IoT in Smart Cities and Homes

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

Deadline for manuscript submissions: closed (30 October 2023) | Viewed by 28113

Special Issue Editors

Head of Division for Computing, School of Computing, Engineering and Physical Sciences, University of the West of Scotland, High Street, Paisley PA1 2BE, UK
Interests: computer vision; embedded systems; machine learning; Internet of Things; signal processing
Special Issues, Collections and Topics in MDPI journals
SMART Technology Research Centre, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK
Interests: computer networking; network security; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is a unique domain where various technologies converge to deliver novel high-impact smart solutions across various sectors, including digital health, optimized transportation, predictive maintenance, energy efficiency and improved environmental sustainability. IoT is a key enabler for current advancements in complex, dynamic and evolving environments, such as achieving smart homes and large geographical smart city applications. The current advances in IoT capabilities have accelerated creativity and achieved technological solutions that were previously unobtainable. These novel IoT applications deliver high-impact solutions within society, through leveraging a range of key technologies, including smart sensing, long-range and low-power communications, edge computing devices, wearable technologies, cyber security, environmental sensors, big data analysis, machine learning, fog computing and data science and analysis.

This Special Issue in MDPI’s journal Applied Sciences calls for submissions of new ideas, experiments, high-impact advances and findings in IoT applications for smart cities and homes.

Dr. Ryan Gibson
Prof. Dr. Hadi Larijani
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
  • intelligent systems
  • smart sensing
  • wearable devices
  • low-power communications
  • edge computing
  • fog computing
  • artificial intelligence
  • machine learning
  • deep learning
  • algorithmic implementation
  • optimization methods
  • data science

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Published Papers (20 papers)

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23 pages, 4590 KiB  
Article
Research on Digital Forensics Analyzing Heterogeneous Internet of Things Incident Investigations
by Dong-Hyuk Shin, Seung-Ju Han, Yu-Bin Kim and Ieck-Chae Euom
Appl. Sci. 2024, 14(3), 1128; https://doi.org/10.3390/app14031128 - 29 Jan 2024
Viewed by 730
Abstract
In the landscape of the Fourth Industrial Revolution, the integration of the Internet of Things (IoT) in smart-home technology presents intricate challenges for digital forensics. This study investigates these challenges, focusing on developing forensic methodologies suitable for the diverse and complex world of [...] Read more.
In the landscape of the Fourth Industrial Revolution, the integration of the Internet of Things (IoT) in smart-home technology presents intricate challenges for digital forensics. This study investigates these challenges, focusing on developing forensic methodologies suitable for the diverse and complex world of smart-home IoT devices. This research is contextualized within the rising trend of interconnected smart homes and their associated cybersecurity vulnerabilities. Methodologically, we formulate a comprehensive approach combining open-source intelligence, application, network, and hardware analyses, aiming to accommodate the operational and data storage characteristics of various IoT devices. Extensive experiments were conducted on prevalent platforms, such as Samsung SmartThings, Aqara, QNAP NAS, and Hikvision IP cameras, to validate the proposed methodology. These experiments revealed crucial insights into the complexities of forensic data acquisition in smart-home environments, emphasizing the need for customized forensic strategies tailored to the specific attributes of various IoT devices. The study significantly advances the field of IoT digital forensics and provides a foundational framework for future explorations into broader IoT scenarios. It underscores the need for evolving forensic methodologies to keep pace with rapid technological advancements in IoT. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes)
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19 pages, 1680 KiB  
Article
Epileptic Seizure Classification Based on Random Neural Networks Using Discrete Wavelet Transform for Electroencephalogram Signal Decomposition
by Syed Yaseen Shah, Hadi Larijani, Ryan M. Gibson and Dimitrios Liarokapis
Appl. Sci. 2024, 14(2), 599; https://doi.org/10.3390/app14020599 - 10 Jan 2024
Viewed by 866
Abstract
An epileptic seizure is a brief episode of symptoms and signs caused by excessive electrical activity in the brain. One of the major chronic neurological diseases, epilepsy, affects millions of individuals worldwide. Effective detection of seizure events is critical in the diagnosis and [...] Read more.
