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Wireless Sensors and Wireless Sensor Networks for Engineering Applications
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Wireless Sensors and Wireless Sensor Networks for Engineering Applications

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

Deadline for manuscript submissions: 9 September 2024 | Viewed by 8571

Special Issue Editors

Prof. Dr. Sangman Moh

E-Mail Website
Guest Editor
Department of Computer Engineering, Chosun University, Gwangju, Korea
Interests: Ad hoc and sensor networks; cognitive radio networks; unmanned aerial vehicle networks; mobile edge computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jian Shen

E-Mail Website
Guest Editor
College of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China
Interests: wireless sensor networks; security and privacy in WSNs; data security; privacy protection
Prof. Dr. Wooyeol Choi

E-Mail Website
Guest Editor
Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea
Interests: PHY/MAC cross-layer protocols; machine learning-based resource allocation; SDR-based performance verification for wireless networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wireless sensors are broadly used in numerous engineering applications, and wireless sensor networks (WSNs) provide wireless sensors with seamless connectivity to end users and the Internet. Recently, wireless sensors and WSNs have been exploited more actively in the various areas of engineering domains because they are necessarily required for our highly connected society. For efficient implementation and qualified services, a number of research and development challenges have been studied but many of them still remain open issues to be resolved. In addition, remote sensing, vehicle (such as ground, aerial, or underwater vehicles)-aided sensing and communication, and machine learning techniques are emerging in the research and development of wireless sensors and WSNs.

This Special Issue aims to cover various state-of-the-art works on the enabling technologies and engineering applications of wireless sensors and WSNs. Not only original research articles but also innovative reviews related to hot issues are welcome. This Special Issue invites submissions on all topics of theories and practices for wireless sensors and WSNs, including but not limited to:

  • Wireless sensors
  • Wireless sensor networks
  • Internet of things
  • Remote sensing and sensors
  • Vehicle-aided sensing and communication
  • Machine learning techniques
  • Signal processing
  • Network security
  • Data security and privacy
  • Novel approaches and applications

Prof. Dr. Sangman Moh
Prof. Dr. Jian Shen
Prof. Dr. Wooyeol Choi
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

  • wireless sensors
  • wireless sensor networks
  • Internet of Things
  • remote sensing and sensors
  • vehicle-aided sensing and communication
  • machine learning techniques
  • signal processing
  • network security
  • data security and privacy
  • novel approaches and applications

Published Papers (6 papers)

