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Electronics
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Recent Advances in Wireless Ad Hoc and Sensor Networks
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Recent Advances in Wireless Ad Hoc and Sensor Networks

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 September 2024 | Viewed by 6681

Special Issue Editors

Dr. Binod Vaidya

E-Mail Website
Guest Editor
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
Interests: IoT technologies; electric vehicle networks; connected and autonomous electric vehicles; smart city applications; cybersecurity; AI; machine learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Byung Rae Cha

E-Mail Website
Guest Editor
School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea
Interests: AI; machine learning; sensor networks; industrial IoT; data lake; IT security

Special Issue Information

Dear Colleagues,

Emerging technologies and standards related to wireless ad hoc and sensor networks (WSN) have significantly evolved over the past few years. WSNs have become an integral part of the Internet of Things (IoT) ecosystem; therefore, a diverse range of IoT applications based on WSNs have been realized in numerous fields such as transportation, energy, industry, health, environment, etc.

These WSN-IoT applications generate huge amounts of data that must be collected and processed in real-time or near real-time. Handling such data may be extremely complex and, therefore, artificial intelligence (AI) and machine learning (ML) techniques are being used for more efficient IoT services that will fulfill end-user expectations.

AI-based WSN-IoT applications are rapidly becoming more useful in every facet of our daily lives including intelligent health monitoring, real-time traffic management, self-driving cars and many other “smart” applications. The advantages of AI/ML are abundant, most notably in terms of increased efficiency, lower human error rates, improved workflows, 24/7 availability, deeper data analysis and more informed decision making.

This Special Issue seeks original unpublished papers empirically addressing recent advancement and results in the design and development of AI/ML for WSN-IoT. Researchers and practitioners working in this area are expected to take this opportunity to discuss the recent advances, current trends, challenges, and state-of-the-art solutions addressing various issues in AI/ML for WSN-IoT. Possible topics to be covered in this Special Issue include but are not limited to the following topics:

  • Artificial intelligence and fuzzy logic applied to wireless ad hoc and sensor networks;
  • Machine learning, reinforcement learning, and deep learning for wireless ad hoc and sensor networks;
  • Advanced intelligent big data analytics in wireless ad hoc and sensor networks;
  • Security and privacy in intelligent wireless ad hoc and sensor networks;
  • Smart AI-WSN applications (smart cities, smart buildings, smart grid, smart health, smart transportation etc.).

Dr. Binod Vaidya
Prof. Dr. Byung Rae Cha
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

  • wireless ad hoc and sensor networks
  • IoT technologies
  • artificial intelligence
  • machine learning
  • smart AI-WSN applications

Published Papers (5 papers)