An epileptic seizure is a brief episode of symptoms and signs caused by excessive electrical activity in the brain. One of the major chronic neurological diseases, epilepsy, affects millions of individuals worldwide. Effective detection of seizure events is critical in the diagnosis and treatment of patients with epilepsy. Neurologists monitor the electrical activity in the brains of patients to identify epileptic seizures by employing advanced sensing techniques, including electroencephalograms and electromyography. Machine learning-based classification of the EEG signal can help differentiate between normal signals and the patterns associated with epileptic seizures. This work presents a novel approach for the classification of epileptic seizures using random neural network (RNN). The proposed model has been trained and tested using two publicly available datasets: CHB-MIT and BONN, provided by Children’s Hospital Boston-Massachusetts Institute of Technology and the University of Bonn, respectively. The results obtained from multiple experiments highlight that the proposed scheme outperformed traditional classification schemes such as artificial neural network and support vector machine. The proposed RNN-based model achieved accuracies of 93.27% and 99.84% on the CHB-MIT and BONN datasets, respectively. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes)
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27 pages, 1971 KiB  
Article
Leveraging Real-World Data from IoT Devices in a Fog–Cloud Architecture for Resource Optimisation within a Smart Building
by Kelvin N. Lawal, Titus K. Olaniyi and Ryan M. Gibson
Appl. Sci. 2024, 14(1), 316; https://doi.org/10.3390/app14010316 - 29 Dec 2023
Viewed by 903
Abstract
It is estimated that over 125 billion heterogeneous and homogeneous Internet of Things (IoT) devices will be internet-connected by 2030. This significant increase will generate large data volumes, posing a global problem for Cloud–Fog computing infrastructures. The current literature uses synthetic data in [...] Read more.
It is estimated that over 125 billion heterogeneous and homogeneous Internet of Things (IoT) devices will be internet-connected by 2030. This significant increase will generate large data volumes, posing a global problem for Cloud–Fog computing infrastructures. The current literature uses synthetic data in the iFogSim2 simulation toolkit; however, this study bridges the gap using real-world data to reflect and address the real-world issue. Smart IoT device data are captured, compared, and evaluated in a fixed and scalable scenario at both the Cloud and Fog layers, demonstrating the improved benefits achievable in energy consumption, latency, and network bandwidth usage within a smart office building. Real-world IoT device data evaluation results demonstrate that Fog computing is more efficient than Cloud computing, with increased scalability and data volume in a fixed- and low-bandwidth smart building architecture. This indicates a direct correlation between the increase in devices and the increase in efficiency within a scalable scenario, while the fixed architecture overall shows the inverse due to the low device numbers used in this study. The results indicate improved energy savings and significant improvements of up to 84.41% and 38.95% in network latency and usage, respectively, within a fixed architecture, while scalability analysis demonstrates improvements up to 4%, 91.38% and 34.78% for energy, latency, and network usage, respectively. Fog computing improvements are limited within a fixed smart building architecture with relatively few IoT devices. However, the benefits of Fog computing are significant in a scalable scenario with many IoT devices. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes)
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20 pages, 8047 KiB  
Article
Data-Quality Assessment for Digital Twins Targeting Multi-Component Degradation in Industrial Internet of Things (IIoT)-Enabled Smart Infrastructure Systems
by Atuahene Kwasi Barimah, Octavian Niculita, Don McGlinchey and Andrew Cowell
Appl. Sci. 2023, 13(24), 13076; https://doi.org/10.3390/app132413076 - 07 Dec 2023
Viewed by 729
Abstract
In the development of analytics for PHM applications, a lot of emphasis has been placed on data transformation for optimal model development without enough consideration for the repeatability of the measurement systems producing the data. This paper explores the relationship between data quality, [...] Read more.
In the development of analytics for PHM applications, a lot of emphasis has been placed on data transformation for optimal model development without enough consideration for the repeatability of the measurement systems producing the data. This paper explores the relationship between data quality, defined as the measurement system analysis (MSA) process, and the performance of fault detection and isolation (FDI) algorithms within smart infrastructure systems. This research employs a comprehensive methodology, starting with an MSA process for data-quality evaluation and leading to the development and evaluation of fault detection and isolation (FDI) algorithms. During the MSA phase, the repeatability of a water distribution system’s measurement system is examined to characterise variations within the system. A data-quality process is defined to gauge data quality. Synthetic data are introduced with varying data-quality levels to investigate their impact on FDI algorithm development. Key findings reveal the complex relationship between data quality and FDI algorithm performance. Synthetic data, even with lower quality, can improve the performance of statistical process control (SPC) models, whereas data-driven approaches benefit from high-quality datasets. The study underscores the importance of customising FDI algorithms based on data quality. A framework for instantiating the MSA process for IIoT applications is also suggested. By bridging data-quality assessment with data-driven FDI, this research contributes to the design of digital twins for IIoT-enabled smart infrastructure systems. Further research on the practical implementation of the MSA process for edge analytics for PHM applications will be considered as part of our future research. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes)
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15 pages, 1362 KiB  
Article
Enhancing Energy Efficiency in Retail within Smart Cities through Demand-Side Management Models
by Ching-Bang Yao and Chang-Yi Kao
Appl. Sci. 2023, 13(24), 13040; https://doi.org/10.3390/app132413040 - 06 Dec 2023
Viewed by 671
Abstract
The energy discourse is multifaceted, encompassing energy creation, storage, and conservation. Beyond the imperative of conserving energy consumption, effective energy management is a critical aspect of achieving overall energy efficiency. Despite being traditionally regarded as low electricity consumers, retailers play a pivotal role [...] Read more.
