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Electronics
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Applications of Sensor Networks and Wireless Communications
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Applications of Sensor Networks and Wireless Communications

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: 15 June 2025 | Viewed by 2627

Special Issue Editors

Dr. Rajan Kadel

E-Mail Website
Guest Editor
School of IT & Engineering, Melbourne Institute of Technology, Melbourne, VIC 3000, Australia
Interests: IoT and sustainability; climate change; generative AI applications
Special Issues, Collections and Topics in MDPI journals
Dr. Samar Shailendra

E-Mail Website
Guest Editor
School of IT & Engineering, Melbourne Institute of Technology, Melbourne, VIC 3000, Australia
Interests: wireless networks; enterprise and cloud networks; AI and machine learning; SDN; NFV; ICN; 5G/6G
Dr. Assefa Kassa Teshome

E-Mail Website
Guest Editor
College of Engineering and Science, Victoria University, Melbourne, VIC 3000, Australia
Interests: electronic and telecommunications engineering; emerging wireless body-area and implant communication techniques; digital signal processing and artificial intelligence

Special Issue Information

Dear Colleagues,

Advancements in sensor networks and wireless communication have transformed numerous industries, opening up new avenues for collecting, analyzing, and communicating data in a wide range of settings. This Special Issue is dedicated to exploring the extensive array of applications for sensor networks and wireless communication systems, highlighting their profound influence across various fields. We would like to invite researchers and practitioners to contribute their research outcomes in the area of the applications of sensor networks and wireless communication technologies. The fusion of sensor networks with the latest wireless communication advancements has offered unparalleled opportunities for enhanced connectivity, data acquisition, analysis, and decision making. The application ranges from precision agriculture to smart cities, from healthcare monitoring to environmental conservation,. The potential of these technologies to transform industries and improve quality of life is vast and ever-expanding. This special issue aims to explore into the multifaceted landscape of sensor networks and wireless communication applications, shedding light on innovative solutions, challenges, and future directions.

We welcome submissions that explore the intersection of sensor networks and wireless communication in areas such as smart agriculture, healthcare monitoring, environmental monitoring, industrial IoT, transportation systems, energy management, disaster plan and recovery, urban planning including smart city, and wildlife conservation. This compilation of research articles, review papers, and case studies aims to stimulate cross-disciplinary conversations and deepen our comprehension of how these technologies can be utilized to tackle real-world problems and steer us towards a more interconnected and sustainable future.

Topics of interest include, but are not limited to, the following:

  • Smart cities;
  • Healthcare monitoring;
  • Environmental sensing;
  • Agricultural monitoring;
  • Infrastructure monitoring;
  • Smart industry;
  • Smart transportation;
  • Security and surveillance;
  • Wildlife conservation.

Dr. Rajan Kadel
Dr. Samar Shailendra
Dr. Assefa Kassa Teshome
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. Electronics 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 2400 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

  • smart cities
  • smart technologies
  • smart industry
  • smart transportation
  • smart monitoring

