor nodes (SNs) and base stations (BS) to accomplish communication. A good routing protocol should be adaptive and scalable to the variations in network topologies. Therefore, a scalable protocol has to execute well when the workload increases or the network grows larger. Many complexities in routing involve security, energy consumption, scalability, connectivity, node deployment, and coverage. This article introduces a wavelet mutation with Aquila optimization-based routing (WMAO-EAR) protocol for wireless communication. The presented WMAO-EAR technique aims to accomplish an energy-aware routing process in WSNs. To do this, the WMAO-EAR technique initially derives the WMAO algorithm for the integration of wavelet mutation with the Aquila optimization (AO) algorithm. A fitness function is derived using distinct constraints, such as delay, energy, distance, and security. By setting a mutation probability P, every individual next to the exploitation and exploration phase process has the probability of mutation using the wavelet mutation process. For demonstrating the enhanced performance of the WMAO-EAR technique, a comprehensive simulation analysis is made. The experimental outcomes establish the betterment of the WMAO-EAR method over other recent approaches. Full article

(This article belongs to the Special Issue Disruptive Technologies and Wireless Sensor Network Communication Algorithms)

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10 pages, 253 KiB

Open AccessArticle

Solutions of Detour Distance Graph Equations

by S. Celine Prabha, M. Palanivel, S. Amutha, N. Anbazhagan, Woong Cho, Hyoung-Kyu Song, Gyanendra Prasad Joshi and Hyeonjoon Moon

Sensors 2022, 22(21), 8440; https://doi.org/10.3390/s22218440 - 2 Nov 2022

Viewed by 1774

Abstract

Graph theory is a useful mathematical structure used to model pairwise relations between sensor nodes in wireless sensor networks. Graph equations are nothing but equations in which the unknown factors are graphs. Many problems and results in graph theory can be formulated in terms of graph equations. In this paper, we solved some graph equations of detour two-distance graphs, detour three-distance graphs, detour antipodal graphs involving with the line graphs. Full article

(This article belongs to the Special Issue Disruptive Technologies and Wireless Sensor Network Communication Algorithms)

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