The energy discourse is multifaceted, encompassing energy creation, storage, and conservation. Beyond the imperative of conserving energy consumption, effective energy management is a critical aspect of achieving overall energy efficiency. Despite being traditionally regarded as low electricity consumers, retailers play a pivotal role in economic activity. While categorized as non-productive energy users, the retail industry operates numerous establishments, facing substantial energy costs that make energy management integral to its operations. Historically, smaller retail stores have lacked awareness of energy saving. However, by connecting these stores, even modest reductions in individual electricity consumption can yield significant overall energy savings. This study aims to investigate the feasibility of implementing the demand-side management (DSM) aggregator model in the retail industry. Through surveys on awareness of energy saving and the application of deep learning techniques to analyze the effectiveness of the Aggregator model, the results reveal that the mean squared prediction error (MSPE) of this research is below 2.05%. This indicates substantial accuracy and offers meaningful reference value for Energy Service Company (ESCO) providers. The findings contribute practical recommendations for the sustainable and competitive implementation of DSM energy management practices in smart cities. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes)
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18 pages, 1539 KiB  
Article
Blocking Linear Cryptanalysis Attacks Found on Cryptographic Algorithms Used on Internet of Thing Based on the Novel Approaches of Using Galois Field (GF (232)) and High Irreducible Polynomials
by Khumbelo Difference Muthavhine and Mbuyu Sumbwanyambe
Appl. Sci. 2023, 13(23), 12834; https://doi.org/10.3390/app132312834 - 29 Nov 2023
Viewed by 656
Abstract
Attacks on the Internet of Things (IoT) are not highly considered during the design and implementation. The prioritization is making profits and supplying services to clients. Most cryptographic algorithms that are commonly used on the IoT are vulnerable to attacks such as linear, [...] Read more.
Attacks on the Internet of Things (IoT) are not highly considered during the design and implementation. The prioritization is making profits and supplying services to clients. Most cryptographic algorithms that are commonly used on the IoT are vulnerable to attacks such as linear, differential, differential–linear cryptanalysis attacks, and many more. In this study, we focus only on linear cryptanalysis attacks. Little has been achieved (by other researchers) to prevent or block linear cryptanalysis attacks on cryptographic algorithms used on the IoT. In this study, we managed to block the linear cryptanalysis attack using a mathematically novel approach called Galois Field of the order (232), denoted by GF (232), and high irreducible polynomials were used to re-construct weak substitution boxes (S-Box) of mostly cryptographic algorithms used on IoT. It is a novel approach because no one has ever used GF (232) and highly irreducible polynomials to block linear cryptanalysis attacks on the most commonly used cryptographic algorithms. The most commonly used cryptographic algorithms on the IoT are Advanced Encryption Standard (AES), BLOWFISH, CAMELLIA, CAST, CLEFIA, Data Encryption Standard (DES), Modular Multiplication-based Block (MMB), RC5, SERPENT, and SKIPJACK. We assume that the reader of this paper has basic knowledge of the above algorithms. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes)
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24 pages, 6283 KiB  
Article
E-MQTT: End-to-End Synchronous and Asynchronous Communication Mechanisms in MQTT Protocol
by Yerin Im and Mingyu Lim
Appl. Sci. 2023, 13(22), 12419; https://doi.org/10.3390/app132212419 - 16 Nov 2023
Viewed by 860
Abstract
Message Queuing Telemetry Transport (MQTT) enables asynchronous confirmation of message reception by brokers but lacks a way for publishers to know when subscribers receive their messages without adding additional communication overhead. This paper addresses this problem by improving MQTT to establish end-to-end communication [...] Read more.
