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IoT-Based Smart Environments, Applications and Tools
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IoT-Based Smart Environments, Applications and Tools

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

Deadline for manuscript submissions: 10 May 2025 | Viewed by 2154

Special Issue Editors

Dr. Waheb Abdullah

E-Mail Website
Guest Editor
College of Engineering, Faculty of Computing, Engineering and the Built Environment, Birmingham City University, Birmingham B4 7XG, United Kingdom
Interests: internet of things; cyber-physical systems; wireless networks; smart city
Special Issues, Collections and Topics in MDPI journals
Dr. Abdulghani Ali Ahmed

E-Mail Website
Guest Editor
Computing, Engineering, and Media (CEM) Faculty, De Montfort University, Leicester, UK
Interests: cybersecurity; forensic investigation

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is a key technology in the current era of digital transformation, revolutionizing our interactions with our surroundings and leading to the emergence of 'smart environments'. This Special Issue, titled “IoT-Based Smart Environments, Applications and Tools”, serves as an interdisciplinary platform that highlights the most recent advancements, trends, and breakthroughs in the field of IoT for smart environments. Its goal is to present research that demonstrates the incorporation of IoT technology in developing smart environments, thereby improving daily human life and boosting efficiency in industrial settings. This Special Issue seeks submissions that focus on innovative IoT solutions, applications, and tools created for real-world challenges across different environment sectors, such as smart homes, cities, industries, and healthcare systems. Emphasis is placed on implementing smart sensor technologies in these settings, showcasing progress in IoT frameworks, data analytics, and the incorporation of AI for more intelligent decision-making processes.

This SI aligns with the scope of Sensors, as it underscores the pivotal role of sensor technology within the IoT ecosystem, providing insights into cutting-edge sensor-based solutions for smart environments.

Dr. Waheb Abdullah
Dr. Abdulghani Ali Ahmed
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

  • IoT applications
  • smart environment systems
  • advanced sensor technology
  • industrial internet of things
  • urban IoT integration
  • healthcare IoT solutions
  • AI-enhanced IoT
  • IoT security measures
  • IoT data analytics.

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Published Papers (3 papers)

