Signal Processing and Communication for Wireless Sensor Network

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 20 July 2024 | Viewed by 7322

Special Issue Editors


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Guest Editor
Division of Mechanical and Electronics Engineering, Hansung University, Seoul 02876, Republic of Korea
Interests: signal processing; wireless communications; machine learning

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Guest Editor
Department of Artificial Intelligence Convergence, Daejeon University, Daejeon 34520, Korea
Interests: internet protocol; IoT; network intelligence and edge computing

Special Issue Information

Dear Colleagues,

With the development of sensors, computing, and wireless communication, there is an increasing demand for the use of wireless sensor networks. In advanced wireless sensor networks, as the number of devices increases and the utilization area expands, the required performance increases, requiring more effective signal processing and communication technologies. Therefore, the design of wireless sensor networks requires consideration in several areas, such as distributed signal processing, wireless communication, machine learning approaches, and cross-layer design. This Special Issue focuses on signal processing and wireless communication used in state-of-the-art wireless sensor networks, and provides readers with a signal processing and communication perspective on network design.

Dr. Gyuyeol Kong
Dr. Younggeun Hong
Guest Editors

Manuscript Submission Information

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Keywords

  • distributed signal processing
  • sensing, detection, and estimation in sensor networks
  • communication and networking technologies
  • cross-layer optimization for sensor networks
  • machine learning techniques for sensor networks
  • energy-efficient implementation for sensor networks

Published Papers (5 papers)