Message Queuing Telemetry Transport (MQTT) enables asynchronous confirmation of message reception by brokers but lacks a way for publishers to know when subscribers receive their messages without adding additional communication overhead. This paper addresses this problem by improving MQTT to establish end-to-end communication between a publisher and subscribers, reducing message exchanges, using what is called End-to-End MQTT (E-MQTT). In E-MQTT, a publisher sets the number of responses that it will wait for when it sends a message. After the broker collects the response messages from subscribers, it sends one aggregated response back to the publisher. The publisher also can receive the response message synchronously or asynchronously. Experimental results consistently show that E-MQTT outperforms traditional MQTT in terms of delay, especially when the publisher needs to monitor when its query message is received by subscribers. Although E-MQTT packets are slightly larger due to additional fields, the difference in packet size compared to MQTT is not significant. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes)
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20 pages, 3106 KiB  
Article
Vulnerability Exploitation Risk Assessment Based on Offensive Security Approach
by Seong-Su Yoon, Do-Yeon Kim, Ka-Kyung Kim and Ieck-Chae Euom
Appl. Sci. 2023, 13(22), 12180; https://doi.org/10.3390/app132212180 - 09 Nov 2023
Cited by 1 | Viewed by 1149
Abstract
Security incidents targeting control systems and the industrial internet of things (IIoT) are on the rise as attackers gain a better understanding of the nature of these systems and their increasing connectivity to information technology (IT). Every year, the number of vulnerabilities associated [...] Read more.
Security incidents targeting control systems and the industrial internet of things (IIoT) are on the rise as attackers gain a better understanding of the nature of these systems and their increasing connectivity to information technology (IT). Every year, the number of vulnerabilities associated with these incidents increases, making it impractical to apply timely patches for all of them. The current vulnerability assessments, which are the basis for vulnerability patching, have limitations in that they do not adequately reflect the risk of exploitation in the real world after discovery and do not consider operational technology (OT) and industrial control system (ICS) environments other than IT environments. This study proposes to evaluate exploit risk in real-world environments by considering OT/ICS environments and calculating three metrics, including exploit chain risk, exploit code availability, and exploit use probability based on cyber threat information, including IIoT vulnerability data, used in OT/ICS environments. In addition, we construct exploitation scenarios in a control system environment to prioritize vulnerabilities with a high risk of exploitation based on the three metrics. We show that by assessing the risk of attackers’ intentions and exploited technologies for attacks against IIoT devices in a control system environment, we can provide defenders with comprehensive attack risk information for proactive defense. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes)
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21 pages, 6303 KiB  
Article
Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning
by Roopdeep Kaur, Gour Karmakar and Muhammad Imran
Appl. Sci. 2023, 13(20), 11560; https://doi.org/10.3390/app132011560 - 22 Oct 2023
Cited by 2 | Viewed by 1274
Abstract
In digital image processing, filtering noise is an important step for reconstructing a high-quality image for further processing such as object segmentation, object detection, and object recognition. Various image-denoising approaches, including median, Gaussian, and bilateral filters, are available in the literature. Since convolutional [...] Read more.
In digital image processing, filtering noise is an important step for reconstructing a high-quality image for further processing such as object segmentation, object detection, and object recognition. Various image-denoising approaches, including median, Gaussian, and bilateral filters, are available in the literature. Since convolutional neural networks (CNN) are able to directly learn complex patterns and features from data, they have become a popular choice for image-denoising tasks. As a result of their ability to learn and adapt to various denoising scenarios, CNNs are powerful tools for image denoising. Some deep learning techniques such as CNN incorporate denoising strategies directly into the CNN model layers. A primary limitation of these methods is their necessity to resize images to a consistent size. This resizing can result in a loss of vital image details, which might compromise CNN’s effectiveness. Because of this issue, we utilize a traditional denoising method as a preliminary step for noise reduction before applying CNN. To our knowledge, a comparative performance study of CNN using traditional and embedded denoising against a baseline approach (without denoising) is yet to be performed. To analyze the impact of denoising on the CNN performance, in this paper, firstly, we filter the noise from the images using traditional means of denoising method before their use in the CNN model. Secondly, we embed a denoising layer in the CNN model. To validate the performance of image denoising, we performed extensive experiments for both traffic sign and object recognition datasets. To decide whether denoising will be adopted and to decide on the type of filter to be used, we also present an approach exploiting the peak-signal-to-noise-ratio (PSNRs) distribution of images. Both CNN accuracy and PSNRs distribution are used to evaluate the effectiveness of the denoising approaches. As expected, the results vary with the type of filter, impact, and dataset used in both traditional and embedded denoising approaches. However, traditional denoising shows better accuracy, while embedded denoising shows lower computational time for most of the cases. Overall, this comparative study gives insights into whether denoising will be adopted in various CNN-based image analyses, including autonomous driving, animal detection, and facial recognition. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes)
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20 pages, 1625 KiB  
Article
IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities
by William Villegas-Ch, Jaime Govea and Angel Jaramillo-Alcazar
Appl. Sci. 2023, 13(19), 10977; https://doi.org/10.3390/app131910977 - 05 Oct 2023
Viewed by 1777
Abstract
This study addresses anomaly detection in smart city environments driven by the Internet of Things. In these cities, digital interconnection and the extensive network of sensors generate enormous amounts of data, which are essential to improving citizens’ efficiency and quality of life. However, [...] Read more.
