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Recent Advances in Internet of Things and System Design
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Recent Advances in Internet of Things and System Design

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 4685

Special Issue Editors

Dr. Ian D. Wilson

E-Mail Website
Guest Editor
Faculty of Computing, Engineering and Science, University of South Wales, Pontypridd CF37 1DL, UK
Interests: Internet of Things (IoT), system design, machine learning; artificial intelligence; optimisation and soft systems; cyber maintainable secure complex systems
Dr. ‪Reza Montasari‬

E-Mail Website
Guest Editor
Hillary Rodham Clinton School of Law, Swansea University, Swansea SA2 8PP, UK
Interests: digital forensics; cyber security; cyber threats; digital forensic investigation of uavs and iot devices; data visualisation

Special Issue Information

Dear Colleagues,

The aim of this Special Issue on the Internet of Things (IoT) and system design is to bring together innovative solutions, methodologies, and technologies that drive the integration and optimisation of IoT systems in real-world scenarios across domains such as healthcare, manufacturing, agriculture, smart cities, transportation, energy, and environmental monitoring. It seeks to provide insight into the latest advancements, challenges, and opportunities in designing, implementing, and managing IoT systems and their applications.

This Special Issue welcomes original research articles, review papers, and case studies covering, but not limited to, the following areas:

  • Innovative architectures, communication protocols, and standards for IoT systems;
  • Methodologies, tools, and techniques for designing, modelling, and optimising IoT systems;
  • Design and development of IoT devices, sensors, actuators, and embedded systems;
  • Approaches for collecting, processing, storing, and analysing IoT data;
  • Security and privacy challenges in IoT systems;
  • Real-world applications and case studies of IoT systems;
  • Strategies and techniques for integrating IoT systems with existing systems;
  • Standardisation efforts, policies, and governance models for IoT systems;
  • Exploration of the convergence of IoT with emerging technologies.

Dr. Ian D. Wilson
Dr. ‪Reza Montasari‬
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. Applied Sciences 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

  • Internet of Things (IoT)
  • system design
  • smart applications
  • embedded systems
  • data analytics
  • IoT security and privacy

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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

