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Internet of Things and Emerging Technologies for Smart Environments
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Internet of Things and Emerging Technologies for Smart Environments

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

Deadline for manuscript submissions: closed (15 January 2024) | Viewed by 4894

Special Issue Editors

Dr. Farhan Amin

E-Mail Website
Guest Editor
Department of Information and communication engineering (ICE), Yeungnam University, Gyeongsan 38541, Republic of Korea
Interests: Internet of Things; Social Internet of Things; big data analytics; inclusive smart cities; data science
Special Issues, Collections and Topics in MDPI journals
Dr. Ikram Asghar

E-Mail Website
Guest Editor
Senior Lecturer Computer Science, Teesside University, Middlesbrough, UK
Interests: information technology; human computer interaction; Alzheimer's disease; requirements engineering; augmented and virtual reality; software engineering
Dr. Aftab Ali

E-Mail Website
Guest Editor
School of Computing, Ulster University, Shore Rd, Newtownabbey BT37 0QB, UK
Interests: applied AI; IoT security and privacy; key agreement; body area networks; blockchains
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the Internet of Things, together with its related emerging technologies, has been driving a revolution in the way people perceive and interact with the surrounding environment. Smart homes and smart offices are effective examples that are enriched with sensing, actuating, communication, and computing capabilities. The IoT provides an umbrella under which many heterogeneous technologies and objects interact and co-operate. The need to enhance its performance with characteristics from other more mature technologies is rising. The full potential of the emerging IoT paradigm requires a large amount of industrial and academic research effort directed to the design, development, and assessment of novel architectures, methodologies, and technologies. The Social Internet of Things (SIoT) refers to the convergence of the Internet of Things and social networking paradigms for the creation of social networks in which things are nodes that establish social links as humans do. This concept has become a hot topic in academic research thanks to the benefits deriving from the potential of social networks within the IoT domain, such as simplification in the navigability of a dynamic network of billions of objects, robustness in the management of the trustworthiness of objects when providing information and services, and efficiency in the dynamic discovery of services and information. The objective of this Special Issue is to present a useful reference regarding recent advancements and developments in IoT and emerging technologies

Dr. Farhan Amin
Dr. Ikram Asghar
Dr. Aftab Ali
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

  • Internet of Things
  • cyber–physical systems
  • smart environments
  • standards and protocols for the Internet of Things
  • IoT toward COVID-19
  • trusted IoT ecosystem
  • smart home
  • big data for IoT
  • machine learning and artificial intelligence for the Internet of Things
  • social Internet of Things
  • SIoT application in health care and hospital information systems
  • data mining and analytics in SIoT
  • SIoT and smart cities
  • cybersecurity in SIoT
  • SIoT and autonomous driving systems
  • SIoT application in smart home systems
  • SIoT experimental platforms
  • machine learning techniques in SIoT
  • cognitive data processing in the SIoT
  • smart environments
  • service discovery method and technology of the SIoT
  • link selection method and technology of the SIoT
  • community discovery method and technology of the SIoT
  • security in IoT
  • trust in SIoT
  • IoT and the digital twins
  • smart home and the digital twin

Published Papers (3 papers)

