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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 13279

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Special Issue Editor

Dr. Kang Hao Cheong

E-Mail Website1 Website2
Guest Editor

1. School of Computer Science and Engineering, Nanyang Technological University, Singapore 639818, Singapore
2. Science, Mathematics and Technology, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore
Interests: game theory; extended reality; data science; AI/ML in the medical field/healthcare; pedagogy and educational research
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Special Issue Information

Dear Colleagues,

Due to the advent in cloud technology, data fusion, and processing, systems can easily interact with various components and the environment to optimize processes via learning through interactions. The IoT and AI have gradually become an essential part of sensor networks. These technologies have many industrial applications, ranging from medical and healthcare to building and construction, and even educational devices. There is immense interest in the topic from researchers across many different fields for numerous applications—e.g., the collection of various data in the building and construction industry through sensors for centralized processing (based on machine learning methods) to achieve the purpose of energy-saving and comfort, the collection of EEG signals from medical devices and the analysis of patient status for automated management, portable EEG technology in educational research, or even sensing technologies for VR/AR, just to name a few.

The scope of this Special Issue is fairly broad with a focus on the use of IoT and/or AI in sensor networks, including but not limited to education, healthcare, medical, engineering, building, and construction. We welcome both empirical and review submissions.

Dr. Kang Hao Cheong
Guest Editor

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

IoT
sensors
artificial intelligence
building
construction
engineering
education
medical
healthcare
machine learning

Published Papers (5 papers)

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Research

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18 pages, 9592 KiB

Open AccessArticle

Monitoring System for Operating Variables in Incubators in the Neonatology Service of a Highly Complex Hospital through the Internet of Things (IoT)

by Pedro Antonio Aya-Parra, Andres Jacob Rodriguez-Orjuela, Viviana Rodriguez Torres, Nidia Patricia Cordoba Hernandez, Natalia Martinez Castellanos and Jefferson Sarmiento-Rojas

Sensors 2023, 23(12), 5719; https://0-doi-org.brum.beds.ac.uk/10.3390/s23125719 - 19 Jun 2023

Cited by 2 | Viewed by 1762

Abstract

Background: Around 15 million premature babies are born annually, requiring specialized care. Incubators are vital for maintaining their body temperature, which is crucial for their well-being. Ensuring optimal conditions in incubators, including constant temperature, oxygen control, and comfort, is essential for improving the care and survival rates of these infants. Methods: To address this, an IoT-based monitoring system was developed in a hospital setting. The system comprised hardware components such as sensors and a microcontroller, along with software components including a database and a web application. The microcontroller collected data from the sensors, which was then transmitted to a broker via WiFi using the MQTT protocol. The broker validated and stored the data in the database, while the web application provided real-time access, alerts, and event recording. Results: Two certified devices were created, employing high quality components. The system was successfully implemented and tested in both the biomedical engineering laboratory and the neonatology service of the hospital. The results of the pilot test supported the concept of IoT-based technology, demonstrating satisfactory responses in temperature, humidity, and sound variables within the incubators. Conclusions: The monitoring system facilitated efficient record traceability, allowing access to data over various timeframes. It also captured event records (alerts) related to variable problems, providing information on duration, date, hour, and minutes. Overall, the system offered valuable insights and enhanced monitoring capabilities for neonatal care. Full article

(This article belongs to the Special Issue Advances in IoT and Sensor Networks)

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17 pages, 1719 KiB

Open AccessArticle

Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration

by George Papastergiou, Apostolos Xenakis, Costas Chaikalis, Dimitrios Kosmanos, Periklis Chatzimisios and Nicholas S. Samaras

Sensors 2023, 23(12), 5422; https://0-doi-org.brum.beds.ac.uk/10.3390/s23125422 - 08 Jun 2023

Viewed by 1114

Abstract

In dense IoT deployments of wireless sensor networks (WSNs), sensor placement, coverage, connectivity, and energy constraints determine the overall network lifetime. In large-size WSNs, it is difficult to maintain a trade-off among these conflicting constraints and, thus, scaling is difficult. In the related research literature, various solutions are proposed that attempt to address near-optimal behavior in polynomial time, the majority of which relies on heuristics. In this paper, we formulate a topology control and lifetime extension problem regarding sensor placement, under coverage and energy constraints, and solve it by applying and testing several neural network configurations. To do so, the neural network dynamically proposes and handles sensor placement coordinates in a 2D plane, having the ultimate goal to extend network lifetime. Simulation results show that our proposed algorithm improves network lifetime, while maintaining communication and energy constraints, for medium- and large-scale deployments. Full article

(This article belongs to the Special Issue Advances in IoT and Sensor Networks)

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17 pages, 2832 KiB

Open AccessArticle

BFT-IoMT: A Blockchain-Based Trust Mechanism to Mitigate Sybil Attack Using Fuzzy Logic in the Internet of Medical Things

by Shayan E Ali, Noshina Tariq, Farrukh Aslam Khan, Muhammad Ashraf, Wadood Abdul and Kashif Saleem