This study addresses anomaly detection in smart city environments driven by the Internet of Things. In these cities, digital interconnection and the extensive network of sensors generate enormous amounts of data, which are essential to improving citizens’ efficiency and quality of life. However, this data may also contain strange events that require early detection to ensure the proper functioning of urban systems. For this, anomaly detection models are explored to identify unusual patterns in urban data. The work focuses on the applicability and effectiveness of these models in different urban scenarios supported by the Internet of Things. Furthermore, its performance is evaluated by comparing it with existing approaches, and its advantages and limitations are analyzed. The results show that the proposed models, including Isolation Forest, recurrent neural network, and variational autoencoder, are highly effective in detecting anomalies in urban data. This work contributes to the field of smart cities by improving the safety and efficiency of urban systems. Early detection of anomalies makes it possible to prevent unplanned interruptions, ensure the safety of citizens, and maintain the integrity of urban systems. Furthermore, the relevance of this work in the existing literature and its importance for the evolution of smart cities supported by the Internet of Things are highlighted. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes)
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29 pages, 5023 KiB  
Article
Internet of Medical Things Healthcare for Sustainable Smart Cities: Current Status and Future Prospects
by Priyanka Mishra and Ghanshyam Singh
Appl. Sci. 2023, 13(15), 8869; https://doi.org/10.3390/app13158869 - 01 Aug 2023
Cited by 3 | Viewed by 2891
Abstract
The concept of smart and connected healthcare has emerged in response to the growing demand for the improvement of healthcare systems and the increasing prevalence of chronic diseases. Looking towards the future, smart healthcare holds great potential to transform the healthcare industry by [...] Read more.
The concept of smart and connected healthcare has emerged in response to the growing demand for the improvement of healthcare systems and the increasing prevalence of chronic diseases. Looking towards the future, smart healthcare holds great potential to transform the healthcare industry by providing more efficient, personalized, and accessible healthcare services. This paper delves into the concept of intelligent, interconnected, and customized healthcare systems within the Internet of Medical Things (IoMT) framework. It explores the utilization of cutting-edge technologies, including the IoMT, in conjunction with big data, cloud computing, artificial intelligence, and blockchain to provide healthcare services that are not only more efficient but also more convenient and personalized. It draws on existing literature, bibliometric data, and global marketing analysis to gain a deeper understanding of these technologies and their impact on the healthcare system. We have explored several upcoming features of the Healthcare 5.0 paradigm, which represents the next evolution in healthcare systems focusing on a more personalized and patient-centric approach. We introduce a healthcare architecture specifically designed for the IoMT that prioritizes the security considerations associated with devices. Finally, we have focused on addressing open research challenges, particularly those related to fundamental social needs, such as ensuring equitable access to smart and connected healthcare systems. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes)
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16 pages, 637 KiB  
Article
Parallel Implementations of ARIA on ARM Processors and Graphics Processing Unit
by Siwoo Eum, Hyunjun Kim, Hyeokdong Kwon, Minjoo Sim, Gyeongju Song and Hwajeong Seo
Appl. Sci. 2022, 12(23), 12246; https://doi.org/10.3390/app122312246 - 30 Nov 2022
Cited by 2 | Viewed by 1333
Abstract
The ARIA block cipher algorithm is Korean standard, IETF standard (RFC 5794), and part of the TLS/SSL protocol. In this paper, we present the parallel implementation of ARIA block cipher on ARMv8 processors and GPU. The ARMv8 processor is the latest 64-bit ARM [...] Read more.
The ARIA block cipher algorithm is Korean standard, IETF standard (RFC 5794), and part of the TLS/SSL protocol. In this paper, we present the parallel implementation of ARIA block cipher on ARMv8 processors and GPU. The ARMv8 processor is the latest 64-bit ARM architecture and supports ASIMD for parallel implementations. With this feature, 4 and 16 parallel encryption blocks are implemented to optimize the substitution layer of ARIA block cipher using four different Sboxes. Compared to previous works, the performance was improved by 2.76× and 8.73× at 4-plaintext and 16-plaintext cases, respectively. We also present optimal implementation on GPU architectures. GPUs are highly parallel programmable processors featuring maximum arithmetic and memory bandwidth. Optimal settings of ARIA block cipher implementation on GPU were analyzed using the Nsight Compute profiler provided by Nvidia. We found that using shared memory reduces the execution timing when performing substitution operations with Sbox tables. When using many threads with shared memory instead of global memory, it improves performance by about 1.08∼1.43×. Additionally, techniques using table expansion to minimize bank conflicts have been found to be inefficient when tables cannot be copied by the size of the bank. We measured the performance of ARIA block ciphers implemented with various settings. This represents an optimized GPU implementation of the ARIA block cipher. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes)
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16 pages, 527 KiB  
Article
Achievable Rate of NOMA-Based Cooperative Spectrum-Sharing CRN over Nakagami-m Channels
by Sajid Hussain Alvi, Bakhtiar Ali, Jawad Mirza, Ali Khaqan, Muhammad Awais Javed, Jehad Ali and Niamat Hussain
Appl. Sci. 2022, 12(23), 12010; https://doi.org/10.3390/app122312010 - 24 Nov 2022
Viewed by 989
Abstract
Efficient data dissemination is a key challenge for IoT applications. In this paper, we present an ergodic achievable rate analysis of the non-orthogonal multiple access (NOMA)-based cooperative transmission scheme in spectrum-sharing cognitive radio networks (CRNs). The transmission scheme consists of two phases, where [...] Read more.