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28 pages, 7234 KiB  
Article
Prototype of Monitoring Transportation Pollution Spikes through the Internet of Things Edge Networks
by Eric Nizeyimana, Damien Hanyurwimfura, Junseok Hwang, Jimmy Nsenga and Dereje Regassa
Sensors 2023, 23(21), 8941; https://doi.org/10.3390/s23218941 - 03 Nov 2023
Viewed by 1239
Abstract
Air pollution is a critical problem in densely populated urban areas, with traffic significantly contributing. To mitigate the adverse effects of air pollution on public health and the environment, there is a growing need for the real-time monitoring and detection of pollution spikes [...] Read more.
Air pollution is a critical problem in densely populated urban areas, with traffic significantly contributing. To mitigate the adverse effects of air pollution on public health and the environment, there is a growing need for the real-time monitoring and detection of pollution spikes in transportation. This paper presents a novel approach to using Internet of Things (IoT) edge networks for the real-time detection of air pollution peaks in transportation, specifically designed for innovative city applications. The proposed system uses IoT sensors in buses, cabs, and private cars. These sensors are equipped with air quality monitoring capabilities, including the measurement of pollutants such as particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), and carbon dioxide (CO2). The sensors continuously collect air quality data and transmit them to edge devices within the transportation infrastructure. The data collected by these sensors are analyzed, and alerts are generated when pollution levels exceed predefined thresholds. By deploying this system within IoT edge networks, transportation authorities can promptly respond to pollution spikes, improving air quality, public health, and environmental sustainability. This paper details the sensor technology, data analysis methods, and the practical implementation of this innovative system, shedding light on its potential for addressing the pressing issue of transportation-related pollution. The proposed IoT edge network for real-time air pollution spike detection in transportation offers significant advantages, including low-latency data processing, scalability, and cost-effectiveness. By leveraging the power of edge computing and IoT technologies, smart cities can proactively monitor and manage air pollution, leading to healthier and more sustainable urban environments. Full article
(This article belongs to the Special Issue Internet of Things and Emerging Technologies for Smart Environments)
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17 pages, 4938 KiB  
Article
Efficient Classification of ECG Images Using a Lightweight CNN with Attention Module and IoT
by Tariq Sadad, Mejdl Safran, Inayat Khan, Sultan Alfarhood, Razaullah Khan and Imran Ashraf
Sensors 2023, 23(18), 7697; https://doi.org/10.3390/s23187697 - 06 Sep 2023
Cited by 1 | Viewed by 1588
Abstract
Cardiac disorders are a leading cause of global casualties, emphasizing the need for the initial diagnosis and prevention of cardiovascular diseases (CVDs). Electrocardiogram (ECG) procedures are highly recommended as they provide crucial cardiology information. Telemedicine offers an opportunity to provide low-cost tools and [...] Read more.
Cardiac disorders are a leading cause of global casualties, emphasizing the need for the initial diagnosis and prevention of cardiovascular diseases (CVDs). Electrocardiogram (ECG) procedures are highly recommended as they provide crucial cardiology information. Telemedicine offers an opportunity to provide low-cost tools and widespread availability for CVD management. In this research, we proposed an IoT-based monitoring and detection system for cardiac patients, employing a two-stage approach. In the initial stage, we used a routing protocol that combines routing by energy and link quality (REL) with dynamic source routing (DSR) to efficiently collect data on an IoT healthcare platform. The second stage involves the classification of ECG images using hybrid-based deep features. Our classification system utilizes the “ECG Images dataset of Cardiac Patients”, comprising 12-lead ECG images with four distinct categories: abnormal heartbeat, myocardial infarction (MI), previous history of MI, and normal ECG. For feature extraction, we employed a lightweight CNN, which automatically extracts relevant ECG features. These features were further optimized through an attention module, which is the method’s main focus. The model achieved a remarkable accuracy of 98.39%. Our findings suggest that this system can effectively aid in the identification of cardiac disorders. The proposed approach combines IoT, deep learning, and efficient routing protocols, showcasing its potential for improving CVD diagnosis and management. Full article
(This article belongs to the Special Issue Internet of Things and Emerging Technologies for Smart Environments)
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15 pages, 875 KiB  
Article
Microservice Application Scheduling in Multi-Tiered Fog-Computing-Enabled IoT
by Maria Ashraf, Muhammad Shiraz, Almas Abbasi, Omar Alqahtani, Gran Badshah and Ayodele Lasisi
Sensors 2023, 23(16), 7142; https://doi.org/10.3390/s23167142 - 12 Aug 2023
Cited by 1 | Viewed by 1082
Abstract
Fog computing extends mobile cloud computing facilities at the network edge, yielding low-latency application execution. To supplement cloud services, computationally intensive applications can be distributed on resource-constrained mobile devices by leveraging underutilized nearby resources to meet the latency and bandwidth requirements of application [...] Read more.
Fog computing extends mobile cloud computing facilities at the network edge, yielding low-latency application execution. To supplement cloud services, computationally intensive applications can be distributed on resource-constrained mobile devices by leveraging underutilized nearby resources to meet the latency and bandwidth requirements of application execution. Building upon this premise, it is necessary to investigate idle or underutilized resources that are present at the edge of the network. The utilization of a microservice architecture in IoT application development, with its increased granularity in service breakdown, provides opportunities for improved scalability, maintainability, and extensibility. In this research, the proposed schedule tackles the latency requirements of applications by identifying suitable upward migration of microservices within a multi-tiered fog computing infrastructure. This approach enables optimal utilization of network edge resources. Experimental validation is performed using the iFogSim2 simulator and the results are compared with existing baselines. The results demonstrate that compared to the edgewards approach, our proposed technique significantly improves the latency requirements of application execution, network usage, and energy consumption by 66.92%, 69.83%, and 4.16%, respectively. Full article
(This article belongs to the Special Issue Internet of Things and Emerging Technologies for Smart Environments)
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