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Research

20 pages, 6782 KiB  
Article
Reconstruction of Radio Environment Map Based on Multi-Source Domain Adaptive of Graph Neural Network for Regression
by Xiaomin Wen, Shengliang Fang and Youchen Fan
Sensors 2024, 24(8), 2523; https://doi.org/10.3390/s24082523 - 15 Apr 2024
Viewed by 344
Abstract
The graph neural network (GNN) has shown outstanding performance in processing unstructured data. However, the downstream task performance of GNN strongly depends on the accuracy of data graph structural features and, as a type of deep learning (DL) model, the size of the [...] Read more.
The graph neural network (GNN) has shown outstanding performance in processing unstructured data. However, the downstream task performance of GNN strongly depends on the accuracy of data graph structural features and, as a type of deep learning (DL) model, the size of the training dataset is equally crucial to its performance. This paper is based on graph neural networks to predict and complete the target radio environment map (REM) through multiple complete REMs and sparse spectrum monitoring data in the target domain. Due to the complexity of radio wave propagation in space, it is difficult to accurately and explicitly construct the spatial graph structure of the spectral data. In response to the two above issues, we propose a multi-source domain adaptive of GNN for regression (GNN-MDAR) model, which includes two key modules: (1) graph structure alignment modules are used to capture and learn graph structure information shared by cross-domain radio propagation and extract reliable graph structure information for downstream reference signal receiving power (RSRP) prediction task; and (2) a spatial distribution matching module is used to reduce the feature distribution mismatch across spatial grids and improve the model’s ability to remain domain invariant. Based on the measured REMs dataset, the comparative results of simulation experiments show that the GNN-MDAR outperforms the other four benchmark methods in accuracy when there is less RSRP label data in the target domain. Full article
(This article belongs to the Special Issue Wireless Sensors and Wireless Sensor Networks for Engineering Applications)
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16 pages, 501 KiB  
Article
Lyapunov Drift-Plus-Penalty-Based Cooperative Uplink Scheduling in Dense Wi-Fi Networks
by Yonggang Kim and Yohan Kim
Sensors 2024, 24(8), 2399; https://doi.org/10.3390/s24082399 - 09 Apr 2024
Viewed by 475
Abstract
In high-density network environments with multiple access points (APs) and stations, individual uplink scheduling by each AP can severely interfere with the uplink transmissions of neighboring APs and their associated stations. In congested areas where concurrent uplink transmissions may lead to significant interference, [...] Read more.
In high-density network environments with multiple access points (APs) and stations, individual uplink scheduling by each AP can severely interfere with the uplink transmissions of neighboring APs and their associated stations. In congested areas where concurrent uplink transmissions may lead to significant interference, it would be beneficial to deploy a cooperative scheduler or a central coordinating entity responsible for orchestrating cooperative uplink scheduling by assigning several neighboring APs to support the uplink transmission of a single station within a proximate service area to alleviate the excessive interference. Cooperative uplink scheduling facilitated by cooperative information sharing and management is poised to improve the likelihood of successful uplink transmissions in areas with a high concentration of APs and stations. Nonetheless, it is crucial to account for the queue stability of the stations and the potential delays arising from information exchange and the decision-making process in uplink scheduling to maintain the overall effectiveness of the cooperative approach. In this paper, we propose a Lyapunov drift-plus-penalty framework-based cooperative uplink scheduling method for densely populated Wi-Fi networks. The cooperative scheduler aggregates information, such as signal-to-interference-plus-noise ratio (SINR) and queue status. During the aggregation procedure, propagation delays are also estimated and utilized as a value of expected cooperation delays in scheduling decisions. Upon aggregating the information, the cooperative scheduler calculates the Lyapunov drift-plus-penalty value, incorporating a predefined model parameter to adjust the system accordingly. Among the possible scheduling candidates, the proposed method proceeds to make uplink decisions that aim to reduce the upper bound of the Lyapunov drift-plus-penalty value, thereby improving the network performance and stability without a severe increase in cooperation delay in highly congested areas. Through comprehensive performance evaluations, the proposed method effectively enhances network performance with an appropriate model parameter. The performance improvement is particularly notable in highly congested areas and is achieved without a severe increase in cooperation delays. Full article
(This article belongs to the Special Issue Wireless Sensors and Wireless Sensor Networks for Engineering Applications)
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19 pages, 7609 KiB  
Article
Multipurpose Modular Wireless Sensor for Remote Monitoring and IoT Applications
by Víctor Sámano-Ortega, Omar Arzate-Rivas, Juan Martínez-Nolasco, Juan Aguilera-Álvarez, Coral Martínez-Nolasco and Mauro Santoyo-Mora
Sensors 2024, 24(4), 1277; https://doi.org/10.3390/s24041277 - 17 Feb 2024
Viewed by 1141
Abstract
Today, maintaining an Internet connection is indispensable; as an example, we can refer to IoT applications that can be found in fields such as environmental monitoring, smart manufacturing, healthcare, smart buildings, smart homes, transportation, energy, and others. The critical elements in IoT applications [...] Read more.
Today, maintaining an Internet connection is indispensable; as an example, we can refer to IoT applications that can be found in fields such as environmental monitoring, smart manufacturing, healthcare, smart buildings, smart homes, transportation, energy, and others. The critical elements in IoT applications are both the Wireless Sensor Nodes (WSn) and the Wireless Sensor Networks. It is essential to state that designing an application demands a particular design of a WSn, which represents an important time consumption during the process. In line with this observation, our work describes the development of a modular WSn (MWSn) built with digital processing, wireless communication, and power supply subsystems. Then, we reduce the WSn-implementing process into the design of its modular sensing subsystem. This would allow the development and launching processes of IoT applications across different fields to become faster and easier. Our proposal presents a versatile communication between the sensing modules and the MWSn using one- or two-wired communication protocols, such as I2C. To validate the efficiency and versatility of our proposal, we present two IoT-based remote monitoring applications. Full article