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Research

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18 pages, 2738 KiB  
Article
PSA-FL-CDM: A Novel Federated Learning-Based Consensus Model for Post-Stroke Assessment
by Najmeh Razfar, Rasha Kashef and Farah Mohammadi
Sensors 2024, 24(16), 5095; https://doi.org/10.3390/s24165095 - 6 Aug 2024
Viewed by 696
Abstract
The rapid development of Internet of Things (IoT) technologies and the potential benefits of employing the vast datasets generated by IoT devices, including wearable sensors and camera systems, has ushered in a new era of opportunities for enhancing smart rehabilitation in various healthcare [...] Read more.
The rapid development of Internet of Things (IoT) technologies and the potential benefits of employing the vast datasets generated by IoT devices, including wearable sensors and camera systems, has ushered in a new era of opportunities for enhancing smart rehabilitation in various healthcare systems. Maintaining patient privacy is paramount in healthcare while providing smart insights and recommendations. This study proposed the adoption of federated learning to develop a scalable AI model for post-stroke assessment while protecting patients’ privacy. This research compares the centralized (PSA-MNMF) model performance with the proposed scalable federated PSA-FL-CDM model for sensor- and camera-based datasets. The computational time indicates that the federated PSA-FL-CDM model significantly reduces the execution time and attains comparable performance while preserving the patient’s privacy. Impact Statement—This research introduces groundbreaking contributions to stroke assessment by successfully implementing federated learning for the first time in this domain and applying consensus models in each node. It enables collaborative model training among multiple nodes or clients while ensuring the privacy of raw data. The study explores eight different clustering methods independently on each node, revolutionizing data organization based on similarities in stroke assessment. Additionally, the research applies the centralized PSA-MNMF consensus clustering technique to each client, resulting in more accurate and robust clustering solutions. By utilizing the FedAvg federated learning algorithm strategy, locally trained models are combined to create a global model that captures the collective knowledge of all participants. Comparative performance measurements and computational time analyses are conducted, facilitating a fair evaluation between centralized and federated learning models in stroke assessment. Moreover, the research extends beyond a single type of database by conducting experiments on two distinct datasets, wearable and camera-based, broadening the understanding of the proposed methods across different data modalities. These contributions develop stroke assessment methodologies, enabling efficient collaboration and accurate consensus clustering models and maintaining data privacy. Full article
(This article belongs to the Special Issue IoT-Based Smart Environments, Applications and Tools)
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16 pages, 2062 KiB  
Communication
Enhancing Hospital Efficiency and Patient Care: Real-Time Tracking and Data-Driven Dispatch in Patient Transport
by Su-Wen Huang, Shyue-Yow Chiou, Rung-Ching Chen and Chayanon Sub-r-pa
Sensors 2024, 24(12), 4020; https://doi.org/10.3390/s24124020 - 20 Jun 2024
Viewed by 569
Abstract
Inefficient patient transport in hospitals often leads to delays, overworked staff, and suboptimal resource utilization, ultimately impacting patient care. Existing dispatch management algorithms are often evaluated in simulation environments, raising concerns about their real-world applicability. This study presents a real-world experiment that bridges [...] Read more.
Inefficient patient transport in hospitals often leads to delays, overworked staff, and suboptimal resource utilization, ultimately impacting patient care. Existing dispatch management algorithms are often evaluated in simulation environments, raising concerns about their real-world applicability. This study presents a real-world experiment that bridges the gap between theoretical dispatch algorithms and real-world implementation. It applies process capability analysis at Taichung Veterans General Hospital in Taichung, Taiwan, and utilizes IoT for real-time tracking of staff and medical devices to address challenges associated with manual dispatch processes. Experimental data collected from the hospital underwent statistical evaluation between January 2021 and December 2021. The results of our experiment, which compared the use of traditional dispatch methods with the Beacon dispatch method, found that traditional dispatch had an overtime delay of 41.0%; in comparison, the Beacon dispatch method had an overtime delay of 26.5%. These findings demonstrate the transformative potential of this solution for not only hospital operations but also for improving service quality across the healthcare industry in the context of smart hospitals. Full article
(This article belongs to the Special Issue IoT-Based Smart Environments, Applications and Tools)
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22 pages, 1170 KiB  
Systematic Review
Systematic Review of IoT-Based Solutions for User Tracking: Towards Smarter Lifestyle, Wellness and Health Management
by Reza Amini Gougeh and Zeljko Zilic
Sensors 2024, 24(18), 5939; https://doi.org/10.3390/s24185939 - 13 Sep 2024
Viewed by 474
Abstract
The Internet of Things (IoT) base has grown to over 20 billion devices currently operational worldwide. As they greatly extend the applicability and use of biosensors, IoT developments are transformative. Recent studies show that IoT, coupled with advanced communication frameworks, such as machine-to-machine [...] Read more.
The Internet of Things (IoT) base has grown to over 20 billion devices currently operational worldwide. As they greatly extend the applicability and use of biosensors, IoT developments are transformative. Recent studies show that IoT, coupled with advanced communication frameworks, such as machine-to-machine (M2M) interactions, can lead to (1) improved efficiency in data exchange, (2) accurate and timely health monitoring, and (3) enhanced user engagement and compliance through advancements in human–computer interaction. This systematic review of the 19 most relevant studies examines the potential of IoT in health and lifestyle management by conducting detailed analyses and quality assessments of each study. Findings indicate that IoT-based systems effectively monitor various health parameters using biosensors, facilitate real-time feedback, and support personalized health recommendations. Key limitations include small sample sizes, insufficient security measures, practical issues with wearable sensors, and reliance on internet connectivity in areas with poor network infrastructure. The reviewed studies demonstrated innovative applications of IoT, focusing on M2M interactions, edge devices, multimodality health monitoring, intelligent decision-making, and automated health management systems. These insights offer valuable recommendations for optimizing IoT technologies in health and wellness management. Full article
(This article belongs to the Special Issue IoT-Based Smart Environments, Applications and Tools)
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