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19 pages, 7427 KiB  
Article
Battery Life Prediction for Ensuring Robust Operation of IoT Devices in Remote Metering
by Taein Yong, Chaebong Lee, Seongseop Kim and Jaeho Kim
Appl. Sci. 2025, 15(6), 2968; https://doi.org/10.3390/app15062968 - 10 Mar 2025
Viewed by 621
Abstract
Primary batteries are extensively employed as power sources in Internet of Things (IoT) devices for remote metering. However, primary batteries maintain a relatively consistent discharge voltage curve over a long period before experiencing a full discharge, making it challenging to predict the battery’s [...] Read more.
Primary batteries are extensively employed as power sources in Internet of Things (IoT) devices for remote metering. However, primary batteries maintain a relatively consistent discharge voltage curve over a long period before experiencing a full discharge, making it challenging to predict the battery’s life. In this study, we introduce a battery life prediction method to ensure the robust operation of IoT devices in remote metering applications. The robust battery life prediction process is divided into two stages. The first stage involves predicting the state of charge (SOC) to enable real-time remote monitoring of the battery status of metering devices. In the second stage, IoT devices implement a hardware-based alerting mechanism to provide warnings prior to complete discharge, leveraging a custom-designed Multi-Stage Discharge battery architecture. In the first stage, we developed the CNN-Series Decomposition Transformer (C-SDFormer) model, which is capable of accurately predicting the SOC of primary batteries. This model was specifically designed to support the real-time monitoring of battery status in large-scale IoT deployments, enabling proactive maintenance and enhancing system reliability. To validate the performance of the C-SDFormer model, data were collected from smart remote meters installed in households. The model was trained using the collected data and evaluated through a series of experiments. The performance of the C-SDFormer model was compared with existing methods for SOC prediction. The results indicate that the C-SDFormer model outperformed the traditional methods. Specifically, the SOC prediction achieved a mean absolute error (MAE) of less than 4.1%, a root mean square error (RMSE) of less than 5.2%, a symmetric mean absolute percentage error (SMAPE) of less than 7.0%, and a coefficient of determination (R2) exceeding 0.96. These results demonstrate the effectiveness of the C-SDFormer model in accurately predicting the SOC of primary batteries. For the second stage, a Multi-Stage Discharge (MSD) primary battery was developed to ensure a hardware-based low battery alert before the battery is fully discharged. This battery was designed to ensure the reliable operation of IoT devices, especially those whose batteries are not proactively managed through real-time monitoring in the first stage. By providing a low battery alert, the MSD battery reduces the risk of unexpected device shutdowns. This feature enhances the overall reliability of IoT devices, ensuring their continuous operation in remote metering applications. Full article
(This article belongs to the Special Issue Recent Advances in Internet of Things and System Design)
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29 pages, 18875 KiB  
Article
Enhancing Driving Safety of Personal Mobility Vehicles Using On-Board Technologies
by Eru Choi, Tuan Anh Dinh and Min Choi
Appl. Sci. 2025, 15(3), 1534; https://doi.org/10.3390/app15031534 - 3 Feb 2025
Viewed by 855
Abstract
Accidents involving electric wheelchairs are a growing concern, with users frequently encountering obstacles that lead to collisions, tipping, or loss of balance. These incidents underscore the need for advanced safety technologies tailored to electric wheelchair users. This research addresses this need by developing [...] Read more.
Accidents involving electric wheelchairs are a growing concern, with users frequently encountering obstacles that lead to collisions, tipping, or loss of balance. These incidents underscore the need for advanced safety technologies tailored to electric wheelchair users. This research addresses this need by developing a driving assistance system to prevent accidents and enhance user safety. The system incorporates ultrasonic sensors and a front-facing camera to detect obstacles and provide real-time warnings. The proposed system operates independently of stable server communication and employs embedded hardware for fast object detection and environmental recognition, ensuring immediate guidance in various scenarios. In this research, we utilized the existing yolov8 model as is. But we attempted to improve performance by hardware acceleration of convolutional neural networks, supporting various layers such as convolution, deconvolution, pooling, batch normalization, and others. Thus, the YOLO model was accelerated during inference on the specialized hardware in our experiments. Performance was evaluated in diverse environments to assess its usability. Results demonstrated high accuracy in detecting obstacles and providing timely warnings. Leveraging hardware acceleration for YOLOv8 delivers faster, scalable, and robust object detection, making it a great platform for enhancing driving safety on edge and embedded devices. These findings provide a strong foundation for future advancements in safety assistance systems for electric wheelchairs and other mobility devices. Future research will focus on enhancing system performance and integrating additional features to create a safer environment for electric wheelchair users. Full article
(This article belongs to the Special Issue Recent Advances in Internet of Things and System Design)
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17 pages, 630 KiB  
Article
Optimizing HAWK Signature Scheme Performance on ARMv8
by Siwoo Eum, Minwoo Lee and Hwajeong Seo
Appl. Sci. 2024, 14(19), 8647; https://doi.org/10.3390/app14198647 - 25 Sep 2024
Viewed by 907
Abstract
This study proposes an optimized implementation of the HAWK Signature algorithm, one of the candidates in the first evaluation round for additional digital signature schemes in the NIST Post-Quantum Cryptography competition. The core motivation of this research is to improve the performance of [...] Read more.
This study proposes an optimized implementation of the HAWK Signature algorithm, one of the candidates in the first evaluation round for additional digital signature schemes in the NIST Post-Quantum Cryptography competition. The core motivation of this research is to improve the performance of HAWK algorithm. By conducting profiling analysis to identify, we identified the most resource-intensive functions. And then we optimized the functions. The optimization techniques through profiling analysis are not limited to HAWK but can be applied to other algorithms as well. Additionally, the study demonstrates how efficient optimization can be achieved using fewer instructions by leveraging lesser-known ARMv8 instructions. By targeting the functions with the highest overhead and utilizing fewer instructions, a performance improvement of approximately 2.5% for Hawk512 and 4% for Hawk1024 was achieved, respectively. These results confirm that combining profiling analysis with efficient instruction usage can lead to significant performance improvements. Full article
(This article belongs to the Special Issue Recent Advances in Internet of Things and System Design)
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Review

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28 pages, 1659 KiB  
Review
Analysis of Enterprise Internet of Things Maturity Models: A Review
by Andrés Felipe Solis Pino, Pablo H. Ruiz, Alicia Mon, Cesar Alberto Collazos and Fernando Moreira
Appl. Sci. 2024, 14(21), 9859; https://doi.org/10.3390/app14219859 - 28 Oct 2024
Cited by 1 | Viewed by 1671
Abstract
Maturity models are valuable tools when assessing the readiness and progress of technology incorporation in organizations, providing information for decision-making, resource allocation, and competitive advantage. The Internet of Things is a technology paradigm of global importance, especially for organizations, as it supports productivity [...] Read more.
Maturity models are valuable tools when assessing the readiness and progress of technology incorporation in organizations, providing information for decision-making, resource allocation, and competitive advantage. The Internet of Things is a technology paradigm of global importance, especially for organizations, as it supports productivity improvements, real-time analysis, and customer satisfaction. Therefore, adopting and implementing this technology in enterprises brings several challenges, such as technological, organizational, security, and maturity issues. However, secondary studies that systematically compile the existing literature on these specific mechanisms for the enterprise domain are still being determined. This article aims to address this knowledge gap by conducting a review to deepen and synthesize the existing knowledge. This research followed established methodologies and protocols to synthesize and analyze the state of the art in the area; 489 documents were retrieved from seven bibliographic databases, and, applying inclusion and exclusion criteria, 36 primary studies were selected. The results indicate that the typical structures of maturity models incorporate technological, organizational, human, performance, and security dimensions through graded levels that denote the sophistication of the Internet of Things. Measurement techniques and metrics vary from model to model. There are few empirical validations or standardized improvement frameworks. The main conclusion is that there is a diversity of models, dimensions, indicators, and methods and a need for more comprehensive, adaptable, and user-friendly tools to help companies assess their Internet of Things maturity and inform future development strategies. Full article
(This article belongs to the Special Issue Recent Advances in Internet of Things and System Design)
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