Sensors 2023, 23(9), 4265; https://0-doi-org.brum.beds.ac.uk/10.3390/s23094265 - 25 Apr 2023

Cited by 7 | Viewed by 1986

Abstract

Numerous sensitive applications, such as healthcare and medical services, need reliable transmission as a prerequisite for the success of the new age of communications technology. Unfortunately, these systems are highly vulnerable to attacks like Sybil, where many false nodes are created and spread with deceitful intentions. Therefore, these false nodes must be instantly identified and isolated from the network due to security concerns and the sensitivity of data utilized in healthcare applications. Especially for life-threatening diseases like COVID-19, it is crucial to have devices connected to the Internet of Medical Things (IoMT) that can be believed to respond with high reliability and accuracy. Thus, trust-based security offers a safe environment for IoMT applications. This study proposes a blockchain-based fuzzy trust management framework (BFT-IoMT) to detect and isolate Sybil nodes in IoMT networks. The results demonstrate that the proposed BFT-IoMT framework is 25.43% and 12.64%, 12.54% and 6.65%, 37.85% and 19.08%, 17.40% and 8.72%, and 13.04% and 5.05% more efficient and effective in terms of energy consumption, attack detection, trust computation reliability, packet delivery ratio, and throughput, respectively, as compared to the other state-of-the-art frameworks available in the literature. Full article

(This article belongs to the Special Issue Advances in IoT and Sensor Networks)

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18 pages, 1401 KiB

Open AccessArticle

A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG Signal

by Mehmet Baygin, Prabal Datta Barua, Sengul Dogan, Turker Tuncer, Sefa Key, U. Rajendra Acharya and Kang Hao Cheong

Sensors 2022, 22(5), 2007; https://0-doi-org.brum.beds.ac.uk/10.3390/s22052007 - 04 Mar 2022

Cited by 16 | Viewed by 2441

Abstract

Recently, deep models have been very popular because they achieve excellent performance with many classification problems. Deep networks have high computational complexities and require specific hardware. To overcome this problem (without decreasing classification ability), a hand-modeled feature selection method is proposed in this paper. A new shape-based local feature extractor is presented which uses the geometric shape of the frustum. By using a frustum pattern, textural features are generated. Moreover, statistical features have been extracted in this model. Textures and statistics features are fused, and a hybrid feature extraction phase is obtained; these features are low-level. To generate high level features, tunable Q factor wavelet transform (TQWT) is used. The presented hybrid feature generator creates 154 feature vectors; hence, it is named Frustum154. In the multilevel feature creation phase, this model can select the appropriate feature vectors automatically and create the final feature vector by merging the appropriate feature vectors. Iterative neighborhood component analysis (INCA) chooses the best feature vector, and shallow classifiers are then used. Frustum154 has been tested on three basic hand-movement sEMG datasets. Hand-movement sEMG datasets are commonly used in biomedical engineering, but there are some problems in this area. The presented models generally required one dataset to achieve high classification ability. In this work, three sEMG datasets have been used to test the performance of Frustum154. The presented model is self-organized and selects the most informative subbands and features automatically. It achieved 98.89%, 94.94%, and 95.30% classification accuracies using shallow classifiers, indicating that Frustum154 can improve classification accuracy. Full article

(This article belongs to the Special Issue Advances in IoT and Sensor Networks)

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Review

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15 pages, 2089 KiB

Open AccessReview

Enhancing the MEP Coordination Process with BIM Technology and Management Strategies

by Ya Hui Teo, Jun Hong Yap, Hui An, Simon Ching Man Yu, Limao Zhang, Jie Chang and Kang Hao Cheong

Sensors 2022, 22(13), 4936; https://0-doi-org.brum.beds.ac.uk/10.3390/s22134936 - 30 Jun 2022

Cited by 7 | Viewed by 4976

Abstract

Building Information Modeling (BIM) has been increasingly used in coordinating the different mechanical, electrical, and plumbing (MEP) services in the construction industries. As the construction industries are slowly adapting to BIM, the use of 2D software may become obsolete in the future as MEP services are technically more complicated to coordinate, due to respective services’ codes of practice to follow and limit ceiling height. The 3D MEP designs are easy to visualize before installing the respective MEP services on the construction site to prevent delay in the construction process. The aid of current advanced technology has brought BIM to the next level to reduce manual work through automation. Combining both innovative technology and suitable management methods not only improves the workflow in design coordination, but also decreases conflict on the construction site and lowers labor costs. Therefore, this paper tries to explore possible advance technology in BIM and management strategies that could help MEP services to increase productivity, accuracy, and efficiency with a lower cost of finalizing the design of the building. This will assist the contractors to complete construction works before the targeted schedule and meet the client’s expectations. Full article

(This article belongs to the Special Issue Advances in IoT and Sensor Networks)

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