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Research

22 pages, 686 KiB  
Article
Enhancing Human Activity Recognition with Siamese Networks: A Comparative Study of Contrastive and Triplet Learning Approaches
by Byung-Rae Cha and Binod Vaidya
Electronics 2024, 13(9), 1739; https://doi.org/10.3390/electronics13091739 - 1 May 2024
Viewed by 370
Abstract
This paper delves into the realm of human activity recognition (HAR) by leveraging the capabilities of Siamese neural networks (SNNs), focusing on the comparative effectiveness of contrastive and triplet learning approaches. Against the backdrop of HAR’s growing importance in healthcare, sports, and smart [...] Read more.
This paper delves into the realm of human activity recognition (HAR) by leveraging the capabilities of Siamese neural networks (SNNs), focusing on the comparative effectiveness of contrastive and triplet learning approaches. Against the backdrop of HAR’s growing importance in healthcare, sports, and smart environments, the need for advanced models capable of accurately recognizing and classifying complex human activities has become paramount. Addressing this, we have introduced a Siamese network architecture integrated with convolutional neural networks (CNNs) for spatial feature extraction, bidirectional LSTM (Bi-LSTM) for temporal dependency capture, and attention mechanisms to prioritize salient features. Employing both contrastive and triplet loss functions, we meticulously analyze the impact of these learning approaches on the network’s ability to generate discriminative embeddings for HAR tasks. Through extensive experimentation, the study reveals that Siamese networks, particularly those utilizing triplet loss functions, demonstrate superior performance in activity recognition accuracy and F1 scores compared with baseline deep learning models. The inclusion of a stacking meta-classifier further amplifies classification efficacy, showcasing the robustness and adaptability of our proposed model. Conclusively, our findings underscore the potential of Siamese networks with advanced learning paradigms in enhancing HAR systems, paving the way for future research in model optimization and application expansion. Full article
(This article belongs to the Special Issue Recent Advances in Wireless Ad Hoc and Sensor Networks)
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13 pages, 3595 KiB  
Article
Internet of Things Gateway Edge for Movement Monitoring in a Smart Healthcare System
by Khalid Al-Naime, Adnan Al-Anbuky and Grant Mawston
Electronics 2023, 12(16), 3449; https://doi.org/10.3390/electronics12163449 - 15 Aug 2023
Viewed by 1004
Abstract
Over the past two decades, there has been a notable and swift advancement in the field of healthcare with regards to the Internet of Things (IoT). This progress has brought forth a substantial prospect for healthcare services to enhance performance, transparency, and cost [...] Read more.
Over the past two decades, there has been a notable and swift advancement in the field of healthcare with regards to the Internet of Things (IoT). This progress has brought forth a substantial prospect for healthcare services to enhance performance, transparency, and cost effectiveness. Internet of Things gateways, such as local computational facilities, mobile devices, or custom miniature computational embedded electronics like the Raspberry Pi (RPi), are crucial in facilitating the required processing and data compression tasks as well as serving as front-end event detectors. Numerous home-based healthcare monitoring systems are currently accessible; however, they have several limitations. This paper examines the role of the Raspberry Pi gateway in the healthcare system, specifically in the context of pre-operative prehabilitation programs (PoPPs). The IoT remote monitoring system employed a Microduino integrated with various supporting boards as a wearable device. Additionally, a Raspberry Pi was utilised as a base station or mobile gateway, while ThingSpeak served as the cloud platform. The monitoring system was developed with the purpose of assisting healthcare personnel in real time, remotely monitoring patients while engaging in one or more of the nine typical physical activities that are often prescribed to individuals participating in a prehabilitation program. Furthermore, an alert notification system was designed to notify the clinician and patient if the values were abnormal (i.e., the patient had not been active for many days). The integration of IOT and Raspberry Pi technology into a pre-operative prehabilitation program yielded a promising outcome with a success rate of 78%. Consequently, this intervention is expected to facilitate the resolution of challenges encountered by healthcare providers and patients, including extended waiting periods and constraints related to staffing and infrastructure. Full article
(This article belongs to the Special Issue Recent Advances in Wireless Ad Hoc and Sensor Networks)
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33 pages, 8378 KiB  
Article
Robust Deep Learning Models for OFDM-Based Image Communication Systems in Intelligent Transportation Systems (ITS) for Smart Cities
by Nazmul Islam and Seokjoo Shin
Electronics 2023, 12(11), 2425; https://doi.org/10.3390/electronics12112425 - 26 May 2023
Cited by 4 | Viewed by 1585
Abstract
Internet of Things (IoT) ecosystem in smart cities demands fast, reliable, and efficient image data transmission to enable real-time Computer Vision (CV) applications. To fulfill these demands, an Orthogonal Frequency Division Multiplexing (OFDM)-based communication system has been widely utilized due to its higher [...] Read more.
Internet of Things (IoT) ecosystem in smart cities demands fast, reliable, and efficient image data transmission to enable real-time Computer Vision (CV) applications. To fulfill these demands, an Orthogonal Frequency Division Multiplexing (OFDM)-based communication system has been widely utilized due to its higher spectral efficiency and data rate. When adapting such a system to achieve fast and reliable image transmission over fading channels, noise is introduced in the signal which heavily distorts the recovered image. This noise independently corrupts pixel values, however, certain intrinsic properties of the image, such as spatial information, may remain intact, which can be extracted as multidimensional features (in the convolution layers) and interpreted (in the top layers) by a Deep Learning (DL) model. Therefore, the current study analyzes the robustness of such DL models utilizing various OFDM-based image communication systems for CV applications in an Intelligent Transportation Systems (ITS) environment. Our analysis has shown that the EfficientNetV2-based model achieved a range of 70–90% accuracy across different OFDM-based image communication systems over the Rayleigh Fading channel. In addition, leveraging different data augmentation techniques further improves accuracy up to 18%. Full article
(This article belongs to the Special Issue Recent Advances in Wireless Ad Hoc and Sensor Networks)
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13 pages, 856 KiB  
Communication
Impact of Age Violation Probability on Neighbor Election-Based Distributed Slot Access in Wireless Ad Hoc Networks
by Wenjun Huang, Xu Li and Yanan Liang
Electronics 2023, 12(2), 351; https://doi.org/10.3390/electronics12020351 - 10 Jan 2023
Cited by 1 | Viewed by 1087
Abstract
In this paper, we propose an analytical model of neighbor election-based distributed slot access by considering the relationship between the age of information (AoI) and the slot access process of nodes in wireless ad hoc networks. A node first maintains the information updates [...] Read more.
In this paper, we propose an analytical model of neighbor election-based distributed slot access by considering the relationship between the age of information (AoI) and the slot access process of nodes in wireless ad hoc networks. A node first maintains the information updates from its neighbors by relaying and receiving messages and determines message transmission slots by holding elections with its relevant competing neighbors. We first find out and analyze the interaction relationship between the transmission probability of nodes, the competing probability of neighbor nodes, and the violation probability that AoI exceeds the timeliness threshold of the neighbor election. Next, we obtain the approximated expression of the competing probability and the age violation probability based on the comprehensive analysis of the neighbor election process. Numerical and simulation results show that our approximation is tighter than the one in the literature and also provides insights into enhancing the design of network-aware distributed multiple access schemes. Full article
(This article belongs to the Special Issue Recent Advances in Wireless Ad Hoc and Sensor Networks)
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15 pages, 622 KiB  
Article
The Rescuer’s Navigation in Metro Stations Based on Inertial Sensors and WiFi
by Qingyong Wang, Weiqiang Qu, Jian Chen and Zhiwei Wang
Electronics 2023, 12(1), 108; https://doi.org/10.3390/electronics12010108 - 27 Dec 2022
Viewed by 1347
Abstract
The demand for metro station rescue navigation is increasing. This paper presents an improved particle filter to challenge the navigation problem in metro stations. A particle filter is often used to estimate the position of pedestrians. However, the particle-impoverishment problem is inevitable. To [...] Read more.
The demand for metro station rescue navigation is increasing. This paper presents an improved particle filter to challenge the navigation problem in metro stations. A particle filter is often used to estimate the position of pedestrians. However, the particle-impoverishment problem is inevitable. To solve this problem, a dingo optimization algorithm (DOA) with global search ability is introduced, and an improved particle filter called a dingo particle filter (DPF) is proposed. Dead reckoning (DR) is taken as the system equation, and WiFi matching results are used as the observation equation. The improved particle filter algorithm introduces a dingo optimization algorithm to improve the diversity of particles and effectively reduce the particle-impoverishment problem. The experimental results show that the average positioning accuracy is 1.1 m and 1.2 m. Full article
(This article belongs to the Special Issue Recent Advances in Wireless Ad Hoc and Sensor Networks)
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