Efficient data dissemination is a key challenge for IoT applications. In this paper, we present an ergodic achievable rate analysis of the non-orthogonal multiple access (NOMA)-based cooperative transmission scheme in spectrum-sharing cognitive radio networks (CRNs). The transmission scheme consists of two phases, where the NOMA transmission strategy is employed at the secondary transmitter (ST) in the second phase to serve the primary receiver (PR) and secondary receivers (SRs). This cooperative spectrum-sharing transmission is favourable when the ST-PR link is strong and the ST faces a spectrum scarcity issue. To evaluate the performance of the network over Nakagami-m channels, ergodic achievable rates at PR and SRs are derived in closed forms, which can be used for any integer or non-integer value of fading index m. Finally, we compare the results of the analytical expressions with simulated results for validation. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes)
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25 pages, 1452 KiB  
Article
Self-Healing of Semantically Interoperable Smart and Prescriptive Edge Devices in IoT
by Asimina Dimara, Vasileios-Georgios Vasilopoulos, Alexios Papaioannou, Sotirios Angelis, Konstantinos Kotis, Christos-Nikolaos Anagnostopoulos, Stelios Krinidis, Dimosthenis Ioannidis and Dimitrios Tzovaras
Appl. Sci. 2022, 12(22), 11650; https://doi.org/10.3390/app122211650 - 16 Nov 2022
Cited by 5 | Viewed by 1684
Abstract
Smart homes enhance energy efficiency without compromising residents’ comfort. To support smart home deployment and services, an IoT network must be established, while energy-management techniques must be applied to ensure energy efficiency. IoT networks must perpetually operate to ensure constant energy and indoor [...] Read more.
Smart homes enhance energy efficiency without compromising residents’ comfort. To support smart home deployment and services, an IoT network must be established, while energy-management techniques must be applied to ensure energy efficiency. IoT networks must perpetually operate to ensure constant energy and indoor environmental monitoring. In this paper, an advanced sensor-agnostic plug-n-play prescriptive edge-to-edge IoT network management with micro-services is proposed, supporting also the semantic interoperability of multiple smart edge devices operating in the smart home network. Furthermore, IoT health-monitoring algorithms are applied to inspect network anomalies taking proper healing actions/prescriptions without the need to visit the residency. An autoencoder long short-term memory (AE-LSTM) is selected for detecting problematic situations, improving error prediction to 99.4%. Finally, indicative evaluation results reveal the mitigation of the IoT system breakdowns. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes)
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23 pages, 6334 KiB  
Article
Smart Wireless CO2 Sensor Node for IoT Based Strategic Monitoring Tool of The Risk of The Indoor SARS-CoV-2 Airborne Transmission
by C. Bambang Dwi Kuncoro, Aurelia Amaris and Arvanida Feizal Permana
Appl. Sci. 2022, 12(21), 10784; https://doi.org/10.3390/app122110784 - 25 Oct 2022
Cited by 1 | Viewed by 1760
Abstract
A close correlation between CO2 concentration and aerosol enables the wide utilization of CO2 concentration as a good representation of Severe Acute Respiratory Syndrome-Coronavirus-2 infection airborne transmission. On the other side, many indoor air-quality monitoring devices have been developed for indoor [...] Read more.