(This article belongs to the Special Issue Wireless Sensors and Wireless Sensor Networks for Engineering Applications)
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13 pages, 2116 KiB  
Article
Resource-Efficient Parallelized Random Access for Reliable Connection Establishment in Cellular IoT Networks
by Taehoon Kim, Seongho Chae, Jin-Taek Lim and Inkyu Bang
Sensors 2023, 23(8), 3819; https://doi.org/10.3390/s23083819 - 08 Apr 2023
Cited by 2 | Viewed by 1261
Abstract
The role of various internet-of-things (IoT) devices responsible for data collection and reporting becomes more important in the era of Industry 4.0. Due to the various advantages (e.g., wide coverage, robust security, etc.), the cellular networks have been continuously evolved to accommodate IoT [...] Read more.
The role of various internet-of-things (IoT) devices responsible for data collection and reporting becomes more important in the era of Industry 4.0. Due to the various advantages (e.g., wide coverage, robust security, etc.), the cellular networks have been continuously evolved to accommodate IoT scenario. In IoT scenario, connection establishment is essential and primary for enabling IoT devices to communicate with centralized unit (e.g., base station (BS)). This connection establishment procedure in cellular networks, random access procedure, is generally operated in a contention-based manner. So, it is vulnerable to simultaneous connection requests from multiple IoT devices to the BS, which becomes worse as the contention participants increase. In this article, we newly propose a resource-efficient parallelized random access (RePRA) procedure for resource-efficiently ensuring reliable connection establishment in cellular-based massive IoT networks. Key features of our proposed technique are twofold: (1) Each IoT device simultaneously performs multiple RA procedures in parallel to improve connection establishment success probability, and (2) the BS handles excessive use of radio resources based on newly proposed two types of redundancy elimination mechanisms. Through extensive simulations, we evaluate the performance of our proposed technique in terms of connection establishment success probability and resource efficiency under various combinations of control parameters. Consequently, we verify the feasibility of our proposed technique for reliably and radio-efficiently supporting a large number of IoT devices. Full article
(This article belongs to the Special Issue Wireless Sensors and Wireless Sensor Networks for Engineering Applications)
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15 pages, 1021 KiB  
Article
Deep Reinforcement Learning-Based Coordinated Beamforming for mmWave Massive MIMO Vehicular Networks
by Pulok Tarafder and Wooyeol Choi
Sensors 2023, 23(5), 2772; https://doi.org/10.3390/s23052772 - 03 Mar 2023
Cited by 7 | Viewed by 2870
Abstract
As a critical enabler for beyond fifth-generation (B5G) technology, millimeter wave (mmWave) beamforming for mmWave has been studied for many years. Multi-input multi-output (MIMO) system, which is the baseline for beamforming operation, rely heavily on multiple antennas to stream data in mmWave wireless [...] Read more.
As a critical enabler for beyond fifth-generation (B5G) technology, millimeter wave (mmWave) beamforming for mmWave has been studied for many years. Multi-input multi-output (MIMO) system, which is the baseline for beamforming operation, rely heavily on multiple antennas to stream data in mmWave wireless communication systems. High-speed mmWave applications face challenges such as blockage and latency overhead. In addition, the efficiency of the mobile systems is severely impacted by the high training overhead required to discover the best beamforming vectors in large antenna array mmWave systems. In order to mitigate the stated challenges, in this paper, we propose a novel deep reinforcement learning (DRL) based coordinated beamforming scheme where multiple base stations serve one mobile station (MS) jointly. The constructed solution then uses a proposed DRL model and predicts the suboptimal beamforming vectors at the base stations (BSs) out of possible beamforming codebook candidates. This solution enables a complete system that facilitates highly mobile mmWave applications with dependable coverage, minimal training overhead, and low latency. Numerical results demonstrate that our proposed algorithm remarkably increases the achievable sum rate capacity for the highly mobile mmWave massive MIMO scenario while ensuring low training and latency overhead. Full article
(This article belongs to the Special Issue Wireless Sensors and Wireless Sensor Networks for Engineering Applications)
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30 pages, 6469 KiB  
Article
Energy-Efficient Object Detection and Tracking Framework for Wireless Sensor Network
by Jayashree Dev and Jibitesh Mishra
Sensors 2023, 23(2), 746; https://doi.org/10.3390/s23020746 - 09 Jan 2023
Cited by 3 | Viewed by 1708
Abstract
Object detection and tracking is one of the key applications of wireless sensor networks (WSNs). The key issues associated with this application include network lifetime, object detection and localization accuracy. To ensure the high quality of the service, there should be a trade-off [...] Read more.
Object detection and tracking is one of the key applications of wireless sensor networks (WSNs). The key issues associated with this application include network lifetime, object detection and localization accuracy. To ensure the high quality of the service, there should be a trade-off between energy efficiency and detection accuracy, which is challenging in a resource-constrained WSN. Most researchers have enhanced the application lifetime while achieving target detection accuracy at the cost of high node density. They neither considered the system cost nor the object localization accuracy. Some researchers focused on object detection accuracy while achieving energy efficiency by limiting the detection to a predefined target trajectory. In particular, some researchers only focused on node clustering and node scheduling for energy efficiency. In this study, we proposed a mobile object detection and tracking framework named the Energy Efficient Object Detection and Tracking Framework (EEODTF) for heterogeneous WSNs, which minimizes energy consumption during tracking while not affecting the object detection and localization accuracy. It focuses on achieving energy efficiency via node optimization, mobile node trajectory optimization, node clustering, data reporting optimization and detection optimization. We compared the performance of the EEODTF with the Energy Efficient Tracking and Localization of Object (EETLO) model and the Particle-Swarm-Optimization-based Energy Efficient Target Tracking Model (PSOEETTM). It was found that the EEODTF is more energy efficient than the EETLO and PSOEETTM models. Full article
(This article belongs to the Special Issue Wireless Sensors and Wireless Sensor Networks for Engineering Applications)
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