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

23 pages, 9237 KiB  
Article
Design and Optimization of an Internet of Things-Based Cloud Platform for Autonomous Agricultural Machinery Using Narrowband Internet of Things and 5G Dual-Channel Communication
by Baidong Zhao, Dingkun Zheng, Chenghan Yang, Shuang Wang, Madina Mansurova, Sholpan Jomartova, Nadezhda Kunicina, Anatolijs Zabasta, Vladimir Beliaev, Jelena Caiko and Roberts Grants
Electronics 2025, 14(8), 1672; https://doi.org/10.3390/electronics14081672 - 20 Apr 2025
Viewed by 178
Abstract
This paper proposes a design and optimization scheme for an Internet of Things (IoT)-based cloud platform aimed at enhancing the communication efficiency and operational performance of autonomous agricultural machinery. The platform integrates the dual communication capabilities of Narrowband Internet of Things (NB-IoT) and [...] Read more.
This paper proposes a design and optimization scheme for an Internet of Things (IoT)-based cloud platform aimed at enhancing the communication efficiency and operational performance of autonomous agricultural machinery. The platform integrates the dual communication capabilities of Narrowband Internet of Things (NB-IoT) and 5G, where NB-IoT is utilized for low-power, reliable data transmission from environmental sensors, such as soil information and weather monitoring, while 5G supports high-bandwidth, low-latency tasks like task scheduling and path tracking to effectively address the diverse communication requirements of modern complex agricultural scenarios. The cloud platform improves operational efficiency and resource utilization through real-time task scheduling, dynamic optimization, and seamless coordination between devices. To accommodate the diverse operational demands of agricultural environments, the system incorporates a real-time data feedback mechanism leveraging sensor data for path tracking and adjustment, enhancing adaptability and stability. Furthermore, a multi-machine collaborative scheduling strategy combining Dijkstra’s algorithm and an improved Harris hawk optimization (IHHO) algorithm, along with a multi-objective optimized path tracking method, is introduced to further improve scheduling efficiency and resource utilization while improving path tracking accuracy and smoothness and reducing external interferences, including environmental fluctuations and sensor inaccuracies. Experimental results demonstrate that the IoT-based cloud platform excels in data transmission reliability, path tracking accuracy, and resource optimization, validating its feasibility in smart agriculture and providing an efficient and scalable solution for large-scale agricultural operations. Full article
(This article belongs to the Special Issue Applications of Sensor Networks and Wireless Communications)
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25 pages, 11298 KiB  
Article
A Smart Space Focus Enhancement System Based on Grey Wolf Algorithm Positioning and Generative Adversarial Networks for Database Augmentation
by Jia-You Cai, Yu-Yong Luo and Chia-Hsin Cheng
Electronics 2025, 14(5), 865; https://doi.org/10.3390/electronics14050865 - 21 Feb 2025
Cited by 1 | Viewed by 454
Abstract
In the age of technological advancement, brainwave monitoring and attention tracking are critical for individual productivity and organizational efficiency. However, distractions pose significant challenges, making an effective brainwave monitoring and attention system essential. Generative Adversarial Networks (GANs) enhance medical datasets by synthesizing diverse [...] Read more.
In the age of technological advancement, brainwave monitoring and attention tracking are critical for individual productivity and organizational efficiency. However, distractions pose significant challenges, making an effective brainwave monitoring and attention system essential. Generative Adversarial Networks (GANs) enhance medical datasets by synthesizing diverse samples. This paper explores their application in improving datasets for indoor positioning and brainwave monitoring-based attention tracking. The goal is to develop an intelligent lighting system that adjusts settings based on users’ brainwave states and positions. GANs enhance brainwave monitoring and positioning datasets, with Principal Component Analysis (PCA) applied for dimensionality reduction. machine learning and deep learning models train on these augmented datasets, enabling dynamic lighting adjustments to optimize user experience. GANs undergo parameter fine-tuning to improve dataset quality. Various classification models, including neural networks (NN), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM), are used for brainwave monitoring, attention, and positioning. Fuzzy logic enhances system stability. The trained models are integrated with hardware components, such as the Raspberry Pi 4, to implement an “Indoor Positioning Deep Learning Brainwave Monitoring and Attention Monitoring System Based on the Grey Wolf Optimizer Algorithm”. Experimental results demonstrate a positioning accuracy of 15 cm and significant improvements in brainwave monitoring and attention tracking. Full article
(This article belongs to the Special Issue Applications of Sensor Networks and Wireless Communications)
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24 pages, 2735 KiB  
Article
Research on High-Efficiency Routing Protocols for HWSNs Based on Deep Reinforcement Learning
by Yu Song, Zhigui Liu, Kunran Li, Xiaoli He and Weizhuo Zhu
Electronics 2024, 13(23), 4746; https://doi.org/10.3390/electronics13234746 - 30 Nov 2024
Viewed by 716
Abstract
In heterogeneous wireless sensor networks (HWSNs), optimizing energy efficiency presents significant challenges due to variations in node energy levels and the complexity of the network environment. This paper introduces an energy efficiency optimization algorithm for HWSNs based on the Deep Q-Network (HDQN). The [...] Read more.
In heterogeneous wireless sensor networks (HWSNs), optimizing energy efficiency presents significant challenges due to variations in node energy levels and the complexity of the network environment. This paper introduces an energy efficiency optimization algorithm for HWSNs based on the Deep Q-Network (HDQN). The algorithm aims to address these challenges by selecting the optimal information transmission path. The HDQN leverages energy differences between nodes and real-time environmental data to enhance network efficiency. Its reward function takes into account node distance, remaining energy, and relay node count to balance node participation and minimize overall energy consumption. The Deep Q-Network (DQN) uses the mean squared error for precise reward estimation, and an improved packet header structure supports effective routing decisions. Simulation results show that the HDQN significantly outperforms existing algorithms—EEHCHR, 2L-HMGEAR, NCOGA, DEEC, and SEP—in terms of energy efficiency, network lifetime, and robustness, demonstrating its potential to advance the performance of HWSNs. The research results of the paper provide a theoretical basis for future energy efficiency research in wireless communication and contribute to the study of the new generation of wireless networks. Full article
(This article belongs to the Special Issue Applications of Sensor Networks and Wireless Communications)
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19 pages, 2049 KiB  
Article
Research on SDP-BF Method with Low False Positive Face to Passive Detection System
by Chenzhuo Jiang, Junjie Li and Yuxiao Yang
Electronics 2024, 13(16), 3240; https://doi.org/10.3390/electronics13163240 - 15 Aug 2024
Viewed by 700
Abstract
With the rapid development of 5G, UAV, and military communications, the data volume obtained by the non-cooperative perception system has increased exponentially, and the distributed system has become the development trend of the non-cooperative perception system. The data distribution service (DDS) produces a [...] Read more.
With the rapid development of 5G, UAV, and military communications, the data volume obtained by the non-cooperative perception system has increased exponentially, and the distributed system has become the development trend of the non-cooperative perception system. The data distribution service (DDS) produces a significant effect on the performance of distributed non-cooperative perception systems. However, the traditional DDS discovery protocol has problems such as false positive misjudgment and high flow overhead, so it can hardly adapt to a large multi-node distributed system. Therefore, the design of a DDS discovery protocol for large distributed system is technically challenging. In this paper, we proposed SDP-DCBF-SFF, a discovery protocol based on the Dynamic Counter Bloom Filter (DCBF) and Second Feedback Filter (SFF). The proposed discovery protocol coarsely filters the interested endpoints through DCBF and then accurately screens the uninterested endpoints through SFF to eliminate the connection requests of false positive endpoints and avoid extra flow overhead. The experimental results indicate that the proposed discovery protocol could effectively reduce the network overhead, and eliminate the false positive probability of endpoints in small, medium, large, and super large systems. In addition, it adopts the self-adaptive extension mechanism of BF to reduce the reconfiguration delay of BF and achieve the smallest system transmission delay. Therefore, the proposed discovery protocol has optimal comprehensive performance and system adaptability. Full article
(This article belongs to the Special Issue Applications of Sensor Networks and Wireless Communications)
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