A close correlation between CO2 concentration and aerosol enables the wide utilization of CO2 concentration as a good representation of Severe Acute Respiratory Syndrome-Coronavirus-2 infection airborne transmission. On the other side, many indoor air-quality monitoring devices have been developed for indoor monitoring applications. However, most of them are multiparameter air-quality sensor systems and tend to consume relatively high power, are relatively large devices, and are fairly expensive; therefore, they not meet the requirement for indoor monitoring applications. This paper presents a smart wireless sensor node that can measure and monitor CO2 concentration levels. The node was designed to meet the requirements of indoor air-quality monitoring applications by considering several factors, such as compact size, low cost, and low power, as well as providing real-time, continuous, reliable, and remote measurement. Furthermore, the commercial off-the-shelf and low-power consumption components are chosen to fit with the low-cost development and reduce energy consumption. Moreover, a low-power algorithm and cloud-based data logger also were applied to minimize the total power consumption. This power strategy was applied as a preliminary development toward an autonomous sensor node. The node has a compact size and consumes low energy for one cycle of CO2 measurement, accompanied by high accuracy with very low measurement error. The experiment result revealed the node could measure and monitor in real-time continuous, reliable, and remote CO2 concentration levels in indoor and outdoor environments. A user interface visualizes CO2 concentration graphically and numerically using the Adafruit platform for easy accessibility over the Internet of Things. The developed node is very promising and suitable for indoor CO2 monitoring applications with the acquired data that could be utilized as an indicator to minimize the risk of indoor Severe Acute Respiratory Syndrome-Coronavirus-2 airborne transmission. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes)
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15 pages, 4613 KiB  
Article
Proof of Concept of Reconfigurable Solvent Vapor Sensor Tag with Wireless Power Transfer for IoT Applications
by Houda Ayadi, Jan Machac, Milan Svanda, Noureddine Boulejfen and Lassaad Latrach
Appl. Sci. 2022, 12(20), 10266; https://doi.org/10.3390/app122010266 - 12 Oct 2022
Cited by 4 | Viewed by 1518
Abstract
In this paper, a concept of a reconfigurable chipless radio frequency identification (RFID) sensor tag for detecting solvent vapors/gas in IoT applications was presented. The concept was based on the authors’ previously published rectangular loop structure equipped with a U-folded dipole loaded with [...] Read more.
In this paper, a concept of a reconfigurable chipless radio frequency identification (RFID) sensor tag for detecting solvent vapors/gas in IoT applications was presented. The concept was based on the authors’ previously published rectangular loop structure equipped with a U-folded dipole loaded with a glide-symmetrical interdigital capacitor coated with a thin layer of tetrasulfonated copper phthalocyanine deposited as a sensing layer to improve the sensing capability in the presence of acetone vapor. In order to further maximize the sensitivity of the designed structure to the desired solvent, a circuit for a central frequency adjustment using a radio frequency varactor diode biased with a wireless power transfer (WPT) was designed. By varying the DC bias of the diode, a continuous tunable range of approximately 200 MHz was achieved. The proposed reconfigurable wireless sensor tag was manufactured and the frequency shift was verified by measurement. The proposed external frequency control can be applied to a wide class of electrical resonators. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes)
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15 pages, 894 KiB  
Article
Secure and Robust Internet of Things with High-Speed Implementation of PRESENT and GIFT Block Ciphers on GPU
by Hyunjun Kim , Siwoo Eum , Wai-Kong Lee , Sokjoon Lee  and Hwajeong Seo
Appl. Sci. 2022, 12(20), 10192; https://doi.org/10.3390/app122010192 - 11 Oct 2022
Cited by 1 | Viewed by 1181
Abstract
With the advent of the Internet of Things (IoT) and cloud computing technologies, vast amounts of data are being created and communicated in IoT networks. Block ciphers are being used to protect these data from malicious attacks. Massive computation overheads introduced by bulk [...] Read more.
With the advent of the Internet of Things (IoT) and cloud computing technologies, vast amounts of data are being created and communicated in IoT networks. Block ciphers are being used to protect these data from malicious attacks. Massive computation overheads introduced by bulk encryption using block ciphers can become a performance bottleneck of the server, requiring high throughput. As the need for high-speed encryption required for such communications has emerged, research is underway to utilize a graphics processor for encryption processing based on the high processing power of the GPU. Applying bit-slicing of lightweight ciphers was not covered in the previous implementation of lightweight ciphers on GPU architecture. In this paper, we implemented PRESENT and GIFT lightweight block ciphers GPU architectures. It minimizes the computation overhead caused by optimizing the algorithm by applying the bit-slicing technique. We performed practical analysis by testing practical use cases. We tested PRESENT-80, PRESENT-128, GIFT-64, and GIFT-128 block ciphers in RTX3060 platforms. The throughput of the exhaustive search are 553.932 Gbps, 529.952 Gbps, 583.859 Gbps, and 214.284 Gbps for PRESENT-80, PRESENT-128, GIFT-64, and GIFT-128, respectively. For the case of data encryption, it achieved 24.264 Gbps, 24.522 Gbps, 85.283 Gbps, and 10.723 Gbps for PRESENT-80, PRESENT-128, GIFT-64, and GIFT-128, respectively. Specifically, the proposed implementation of a PRESENT block cipher is approximately 4× higher performance than the latest work that implements PRESENT block cipher. Lastly, the proposed implementation of a GIFT block cipher on GPU is the first implementation for the server environment. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes)
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24 pages, 1244 KiB  
Article
Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
by Bhupesh Kumar Mishra, Keshav Dahal and Zeeshan Pervez
Appl. Sci. 2022, 12(16), 8358; https://doi.org/10.3390/app12168358 - 21 Aug 2022
Cited by 6 | Viewed by 1781
Abstract
In smart cities, relief items distribution is a complex task due to the factors such as incomplete information, unpredictable exact demand, lack of resources, and causality levels, to name a few. With the development of Internet of Things (IoT) technologies, dynamic data update [...] Read more.