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Research

21 pages, 3451 KiB  
Article
Improving the Reliability of Long-Range Communication against Interference for Non-Line-of-Sight Conditions in Industrial Internet of Things Applications
by Boubaker Abdallah, Sabrine Khriji, Rym Chéour, Charbel Lahoud, Klaus Moessner and Olfa Kanoun
Appl. Sci. 2024, 14(2), 868; https://doi.org/10.3390/app14020868 - 19 Jan 2024
Viewed by 849
Abstract
LoRa technology, renowned for its low-power, long-range capabilities in IoT applications, faces challenges in real-world scenarios, including fading channels, interference, and environmental obstacles. This paper aims to study the reliability of LoRa in Non-Line-of-Sight (NLoS) conditions and in noisy and mobile environments for [...] Read more.
LoRa technology, renowned for its low-power, long-range capabilities in IoT applications, faces challenges in real-world scenarios, including fading channels, interference, and environmental obstacles. This paper aims to study the reliability of LoRa in Non-Line-of-Sight (NLoS) conditions and in noisy and mobile environments for Industrial IoT (IIoT) applications. Experimental measurements consider factors like vegetation and infrastructure, introducing mobility to replicate NLoS conditions. Utilizing an open-source LoRa Physical Layer (PHY) Software-Defined Radio (SDR) prototype developed with GNU Radio, we assess communication reliability through metrics such as Block Error Rate (BLER), Signal-to-Noise-Interference-plus-Noise Ratio (SINR), and data rate. The study reveals the estimated overall reliability of the LoRa signal at 90.23%, emphasizing specific configuration details. This work contributes to the broader field of LoRa communication, encompassing hardware, software, protocols, and management, enhancing our understanding of LoRa’s dependability in challenging IIoT environments. Full article
(This article belongs to the Special Issue Signal Processing and Communication for Wireless Sensor Network)
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18 pages, 99166 KiB  
Article
A Real-Time Shipping Container Accident Inference System Monitoring the Alignment State of Shipping Containers in Edge Environments
by Se-Yeong Oh, Junho Jeong, Sang-Woo Kim, Young-Uk Seo and Joosang Youn
Appl. Sci. 2023, 13(20), 11563; https://doi.org/10.3390/app132011563 - 22 Oct 2023
Viewed by 1071
Abstract
Along with the recent development of artificial intelligence technology, convergence services that apply technology are undergoing active development in various industrial fields. In particular, artificial intelligence-based object recognition technologies are being widely applied to the development of intelligent analysis services based on image [...] Read more.
Along with the recent development of artificial intelligence technology, convergence services that apply technology are undergoing active development in various industrial fields. In particular, artificial intelligence-based object recognition technologies are being widely applied to the development of intelligent analysis services based on image data and streaming video data. As such, in the port yard, these object recognition technologies are being used to develop port safety services in smart ports. Accidents are a frequent occurrence in port yards due to misaligned loading of ship containers. In order to prevent such accidents, various studies using artificial intelligence technology are underway. In this paper, we propose a real-time shipping container accident inference edge system that can analyze the ship container’s loading status from a safety point of view to prevent accidents in advance. The proposed system includes the collection of video data of the ship container, inferring the safety level of the alignment status of the ship container, and transmitting the inference results for the safety level. In this paper, the proposed inference model is implemented with YOLOv3, YOLOv4 and YOLOv7 networks and can be used in video monitoring to realize the accurate classification and positioning of three different safety levels (safe, caution, and danger) in real time. In the performance evaluation, the detection accuracy of the inference model implemented with the YOLOv4 network was greater than 0.95. Its performance was also significantly better than that of the inference model implemented with the YOLOv3 and YOLOv7 networks. Although it was slightly inferior to the YOLOv4 network in terms of the accuracy, the inference model implemented with the YOLOv3 network had a faster inference speed than the model implemented with the YOLOv4 and YOLOv7 networks. Because of the port safety scenario, in which the inference accuracy is more important than the inference speed, we applied the YOLOv4 algorithm to the inference model of the system. Full article
(This article belongs to the Special Issue Signal Processing and Communication for Wireless Sensor Network)
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19 pages, 6186 KiB  
Article
Inference Latency Prediction Approaches Using Statistical Information for Object Detection in Edge Computing
by Gyuyeol Kong and Yong-Geun Hong
Appl. Sci. 2023, 13(16), 9222; https://doi.org/10.3390/app13169222 - 14 Aug 2023
Viewed by 788
Abstract
To seamlessly deliver artificial intelligence (AI) services using object detection, both inference latency from a system perspective as well as inference accuracy should be considered important. Although edge computing can be applied to efficiently operate these AI services by significantly reducing inference latency, [...] Read more.
To seamlessly deliver artificial intelligence (AI) services using object detection, both inference latency from a system perspective as well as inference accuracy should be considered important. Although edge computing can be applied to efficiently operate these AI services by significantly reducing inference latency, deriving an optimized computational offloading policy for edge computing is a challenging problem. In this paper, we propose inference latency prediction approaches for determining the optimal offloading policy in edge computing. Since there is no correlation between the image size and inference latency during object detection, approaches to predict inference latency are required for finding the optimal offloading policy. The proposed approaches predict the inference latency between devices and object detection algorithms by using their statistical information on the inference latency. By exploiting the predicted inference latency, a client may efficiently determine whether to execute an object detection task locally or remotely. Through various experiments, the performances of predicted inference latency according to the object detection algorithms are compared and analyzed by considering two communication protocols in terms of the root mean square error. The simulation results show that the predicted inference latency matches the actual inference latency well. Full article
(This article belongs to the Special Issue Signal Processing and Communication for Wireless Sensor Network)
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22 pages, 2950 KiB  
Article
An Ensemble Tree-Based Model for Intrusion Detection in Industrial Internet of Things Networks
by Joseph Bamidele Awotunde, Sakinat Oluwabukonla Folorunso, Agbotiname Lucky Imoize, Julius Olusola Odunuga, Cheng-Chi Lee, Chun-Ta Li and Dinh-Thuan Do
Appl. Sci. 2023, 13(4), 2479; https://doi.org/10.3390/app13042479 - 14 Feb 2023
Cited by 18 | Viewed by 2163
Abstract
With less human involvement, the Industrial Internet of Things (IIoT) connects billions of heterogeneous and self-organized smart sensors and devices. Recently, IIoT-based technologies are now widely employed to enhance the user experience across numerous application domains. However, heterogeneity in the node source poses [...] Read more.
With less human involvement, the Industrial Internet of Things (IIoT) connects billions of heterogeneous and self-organized smart sensors and devices. Recently, IIoT-based technologies are now widely employed to enhance the user experience across numerous application domains. However, heterogeneity in the node source poses security concerns affecting the IIoT system, and due to device vulnerabilities, IIoT has encountered several attacks. Therefore, security features, such as encryption, authorization control, and verification, have been applied in IIoT networks to secure network nodes and devices. However, the requisite machine learning models require some time to detect assaults because of the diverse IIoT network traffic properties. Therefore, this study proposes ensemble models enabled with a feature selection classifier for Intrusion Detection in the IIoT network. The Chi-Square Statistical method was used for feature selection, and various ensemble classifiers, such as eXtreme gradient boosting (XGBoost), Bagging, extra trees (ET), random forest (RF), and AdaBoost can be used for the detection of intrusion applied to the Telemetry data of the TON_IoT datasets. The performance of these models is appraised based on accuracy, recall, precision, F1-score, and confusion matrix. The results indicate that the XGBoost ensemble showed superior performance with the highest accuracy over other models across the datasets in detecting and classifying IIoT attacks. Full article
(This article belongs to the Special Issue Signal Processing and Communication for Wireless Sensor Network)
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22 pages, 1239 KiB  
Article
Combined Use of LoRaWAN Medium Access Control Protocols for IoT Applications
by Luca Leonardi, Lucia Lo Bello, Gaetano Patti, Alessio Pirri and Mattia Pirri
Appl. Sci. 2023, 13(4), 2341; https://doi.org/10.3390/app13042341 - 11 Feb 2023
Cited by 3 | Viewed by 1646
Abstract
The low power wide area networks (LPWANs) based on the LoRaWAN standard are suitable for Internet of Things (IoT) applications that involve a large number of low-power devices distributed over large areas. The LoRaWAN standard imposes some limitations on end-device configuration, such as [...] Read more.
The low power wide area networks (LPWANs) based on the LoRaWAN standard are suitable for Internet of Things (IoT) applications that involve a large number of low-power devices distributed over large areas. The LoRaWAN standard imposes some limitations on end-device configuration, such as the medium access strategies to be adopted, which depend on the region in which the network operates. In particular, in Europe, according to the ETSI regulations, a LoRaWAN end-device can use either a pure ALOHA medium access control (MAC) protocol or a polite medium access technique based on Listen Before Talk (LBT) Adaptive Frequency Agility (AFA). The aim of this work is to investigate the combined use of the two MAC protocols in the same LoRaWAN network. In particular, the work presents a simulative assessment of a LoRaWAN network that combines the use of Pure ALOHA and LBT AFA in realistic scenarios, under different workloads, when they work in compliance with the ETSI regulations. The work provides quantitative information that can help the network designer choose which protocol is more suitable for achieving the desired performance. Full article
(This article belongs to the Special Issue Signal Processing and Communication for Wireless Sensor Network)
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