In smart cities, relief items distribution is a complex task due to the factors such as incomplete information, unpredictable exact demand, lack of resources, and causality levels, to name a few. With the development of Internet of Things (IoT) technologies, dynamic data update provides the scope of distribution schedule to adopt changes with updates. Therefore, the dynamic relief items distribution schedule becomes a need to generate humanitarian supply chain schedules as a smart city application. To address the disaster data updates in different time periods, a dynamic optimised model with a sliding time window is proposed that defines the distribution schedule of relief items from multiple supply points to different disaster regions. The proposed model not only considers the details of available resources dynamically but also introduces disaster region priority along with transportation routes information updates for each scheduling time slot. Such an integrated optimised model delivers an effective distribution schedule to start with and updates it for each time slot. A set of numerical case studies is formulated to evaluate the performance of the optimised scheduling. The dynamic updates on the relief item demands’ travel path, causality level and available resources parameters have been included as performance measures for optimising the distributing schedule. The models have been evaluated based on performance measures to reflect disaster scenarios. Evaluation of the proposed models in comparison to the other perspective static and dynamic relief items distribution models shows that adopting dynamic updates in the distribution model cover most of the major aspects of the relief items distribution task in a more realistic way for post-disaster relief management. The analysis has also shown that the proposed model has the adaptability to address the changing demand and resources availability along with disaster conditions. In addition, this model will also help the decision-makers to plan the post-disaster relief operations in more effective ways by covering the updates on disaster data in each time period. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes)
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Review

Jump to: Research

26 pages, 936 KiB  
Review
A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
by Nadir Abbas, Zeshan Abbas, Xiaodong Liu, Saad Saleem Khan, Eric Deale Foster and Stephen Larkin
Appl. Sci. 2023, 13(17), 9881; https://doi.org/10.3390/app13179881 - 31 Aug 2023
Cited by 12 | Viewed by 1973
Abstract
This article presents a survey of unmanned aerial vehicle (UAV) applications in smart cities, emphasizing integration challenges. Smart cities leverage innovative technologies, including the Internet of Things (IoT) and UAVs, to enhance residents’ quality of life. The study highlights UAV applications, challenges, limitations, [...] Read more.
This article presents a survey of unmanned aerial vehicle (UAV) applications in smart cities, emphasizing integration challenges. Smart cities leverage innovative technologies, including the Internet of Things (IoT) and UAVs, to enhance residents’ quality of life. The study highlights UAV applications, challenges, limitations, and future perspectives of smart city development. Advanced control methods for maximizing UAV benefits are discussed. Control theory challenges and issues for the deployment of UAVs are addressed. By concentrating on challenges, potential applications, and advanced control techniques, this paper offers insights into UAVs’ role in shaping the future of smart cities. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes)
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15 pages, 8041 KiB  
Review
Constraints of Using Conductive Screen-Printing for Chipless RFID Tags with Enhanced RCS Response
by Milan Svanda, Jan Machac and Milan Polivka
Appl. Sci. 2023, 13(1), 148; https://doi.org/10.3390/app13010148 - 22 Dec 2022
Cited by 1 | Viewed by 1104
Abstract
The analysis and experimental verification of the properties of four types of chipless RFID tags with an increased RCS response level designed and fabricated by conductive screen-printing using silver paste on foil and paper substrates was performed. The analytical formula for the quality [...] Read more.
The analysis and experimental verification of the properties of four types of chipless RFID tags with an increased RCS response level designed and fabricated by conductive screen-printing using silver paste on foil and paper substrates was performed. The analytical formula for the quality factor of microstrip structures with a reduced conductivity of the metal layers was used to predict the changes and detectability of the backscattered RCS response. The analysis provides insight into the limitations and outlines the possibilities of chipless structures screen-printed on foil and paper substrates, which can be of significant benefit to further reducing the cost, and to speed up the production of these tags for identification and sensing purposes. Full article
(This article belongs to the Special Issue IoT in Smart Cities and Homes)
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